• Journal of Korean Society of Forest Science
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• 제98권4호
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• pp.409-416
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• 2009

Forest biomass estimation is essential for greenhouse gas inventories and terrestrial carbon accounting. Remote sensing allows for estimating forest biomass over a large area. This study was conducted to estimate forest biomass and to produce a forest biomass map for Muju county using forest biomass conversion table developed by field plot data from the 5th National Forest Inventory and Landsat TM-5. Correlation analysis was carried out to select suitable independent variables for developing regression models. It was resulted that the height class, crown closure density, and age class were highly correlated with forest biomass. Six regression models were used with the combination of these three stand variables and verified by validation statistics such as root mean square error (RMSE) and mean bias. It was found that a regression model with crown closure density and height class (Model V) was better than others for estimating forest biomass. A biomass conversion table by model V was produced and then used for estimating forest biomass in the study site. The total forest biomass of the Muju county was estimated about 8.8 million ton, or 128.3 ton/ha by the conversion table.

• Journal of Korean Society of Forest Science
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• 제60권1호
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• pp.10-16
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• 1983

This survey was carried out in Schneegattern Forest District which is located 40 km northeast of Salzburg, Austria. The purpose of interpretation with two sampling methods, stratified sampling and unstratified sampling, on B & W infrared photos, with a scale of 1:10,000 was to know coniferous stand volumn and to reduce the cost, Forest stands were classified into 4 groups; those were non-forest, young stands, beech, coniferous stands. Coniferous and beech stands were devided into age classes I (41-80 years), II (above 81 years). After this delineation sample points were designated on the orthophoto map whose data were transferred from the aerial photos. The volumn data were calculated from DBH using relascope in the field and the results were as follows. 1) Coniferous stand volumn per hactare was ($470{\pm}31.9m^3$ 2) The diameter distribution of $C_1$ was binomial, but $C_2$ showed normal distribution. 3) The stratified sampling method was better than unstratified sampling method.

• Journal of Korean Society for Geospatial Information Science
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• 제22권3호
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• pp.145-154
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• 2014

The road network becomes more complex and extensive. Therefore, the inconveniences are caused in accordance with the time delay of the restoration of damaged roads, demands for excessive costs on information collection, and limitations on acquisition of damage information of the roads. Recently, road centric spatial information is gathered using mobile multi sensor system for road inventory. But expensive MMS(Mobile Mapping System) equipments require high maintenance costs from beginning and takes a lot of time in the data processing. So research is needed for continuous maintenance by collecting and displaying the damaged information on a digital map using low cost mobile camera system. In this research we aim to develop the techniques for mosaic with a regular ground sample distance using successive image from oblique camera on a vehicle. For doing this, mosaic image is generated by estimating the homography of high resolution oblique image, and the ground sample distance and appropriate overlap are analyzed using high resolution aerial oblique images which contain resolution target. Based on this we have proposed the appropriate overlap and exposure interval for mobile road inventory system.

• Journal of Wetlands Research
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• 제20권1호
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• pp.20-26
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• 2018

The purpose of this study is to construct a GIS / DB by grasping a small but ecologically valuable village wetland distribution, and to propose conservation management and wise use plan. The study area is Seocheon-gun, a typical farming village. Firstly, based on the digital topographical map (1:5,000), the Arc-GIS tool was used to identify the provisional(draft) village wetlands. In addition, for the management of village wetlands, wetlands with an area of more or less than $625m^2$ each were derived and according to ecological regions study area was classified into urban areas, inland areas and coastal areas. And finally, according to the wetland identifying indicators, the village wetlands were identified as the final village wetlands through indoor and field trips. The results of the study show that there are 570 village wetlands in Seocheon - gun province, which are 74 in urban areas, 220 in inland areas, and 276 in coastal areas. The case study for village wetland identification was conducted in one out of two urban areas (Seocheon - eup), two of four coastal areas (Biin - myeon and Seo - myeon), and three of seven inland areas (Masan - myeon, Hansan - myeon, and Sicho - myeon). The distribution of village wetlands was found mainly to be a village wetland with an area of less than $625m^2$. In addition, compared with inland areas, the discrimination rate of village wetlands in coastal areas and urban areas was relatively low, indicating that inland areas were still less disturbed, and land use in urban areas and coastal areas is changing rapidly. Especially, land with less awareness such as village wetlands is relatively easily damaged, and management strategy is urgent.

