研究者業績

朴 慧美

パク ヘミ  (HAEMI PARK)

基本情報

所属
上智大学 地球環境学研究科地球環境学専攻 助教

研究者番号
60854700
ORCID ID
 https://orcid.org/0000-0002-4289-0713
J-GLOBAL ID
202201017586855230
researchmap会員ID
R000034352

主要な論文

 8
  • Haemi Park, Junghee Lee, Cheolhee Yoo, Seongmun Sim, Jungho Im
    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 8614-8626 2021年  査読有り筆頭著者
  • Haemi Park, Wataru Takeuchi, Kazuhito Ichii
    Remote Sensing 12(2) 250-250 2020年1月10日  査読有り筆頭著者
    Tropical peatland ecosystems are known as large carbon (C) reservoirs and affect spatial and temporal patterns in C sinks and sources at large scales in response to climate anomalies. In this study, we developed a satellite data-based model to estimate the net biosphere exchange (NBE) in Indonesia and Malaysia by accounting for fire emissions (FE), ecosystem respiration (R-e), and gross primary production (GPP). All input variables originated from satellite-based datasets, e.g., the precipitation of global satellite mapping of precipitation (GSMaP), the land surface temperature (LST) of the moderate resolution imaging spectroradiometer (MODIS), the photosynthetically active radiation of MODIS, and the burned area of MODIS fire products. First, we estimated the groundwater table (GWT) by incorporating LST and precipitation into the Keetch-Byram Drought Index (KBDI). The GWT was validated using in-situ measurements, with a root mean square error (RMSE) of 24.97 cm and an r-squared (R-2) of 0.61. The daily GWT variations from 2002 to 2018 were used to estimate respiration (R-e) based on a relationship between the in situ GWT and flux-tower-observed R-e. Fire emissions are a large direct source of CO2 from terrestrial ecosystems into the atmosphere and were estimated by using MODIS fire products and estimated biomass. The GPP was calculated based on the MODIS GPP product after parameter calibration at site scales. As a result, averages of long-term (17 years) R-e, GPP, FE, and NBE from whole peatlands in the study area (6 degrees N-11 degrees S, 95-141 degrees E) were 66.71, 39.15, 1.9, and 29.46 Mt C/month, respectively. We found that the NBE from tropical peatlands in the study area was greater than zero, acting as a C source. R-e and FE were influenced by El Nino, and the value of the NBE was also high in the El Nino period. In future studies, the status of peatland degradation should be clarified in detail to accurately estimate the C budget by applying appropriate algorithms of R-e with delineations of types of anthropogenic impacts (e.g., drainages and fires).
  • Sumin Park, Haemi Park, Jungho Im, Cheolhee Yoo, Jinyoung Rhee, Byungdoo Lee, ChunGeun Kwon
    PLOS ONE 14(10) e0223362-e0223362 2019年10月10日  査読有り筆頭著者
  • Haemi Park, Jungho Im, Miae Kim
    AGRICULTURAL AND FOREST METEOROLOGY 271 180-192 2019年6月  査読有り筆頭著者
    Gross primary production (GPP) is a crucial factor in the carbon cycle especially for the absorption of carbon dioxide into the biosphere from the atmosphere. A large discrepancy between a satellite-based GPP product named MOD17A2H GPP and in-situ data measured at eddy covariance flux towers has been identified over East Asia where the biome types and climatic characteristics are heterogeneous with rugged terrain. For that reason, this study focuses on two potential major error sources in MOD17A2H GPP, which are the coarse resolution land cover information and inappropriate meteorological parameters. The finer resolution observation and monitoring global land cover (FROM-GLC) and the MODIS land cover product collection 5.1 (MCD12Q1) were used to describe biome types in detail, by combining spatial distribution from FROM-GLC and the phenological characteristics of land cover from MCD12Q1. Meteorological parameters were optimized using the 55-years Japanese reanalysis meteorological data (JRA-55). The light use efficiency of the MOD17 GPP algorithm was modified using the combined land cover information (FROM-MCD). Although the use of FROM-MCD and JRA-55 did not improve MOD17A2H GPP, the optimization of two meteorological parameters-daily minimum air temperature (TMIN) and vapor pressure deficit (VPD) significantly improved the GPP algorithm for East Asia. The results show that the root mean square errors (RMSEs) between the estimated and in situ GPPs decreased from 21.83 (gC/m(2)/8days) to 16.11 (gC/m(2)/8days) through optimizing the two parameters at 9 flux tower sites. The optimized TMIN and VPD thresholds in the MOD17 GPP algorithm were applied to the entire study area (i.e., East Asia) according to the Koppen-Geiger climate classes. The estimated GPP using the proposed approach was compared to GPPs from widely used process-based models (i.e., VISIT, BEAMS, and BESS), which confirmed that the proposed approach with locally optimized meteorological parameters improved on the underestimation of the MOD17 GPP algorithm for East Asia. The uncertainty of the VPDmin parameter was revealed to be larger than that of TMINmax.

