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Actions to Expand the Use of Geospatial Data and Satellite Imagery for Improved Estimation of Carbon Sinks in the LULUCF Sector

  • Ji-Ae Jung;Yoonrang Cho;Sunmin Lee;Moung-Jin Lee
    • Korean Journal of Remote Sensing
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    • v.40 no.2
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    • pp.203-217
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    • 2024
  • The Land Use, Land-Use Change and Forestry (LULUCF) sector of the National Greenhouse Gas Inventory is crucial for obtaining data on carbon sinks, necessitating accurate estimations. This study analyzes cases of countries applying the LULUCF sector at the Tier 3 level to propose enhanced methodologies for carbon sink estimation. In nations like Japan and Western Europe, satellite spatial information such as SPOT, Landsat, and Light Detection and Ranging (LiDAR)is used alongside national statistical data to estimate LULUCF. However, in Korea, the lack of land use change data and the absence of integrated management by category, measurement is predominantly conducted at the Tier 1 level, except for certain forest areas. In this study, Space-borne LiDAR Global Ecosystem Dynamics Investigation (GEDI) was used to calculate forest canopy heights based on Relative Height 100 (RH100) in the cities of Icheon, Gwangju, and Yeoju in Gyeonggi Province, Korea. These canopy heights were compared with the 1:5,000 scale forest maps used for the National Inventory Report in Korea. The GEDI data showed a maximum canopy height of 29.44 meters (m) in Gwangju, contrasting with the forest type maps that reported heights up to 34 m in Gwangju and parts of Icheon, and a minimum of 2 m in Icheon. Additionally, this study utilized Ordinary Least Squares(OLS)regression analysis to compare GEDI RH100 data with forest stand heights at the eup-myeon-dong level using ArcGIS, revealing Standard Deviations (SDs)ranging from -1.4 to 2.5, indicating significant regional variability. Areas where forest stand heights were higher than GEDI measurements showed greater variability, whereas locations with lower tree heights from forest type maps demonstrated lower SDs. The discrepancies between GEDI and actual measurements suggest the potential for improving height estimations through the application of high-resolution remote sensing techniques. To enhance future assessments of forest biomass and carbon storage at the Tier 3 level, high-resolution, reliable data are essential. These findings underscore the urgent need for integrating high-resolution, spatially explicit LiDAR data to enhance the accuracy of carbon sink calculations in Korea.

Evaluation of Spectral Band Adjustment Factor Applicability for Near Infrared Channel of Sentinel-2A Using Landsat-8 (Landsat-8을 활용한 Sentinel-2A Near Infrared 채널의 Spectral Band Adjustment Factor 적용성 평가)

  • Nayeon Kim;Noh-hun Seong;Daeseong Jung;Suyoung Sim;Jongho Woo;Sungwon Choi;Sungwoo Park;Kyung-Soo Han
    • Korean Journal of Remote Sensing
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    • v.39 no.3
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    • pp.363-370
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    • 2023
  • Various earth observation satellites need to provide accurate and high-quality data after launch. To maintain and enhance the quality of satellite data, it is crucial to employ a cross-calibration process that accounts for differences in sensor characteristics, such as the spectral band adjustment factor (SBAF). In this study, we utilized Landsat-8 and Sentinel-2A satellite imagery collected from desert sites in Libya4, Algeria3, and Mauritania2 among pseudo-invariant calibration sites to calculate and apply SBAF, thereby compensating the uncertainties arising from variations in bandwidths. We quantitatively compared the reflectance differences based on the similarity of bandwidths, including Blue, Green, Red, and both the near-infrared (NIR) narrow, and NIR bands of Sentinel-2A. Following the application of SBAF, significant results with reflectance differences of approximately 1% or less were observed for all bands except NIR. In the case of the Sentinel-2A NIR band, it exhibited a significantly larger bandwidth difference compared to the NIR narrow band. However, after applying SBAF, the reflectance difference fell within the acceptable error range (5%) of 1-2%. It indicates that SBAF can be applied even when there is a substantial difference in the bandwidths of the two sensors, particularly in situations where satellite utilization is limited. Therefore, it was determined that SBAF could be applied even when the bandwidth difference between the two sensors is large in a situation where satellite utilization is limited. It is expected to be helpful in research utilizing the quality and continuity of satellite data.

