Kyoung-Jin Pi

11-year variability of summer snow cover extent over Himalayas

Snow is a component of the cryosphere which has played an important role in Earth energy balance. Northern hemisphere snow cover extent (SCE) has steadily decreased since 1980 and in recently the trend of SCE is sharply decreased. Because Himalaya regions shows most significant changes except for the Arctic, we analyzed this region for SCE. We used Moderate Resolution Imaging Spectroradiometer (MODIS) snow product from 2001 to 2011 in august. Analysis was made by considering some conditions (region, elevation, longitude and climate) which can affect the changes in SCE. The entire SCE in Himalaya for 11 years has steadily increased(+55,098 km2). Trends for SCE in western region has increased(+77,781km2), But trend for central and eastern have decreased -3,453 km2, -19,230km2, respectively. According to elevation increases, the ratio of snow in each study area is increased. In 30°N~35°N SCE shows increased trend, 27°N~28°N shows decreased trend. In tundra climate, trends for SCE are similar to regional analysis. whereas the result in tropical climates trend was increased. these performed result shows different side for change of SCE depending on each condition. The result of this study were similar to the rapid decline of the northern hemisphere SCE area in recent. The result of this study can be used to help management to water budget in Central-Asia country located to Himalayas.

Estimation of maximum air temperature using COMS data in Northeast Asia

Air temperature (Ta) plays important role for the circulation of energy and water between the surface and atmosphere. Ta was accurately measured from ground observation stations. However, the number of ground observation stations is limited, and Ta is influenced from temporal and spatial change. In this study, Ta was estimated using satellite data from April 2011 to March 2012 in the Northeast Asia where consist of the various ecosystem. States of surface and atmosphere were considered through Normalized Difference Water Index (NDWI) and the differences of brightness temperature values of 11μm (TBB1) and 12μm (TBB2). Dataset was divided into nine cases that had seasonal characteristics according surface states (NDWI) and atmosphere states (TBB1-TBB2). Ta was acquired from 174 ground observation stations, and multiple regression equation of each case was consisted of LST, NDVI, TBB1-TBB2. The weighting region was set to be within 8.33% of total density from boundary area of cases in order to reduce the errors that can occur due to the small value. The weighting was applied as distance from the nearest four points. The spatial representativeness of estimated Ta was determined as 9 by 9 window size. R-squared of estimated Ta from satellite was 0.94, RMSE was 2.98 K, Bias was 0.56 K.

An influence assessment of GSICS correction using sea surface temperature from geostationary satellite: COMS

There is a strong need for accurate estimation of radiance from satellite regarding establishing a climate records such as global climate circulation, change and Earth’s atmosphere. It is important that exact radiance measurements from satellite to numerical weather prediction models for climate change detection. Furthermore, accurate measurements from satellite rely on calibration of channel data in terms of the radiometric characteristics. Related to improved calibration and inter-calibration of the sensors, the World Meteorological Organization (WMO) and the Coordination Group for Meteorological Satellite (CGMS) initiated the Global Space-based Inter-Calibration System (GSICS) in 2005, which provide coefficients to the user community to adjust satellite observations. To assess influence of the GSICS corrections and impacts of input parameters changes on satellite products, the coefficients of the GSICS corrections were applied to infrared (IR) data from Communication Ocean and Meteorological Satellite (COMS), which have Meteorological Imager (MI) sensor for meteorological missions. The IR data centered at wavelengths of 10.8 (IR1) and 12.0μm (IR2) from the COMS MI were compared with that of the Infrared Atmospheric Sounding Interferometer (IASI) sensor, which is reference sensor of the GSICS corrections. The IR1 and IR2 data that were corrected by GSICS produced Sea Surface Temperature (SST), which has been influenced by input parameters such as IR data and solar zenith angle. As a result of comparison with in situ measurements, the Global Telecommunication System (GTS) buoy data, COMS IR data that were corrected by the GSICS corrections produced high quality products of SST than original COMS IR data.