MISC

 10
  • Haemi Park, Takeo Tadono
    2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2021年7月11日  
  • Haemi Park, Daiki Shimizu, Wataru Takeuchi
    IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium 4777-4778 2020年9月26日  
    Tropical peatlands have been experienced human-induced disturbances including drainage constructions, oil palm plantations, and wildfires. Since peat soils consist of large organic matter, it is important for the carbon cycle in the tropical area. This study aimed at the delineating of the distribution of drainage canals using microwave images. As the result of validation, overall accuracy was 56.3 % and about 10 % of the peatland areas detected as the drainage canals. For further study, a Keetch Byram Drought Index-based groundwater table will be revised applying the decreasing effect by drainage canals.
  • Daiki Shimizu, Wataru Takeuchi, Haemi Park
    40th Asian Conference on Remote Sensing, ACRS 2019:Progress of Remote Sensing Technology for Smart Future 2020年  
    © 2020 40th Asian Conference on Remote Sensing, ACRS 2019: "Progress of Remote Sensing Technology for Smart Future". All rights reserved. Carbon dioxide (CO2) accounts for large part of greenhouse gas and its budget estimation is heavily needed for quantitative evaluation of greenhouse gas (GHG) management policies like carbon emission trading. Among various CO2 emission sources, peatlands are dominant ones and the carbon stored in these areas occupies one-third of global soil organic carbon. In peatlands, the effect of drainage canals is significant since the decrease of groundwater level is the major factor in CO2 emission in peatlands and is affected by those canals, therefore grasping the distribution of drainage canal is essential. Few researches have been conducted related to this theme although drainage canal of Indonesia has been known as human effect. These studies leave an issue in terms of accuracy in mapping the drainage canals owing to the characteristics of used methods. The objective of this research is to make more precise drainage canal map in non-forested area in Indonesia using microwave satellite images from ALOS PALSAR-2. This research selected HH polarimetric for edge detection of drainage canal.
  • Haemi Park, Wataru Takeuchi
    40th Asian Conference on Remote Sensing, ACRS 2019:Progress of Remote Sensing Technology for Smart Future 2020年  
    © 2020 40th Asian Conference on Remote Sensing, ACRS 2019: "Progress of Remote Sensing Technology for Smart Future". All rights reserved. Forest ecosystem in cool to cold climate plays a role of major carbon absorber. In Russia, there are fire events continuously along with recent economic development. Against the limitation of lack of in-situ observation data, this study aimed to generate a system for estimating land surface dryness using satellite-based Keetch-Byram drought index (KBDI). The KBDI is calculated by land surface temperature (LST) of Himawari, and precipitation of Global Rainfall Watch (GSMaP). The empirical method of KBDI considers vegetation status as a proxy of evapotranspiration. Especially, fire events were detected by MOD14 hotspot data and the carbon emission was calculated with reference to the modeled biomass from the Vegetation Integrative Simulator for Trace gases (VISIT). Since this region has large amount of snow during winter, daily snow cover area of AMSR-2 was used for the comparison between KBDI and snow covers. As a result, the land surface is dry in summer because of the high LST and this related to the increase of the fire occurrence. While the KBDI is low or not available with low LST in winter, the fire occurrences were also low. Fire occurrences have rapidly increased in spring, however, KBDI is not estimated during the season when some snow coverages still remained. It implied if KBDI is used as an index of fire control in this region, we need to consider snow accumulation and melting algorithms which let provide the moisture into the soil layer especially in spring season, and it might affect to prevent the fire occurrence in high latitudinal regions.
  • Haemi Park, Wataru Takeuchi, Kazuhito Ichii
    2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) 6859-6862 2019年  
    Peatland is a natural carbon reservoir in terrestrial ecosystem. The ground water table in peatland is a key factor for carbon exchange through soil decomposition or carbon sink. Especially, Indonesia has the biggest peatland area in Asia. This study focused on the carbon dioxide budget between emissions by ecosystem respiration including fire event and the absorption by photosynthesis. As the result, the annual average of net biome ecosystem carbon dioxide exchange during 12 years were reached to 195.03 MtC/yr. Over the three times of CO2 from fire emissions were emitted by ecosystem respiration from whole peatlands in Indonesia.
  • Haemi Park, Sonidarmawan, Wataru Takeuchi
    35th Asian Conference on Remote Sensing 2014, ACRS 2014: Sensing for Reintegration of Societies 2014年  
    Peat land is one of the largest CO2 emission source due to the fires and decomposition. Especially, tropical region has 10% of global peat soil. However, ground water table of tropical peat lands is decreased drastically by human activity for converting to agricultural use. Fire events on peat lands are continued for land clearance. Moreover, dryness of peat promotes fire occurrence. The objective of this study is to reveal vulnerability against fire of Indonesian peat lands by using analysis of relationship between location of fire and human accessibility. First of all, MOD14 of MODIS hotspot was used with method of extraction high temperature of surface. The loss of biomass is calculated by sum of above ground biomass and soil organic matter. Secondly, ground water table describing peat soil dryness was calculated by Keetch-Byram drought index (KBDI). Satellite-sensed data with precipitation (GSMaP) and land surface temperature (MTSAT) were used. Thirdly, vulnerability against fire is