• Journal of Korean Society of Forest Science
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• 제103권3호
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• pp.446-452
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• 2014

This research aimed to provide a method to estimate growing stock and carbon stock using the characteristics of forest type map such as the age-class, DBH class and crown density class. We transformed the growing stock data of national forest inventory (mainly Kangwon-do province) onto those of time when the forest type map was established. We developed a simulation model for the growing stock using the transformed data and the characteristics of forest type map by the quantification method I. By comparing partial correlation coefficient, we found that quantification of growing stock was largely affected by age-class followed by crown density class, forest type and DBH class. The growing stock, was estimated as minimum in the broadleaved forest with age-class II, DBH class 'Small', and crown density class 'Low' as $20.0m^3/ha$, whereas showed maximum value in the coniferous forest with age-class VI, DBH class 'Large', and crown density class 'High' as $305.0m^3/ha$. The growing stock for coniferous, broadleaved, and mixed forest were estimated as $30.5{\sim}305.0m^3/ha$, $20.0{\sim}200.4m^3/ha$, and $23.8{\sim}238.1m^3/ha$, respectively. When we compared the carbon stock by forest type, the carbon stock by age class based on growing stock was maximum when DBH class was 'Large' and crown density class was 'High' regardless of forest type. This estimation of growing stock by using characteristic of forest type can be used to estimate the changes in growing stock and carbon stock resulting from deforestation or natural disaster. In addition, we hope it provide a useful advice when forest officials and policy makers have to make decisions in regard to forest management.

• Korean Journal of Remote Sensing
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• 제36권2_2호
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• pp.325-335
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• 2020

Forest canopy height can be used for estimate of above-ground forest biomass (AGB) by means of the allometric equation. The remote locations and harsh conditions of mangrove forests limit the number of field inventory data stations needed for large-scale modeling of carbon and biomass dynamics. Although active and passive spaceborne sensors have proven successful in mapping mangroves globally, the sensors generally have coarse spatial resolution and overlook small-scale features. Here we generate a 12 m spatial resolution mangrove canopy height map from TanDEM-X data acquired over the world largest intact mangrove forest located in the Sundarbans. With single-pol. TanDEM-X data from 2011 to 2013, the proposed technique makes use of the fact that the double-bounce scattering that occurs between the water and mangrove trees yields water surface level elevation over mangrove forest areas, thus allowing us to estimate forest height with the assumption of an underlying flat topography. Our observations have led to a large-scale mangrove canopy height map over the entire Sundarbans region at a 12 m spatial resolution. Our canopy height estimates were validated with ground measurements acquired in 2015, a correlation coefficient of 0.83 and a RMSE of 0.84 m. With globally available TanDEM-X data, the technique described here will potentially provide accurate global maps of mangrove canopy height at 12 m spatial resolution and provide crucial information for understanding biomass and carbon dynamics in the mangrove ecosystems.

• Journal of Korean Society of Forest Science
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• 제96권3호
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• pp.259-268
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• 2007

This study was conducted to produce forest thematic maps and estimate forest statistics for Pyeongchang Gun using the kNN technique, which has been applied to produce thematic maps of variables of interest including unobserved plots by combining field plot data, remotely sensed data and other digital map data in forest inventories. The estimation errors for three horizontal reference areas (HRAs), whose radii are 20, 40 and 60 km respectively, were compared. Although the precision for the 40 km radius was lower compared to that for the 60 km radius, the 40 km radius was found to be an efficient HRA because their difference in precision was modest. At a value of k=5 nearest neighbors for the selected HRA, the overall accuracy was high. As a result, using the k=5 neighbors within the HRA of 40 km radius, thematic maps of number of trees, basal area, and growing stock per hectare were generated. As compared to the forest statistics based on field sample plots, the estimated means of each parameter from the produced maps were underestimated.