Detection and Assessment of Forest Cover Change in Gangwon Province, Inter-Korean, Based on Gaussian Probability Density Function (가우시안 확률밀도 함수기반 강원도 남·북한 지역의 산림면적 변화탐지 및 평가)

  • Lee, Sujong;Park, Eunbeen;Song, Cholho;Lim, Chul-Hee;Cha, Sungeun;Lee, Sle-gee;Lee, Woo-Kyun
    • Korean Journal of Remote Sensing
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    • v.35 no.5_1
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    • pp.649-663
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    • 2019
  • The 2018 United Nations Development Programme (UNDP) report announced that deforestation in North Korea is the most extreme situation and in terms of climate change, this deforestation is a global scale issue. To respond deforestation, various study and projects are conducted based on remote sensing, but access to public data in North Korea is limited, and objectivity is difficult to be guaranteed. In this study, the forest detection based on density estimation in statistic using Landsat imagery was conducted in Gangwon province which is the only administrative district divided into South and North. The forest spatial data of South Korea was used as data for the labeling of forest and Non-forest in the Normalized Difference Vegetation Index (NDVI), and a threshold (0.6658) for forest detection was set by Gaussian Probability Density Function (PDF) estimation by category. The results show that the forest area decreased until the 2000s in both Korea, but the area increased in 2010s. It is also confirmed that the reduction of forest area on the local scale is the same as the policy direction of urbanization and industrialization at that time. The Kappa value for validation was strong agreement (0.8) and moderate agreement (0.6), respectively. The detection based on the Gaussian PDF estimation is considered a method for complementing the statistical limitations of the existing detection method using satellite imagery. This study can be used as basic data for deforestation in North Korea and Based on the detection results, it is necessary to protect and restore forest resources.

Classification of Mental States Based on Spatiospectral Patterns of Brain Electrical Activity

  • Hwang, Han-Jeong;Lim, Jeong-Hwan;Im, Chang-Hwan
    • Journal of Biomedical Engineering Research
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    • v.33 no.1
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    • pp.15-24
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    • 2012
  • Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (±9.64) was attained for the proposed method; whereas an average accuracy of 57% (±5.68) was attained for the conventional method (significance was assessed by the one-tail paired t-test, p < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.

Study of CO2 Absorption in Forest by Airborn LiDAR Data (LiDAR 자료를 이용한 산림 CO2 흡수량 산출 연구)

  • Go, Sin Young;Park, Jung Gi;Cho, Gi Sung
    • Journal of Korean Society for Geospatial Information Science
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    • v.21 no.4
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    • pp.29-35
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    • 2013
  • Generally, Calculation of carbon dioxide absorption in the forest area is calculated using the information of the forest, such as tree height and DBH(Diameter of Breast Height). Tree height and DBH of these are obtained using the remote sensing data such as imagery and information of local forest survey. However, Mixed forest with a high proportion of field survey to lower the accuracy of forest information. In this study, vertical structure of the tree were identified by applying region growing method based on the slope using LiDAR data and height and number of the tree were identified by applying extracting top of the tree algorithm. Through the vertex tree extraction algorithm to identify height of tree and the number of individuals, substitute this for the DBH relation formula which is drawn from data through field surveys. In this, a quantitative calculation of carbon dioxide absorption were able to calculate the basic data. Also, carbon dioxide absorption of three type trees were calculated and average per unit area of carbon dioxide absorption were able to estimate.

Analysis on the Long-Term Shoreline Changes for Beaches Near Bangpo Port Using Aerial Imagery (항공사진을 이용한 방포항 인근 해빈의 장기간 해안선 변화 분석)

  • Kim, Baeck-Oon;Yun, Kong-Hyun;Lee, Chang-Kyung
    • Korean Journal of Remote Sensing
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    • v.29 no.5
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    • pp.477-486
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    • 2013
  • To analyze tendency of temporal and spatial change of shorelines and to estimate rate of shoreline changes using long-term shoreline change data is very important for the coastal environmental management. In this study, investigation was conducted to estimate the rate of shoreline changes using long-term shoreline change data from the year 1985 to 2009 aerial photographs. In this process aerial triangulation, GPS surveying and digital mapping was done for the estimation of changes. As the results, shorelines of Bangpo and Kkotji Beach retreated at a maximum rate of 0.2 m/yr and 0.8 m/yr, respectively. The shoreline could be changed by various factors. However, it was presumed that coastal erosion has been mainly affected by retaining wall constructed in the late 1990s.

Production of the Thematic Standard Map for Coastal Regions Based on Remote Sensing Data (원격탐사기반 연안주제도 선진화 방안 연구)

  • Lee, Yoon-Kyung;Ryu, Joo-Hyung
    • Korean Journal of Remote Sensing
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    • v.33 no.6_2
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    • pp.1159-1169
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    • 2017
  • Since 1970's, various satellite remote sensing technology has been developed and accumulation of observational data has been carried out, and coastal environment monitoring research is being conducted through analysis of relationship between satellite remote sensing data and coastal sedimentary environment. However, generation of coastal thematic maps by various national agencies are not included in the production of coastal themes using satellite imagery. In this study, we want to deduct the current problems through survey of marine spatial information provided by the government. The purpose of this study is to investigation the direction of the development of large scale coastal thematic maps by analyzing coastal boundary map, coastal topographic map and coastal sedimentary facies map.