revealed by road distribution and detection of fire location. The reasons of fire are dryness of peat or human activity. This study investigated the distance to the road as an indicator of vulnerability peat area because human accessibility is considered as a possibility of fire. Finally, the reason of fire is classified by two cases. The one is natural reason such as dryness and the other is artificial way. In case of that the fire was occurred under drought condition and was far from road, it is considered to natural burning event caused by dryness. If fire was occurred even under moist condition and location was close from street, it is expected as human made fire. For reducing CO2 emission from peat lands of Indonesia, not only rewetting ground water table but also control of human disturbance are important. Thus, fire vulnerability analysis can be useful data on reducing CO2 emission of tropical peat lands.
  • Haemi Park, Wataru Takeuchi
    2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) 2014年  
    Carbon dioxide (CO2) budget from tropical peat lands in Indonesia was estimated by using satellite data. Indonesia has many CO2 sources that related with peat lands. Our previous results have been continued to estimate CO2 budget from ground water decreasing. The new motivation of this study is to estimate human impact on the peat lands hydrologic environment. The drainage canal is supposed to be artificial in this region. ALOS PALSAR mosaic data was used for detection of drainage canal in Indonesia. Canny edge detection method was used. As the result, daily soil respirations of Jambi and Palangkaraya became 12.63% and 16.84% larger than before consideration of human effect.
  • Haemi Park, Wataru Takeuchi, Kazuhito Ichii
    34th Asian Conference on Remote Sensing 2013, ACRS 2013 3 2065-2071 2013年  
    The carbon budget on global peat land is a big research issue recently. In this study, ground water table was used to quantify the CO2 emission. Peat lands have much water contents in their soil which was not decomposed dead plants. However if their ground water table decline, the peat soil will be converted to a source of carbon to atmosphere. Therefore we estimated ground water table of global with remote sensed data. The objectives of this study are to reveal the carbon budget of peat lands and to improve recent models with adding soil respiration and fire emission. The result of the model calculation of peat land in Palankaraya were compared with the satellite-based estimation. The model improving was done only one site of the Indonesian peat land (Palankaraya). As the result, annual NEE that is annual average of CO2 emission of global peat lands was 7.86Pg(ER-GPP; 9.75-1.89(Pg)). The annual mean of fire emission from Indonesian peat land was 34.5 Tg. Especially in Indonesia the soil respiration of peat soil was calculated as 384Tg. This result of Indonesian peat land was applied to the results of 7 models. Finally the underestimation of 6 models were modified with soil respiration because of the models didn't include peat soil decomposition into parameters before adding this soil respiration. Copyright© (2013) by the Asian Association on Remote Sensing.
  • Haemi Park, Wataru Takeuchi
    2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) 2856-2859 2013年  
    The peatlands are known as the carbon sink in natural. However some disturbances such as fire and drainage are occurred in Indonesian peatlands. The declining of ground water table is the most influential reason of carbon emission from peatlands. For detecting CO2 emission from peatlands in Indonesia, ground water table was estimated by satellite based precipitation and land surface temperature. The CO2 emission is represented by NEE(Net Ecosystem CO2 Exchange) which can be calculated with this equation; NEE = ER - GPP; where ER is ecosystem respiration, and GPP is gross primary productivity. As the result, the ecosystem respiration was larger than GPP in this peat forest. The annual average of ER was about twice of GPP in this region. The GPP of MOD17A2 is underestimated from in-situ observed GPP with 37.2%. The CO2 emission through fire and respiration is increased when GWT was declined.
  • Haemi Park, Wataru Takeuchi
    33rd Asian Conference on Remote Sensing 2012, ACRS 2012 2 1606-1609 2012年  
    As a cause of global warming, CO2 is most effective green house gas. And many countries are trying to reduce emission of that. However, the report of quantitative description is few still because the amount of CO 2 emission is difficult to estimate for their complex process. One of the main reasons of CO2 emission is from peatlands in natural. The peat land is known as the carbon sink as there is much Carbon that wasn't decomposed. This carbon is increasingly released to the atmosphere due to drainage and fires associated with plantation development and logging. These two reasons of CO2 emission are associated with ground water level. If the ground water level declined, CO2 is released more than before by promoting decomposition and fire from the peatland. The purpose of this study is to assess the inter relationships between CO2 emission and dryness in the peatland by using remote sensing data and ground water level assumption. CO2 emission from peat soil is estimated by calculating CO 2 flux model. And ground water level assumption as dryness of peatland is estimated with KBDI (Keetch-Byram Drought Index) which calculated by using GSMaP for daily precipitation data and MTSAT for land surface temperature data as source of evapotranspiration. KBDI is validated with soil moisture products from AMSR-E (Advanced Microwave Scanning Radiometer for EOS) images. Current distribution of Asian tropical peatland is extracted by MODIS images.

書籍等出版物

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担当経験のある科目(授業)

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所属学協会

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共同研究・競争的資金等の研究課題

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