• Korean Journal of Remote Sensing
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• 제30권5호
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• pp.651-664
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• 2014

Recently, the demands of accurate forest carbon stock estimation and mapping are increasing in Korea. This study investigates the feasibility of two methods, k-Nearest Neighbor (kNN) and Regression Tree Analysis (RTA), for carbon stock estimation of pilot areas, Gongju and Sejong cities. The 3rd and 5th ~ 6th NFI data were collected together with Landsat TM acquired in 1992, 2010 and Aster in 2009. Additionally, various vegetation indices and tasseled cap transformation were created for better estimation. Comparison between two methods was conducted by evaluating carbon statistics and visualizing carbon distributions on the map. The comparisons indicated clear strengths and weaknesses of two methods: kNN method has produced more consistent estimates regardless of types of satellite images, but its carbon maps were somewhat smooth to represent the dense carbon areas, particularly for Aster 2009 case. Meanwhile, RTA method has produced better performance on mean bias results and representation of dense carbon areas, but they were more subject to types of satellite images, representing high variability in spatial patterns of carbon maps. Finally, in order to identify the increases in carbon stock of study area, we created the difference maps by subtracting the 1992 carbon map from the 2009 and 2010 carbon maps. Consequently, it was found that the total carbon stock in Gongju and Sejong cities was drastically increased during that period.

• Korean Journal of Remote Sensing
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• 제37권2호
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• pp.291-304
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• 2021

Updating a forest type map is essential for sustainable forest resource management and monitoring to cope with climate change and various environmental problems. According to the necessity of efficient and wide-area forestry remote sensing, CAS500-4 (Compact Advanced Satellite 500-4; The agriculture and forestry satellite) project has been confirmed and scheduled for launch in 2023. Before launching and utilizing CAS500-4, this study aimed to pre-evaluation the possibility of satellite-based tree species classification using RapidEye, which has similar specifications to the CAS500-4. In this study, the study area was the Chuncheon forest management complex, Gangwon-do. The spectral information was extracted from the growing season image. And the GLCM texture information was derived from the growing and non-growing seasons NIR bands. Both information were used to classification with random forest machine learning method. In this study, tree species were classified into nine classes to the coniferous tree (Korean red pine, Korean pine, Japanese larch), broad-leaved trees (Mongolian oak, Oriental cork oak, East Asian white birch, Korean Castanea, and other broad-leaved trees), and mixed forest. Finally, the classification accuracy was calculated by comparing the forest type map and classification results. As a result, the accuracy was 39.41% when only spectral information was used and 69.29% when both spectral information and texture information was used. For future study, the applicability of the CAS500-4 will be improved by substituting additional variables that more effectively reflect vegetation's ecological characteristics.

• Journal of the Korea Society of Computer and Information
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• 제26권6호
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• pp.81-88
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• 2021

In this paper, we design and implement a bus information notification application based on the GPS sensor of a smartphone. This application provides the ability to check bus stops within a radius of 200m based on the current user's location using the smartphone's GPS sensor, Google Map, and open API. In addition, if you click the marker of the desired stop, you can see the name of the stop, and click the view arrival information button to check the detailed bus arrival information of the stop. In addition, it provides a function to check the location information of pharmacies, nonghyups, and post offices that sell public masks, the names of public mask stores, and mask inventory through the public mask store button. Each icon was used differently to make visual differences in order to easily indicate the difference between the times of public mask sales and bus stops. In addition, if you want to know the information of other bus stops and the route of the desired bus, not around the user's location, click the bus stop search button. Finally, after storing the destination stop or location, it implements a function that provides an alarm when it approaches the location.