Change Detection Comparison of Multitemporal Infrared Satellite Imagery Using Relative Radiometric Normalization (상대 방사 정규화를 이용한 다시기 적외 위성영상의 변화탐지 비교)

  • Han, Dongyeob;Song, Jeongheon;Byun, Younggi
    • Korean Journal of Remote Sensing
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    • v.33 no.6_3
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    • pp.1179-1185
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    • 2017
  • The KOMPSAT-3A satellite acquires high-resolution MWIR images twice a day compared to conventional Earth observing satellites. New radiometric information of Earth's surface can be provided due to different characteristics from existing SWIR images or TIR images. In this study, the difference image of multitemporal images was generated and compared with existing infrared images to find the characteristics of KOMPSAT-3A MWIR satellite images. A co-registration was performed and the difference between pixel values was minimized by using PIFs (Pseudo Invariant Features) pixel-based relative normalization. The experiment using Sentinel-2 SWIR image, Landsat 8 TIR image, and KOMPSAT-3A MWIR image showed that the distinction between artifacts in the difference image of KOMPSAT-3A is prominent. It is believed that the utilization of KOMPSAT-3A MWIR images can be improved by using the characteristics of IR image.

The Classifications using by the Merged Imagery from SPOT and LANDSAT

  • Kang, In-Joon;Choi, Hyun;Kim, Hong-Tae;Lee, Jun-Seok;Choi, Chul-Ung
    • Proceedings of the KSRS Conference
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    • 1999.11a
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    • pp.262-266
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    • 1999
  • Several commercial companies that plan to provide improved panchromatic and/or multi-spectral remote sensor data in the near future are suggesting that merge datasets will be of significant value. This study evaluated the utility of one major merging process-process components analysis and its inverse. The 6 bands of 30×30m Landsat TM data and the 10×l0m SPOT panchromatic data were used to create a new 10×10m merged data file. For the image classification, 6 bands that is 1st, 2nd, 3rd, 4th, 5th and 7th band may be used in conjunction with supervised classification algorithms except band 6. One of the 7 bands is Band 6 that records thermal IR energy and is rarely used because of its coarse spatial resolution (120m) except being employed in thermal mapping. Because SPOT panchromatic has high resolution it makes 10×10m SPOT panchromatic data be used to classify for the detailed classification. SPOT as the Landsat has acquired hundreds of thousands of images in digital format that are commercially available and are used by scientists in different fields. After the merged, the classifications used supervised classification and neural network. The method of the supervised classification is what used parallelepiped and/or minimum distance and MLC(Maximum Likelihood Classification) The back-propagation in the multi-layer perception is one of the neural network. The used method in this paper is MLC(Maximum Likelihood Classification) of the supervised classification and the back-propagation of the neural network. Later in this research SPOT systems and images are compared with these classification. A comparative analysis of the classifications from the TM and merged SPOT/TM datasets will be resulted in some conclusions.

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OBSERVATION OF MICROPHYTOBENTHIC BIOMASS IN HAMPYEONG BAY USING LANDSAT TM IMAGERY

  • Choi, Jae-Won;Won, Joon-Sun;Lee, Yoon-Kyung;Kwon, Bong-Oh;Koh, Chul-Hwan
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.441-444
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    • 2005
  • The goal of this study is to investigate the relationship between microphytobenthic biomass and normalized vegetation index obtained from Landsat TM images. Monitoring a seasonal change of microphytobenthic biomass in the sand bar is specifically focused. Since the study area, Hampyeong Bay, was difficult to approach, we failed to obtain ground truths simultaneously on satellite image acquisition. Instead, chlorophyll-a concentration in surface top layer was measured on different dates for microphytobenthic biomass. Although data were acquired on different dates, a correlation between the field and satellite images was calculated for investigating general trends of seasonal change. NDVI and tasseled cap transformed images were also used to review the variation of microphytobenthic biomass by using Landsat TM and ETM+ images. Atmosphere effects were corrected by applying COST model. Seaweeds were also flouring in the same season of microphytobentic blooming. Songseok-ri area was minimally affected by seaweeds from February to May, and selected as a test site. NDVI value was classified into high-, moderate-, and low-grade. It was well developed over fme-grained sediments and rapidly reduced from May to November over sand bar. In this bay, correlation between grain size and microphytobenthic biomass was clearly seen. From the classified NDVI and tasseled cap transformed data, we finally constructed spatial distribution and seasonal variation maps of microphytobenthic biomass.

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