Assessment of tundra-taiga boundary changes using MODIS LAI data

Surface of the earth temperature of the earth caused phenomenon that rise and is global warming as greenhouse gas concentration into waiting by continuous discharge of greenhouse gas increases since passing industrial revolution. While gravity about climate fluctuation is risen worldwide, place that can diminish successively biggest surface of the earth change by global warming is high latitude area of polar regions. This study observed distribution of vegetation to confirm change of tundra-taiga boundary. Tundra-taiga boundary is used to observe the transfer of vegetation pattern because it is very sensitive to human activity, natural disturbances and climate change. The circumpolar tundra-taiga boundary could observe reaction about some change. Reaction and confirmation about climate change were definite than other place. This study used Leaf Area Index(LAI) 8-Day data in August from 2000 to 2009 that acquire from Terra satellite MODerate resolution Imaging Spectroradiometer(MODIS) sensor and used Köppen Climate Map, Global Land Cover 2000 for reference data. This study conducted analysis of spatial distribution in low density vegetated areas and inter-annual / zonal analysis for using the long period data of LAI. Change of LAI was confirmed by analysis based on boundary value of LAI in study area. Development of vegetation could be confirmed by area of grown vegetation(730,325km2 ) than area of reduced vegetation(22,372km2 ) in tundra climate. Also, area was increased with the latitude 64°N~66° N as the center and around the latitude 62° N through area analysis by latitude. Vegetation of tundra-taiga boundary was general increase from 2000 to 2009. While area of reduced vegetation was a little, area of vegetation growth and development was increased significantly.

An improvement of satellite-based algorithm for gross primary production estimation optimized over Korea

Monitoring the global gross primary production (GPP) is relevant to understanding the global carbon cycle and evaluating the effects of interannual climate variation on food and fiber production. GPP, the flux of carbon into ecosystems via photosynthetic assimilation, is an important variable in the global carbon cycle and a key process in land surface-atmosphere interactions. The Moderate-resolution Imaging Spectroradiometer (MODIS) is one of the primary global monitoring sensors. MODIS GPP has some of the problems that have been proven in several studies. Therefore this study was to solve the regional mismatch that occurs when using the MODIS GPP global product over Korea. To solve this problem, we estimated each of the GPP component variables separately to improve the GPP estimates. We compared our GPP estimates with validation GPP data to assess their accuracy. For all sites, the correlation was close with high significance (R2 = 0.8164, RMSE = 0.6126 g·C·m-2·d-1, bias = -0.0271 g·C·m-2·d-1). We also compared our results to those of other models. The component variables tended to be either over- or under-estimated when compared to those in other studies over the Korean peninsula, although the estimated GPP was better. The results of this study will likely improve carbon cycle modeling by capturing finer patterns with an integrated method of remote sensing. Keywords: VEGETATION, Gross Primary Production, MODIS.

Inter-annual variation of NDVI over Korea Peninsula using harmonic analysis

Global warming and climatic changes due to human activities impact on marine and terrestrial ecosystems, which feedbacks to climate system. These negative feedbacks amplify or accelerate again global climate change. In particular, life cycle of vegetation sensitively vary according to global climate change. This study attempts to analyze quantitatively vegetation change in Korea peninsula using harmonic analysis. Satellite data was extracted from SPOT/VEGETATION S10 MVC (Maximum Value Composite) NDVI (Normalized Difference Vegetation Index) products during 10 years (1999 to 2008) around Korea peninsula. This NDVI data set was pre-processed to correct noise pixels cause by cloud and ground wetness. Variation of vegetation life cycle was analyzed through amplitudes and phases of annual harmonic components (first harmonic components) per year for two land cover types (cropland and forest). The results clearly show that the peak of vegetation life cycle in Korea peninsula is brought forward to early. Especially, it represents that the phases over low latitudes area between 32.8°N and 38°N steadily decrease every year both forest and cropland. The study estimated that phase values moved up approximately 0.5 day per year in cropland and 0.8 day per year in forest.

Dynamic threshholds for land surface change detection using image differencing

Change detection using satellite imagery has been increasing the need for effective land management, land environmental changes. Utilizing remote sensing data analysis is high application possibility about management in the field of environmental changes, because relatively wide area in a short-term is to get the visual information. The principal objective of this study was to provide that statistic approaches to determine dynamic thresholds for detection of significant change using image differencing of NDVI (Normalized Difference Vegetation Index). Dynamic threshold look-up-table obtained from statistics (per-pixel standard deviations over 10 years) of 10-year wide-swath satellite data (SPOT/VEGETATION) was used to apply Landsat-based change detection. Two areas is utilized in research using Landsat 7 ETM+ images that have resolution 30×30 m. When achieve changed detection taking advantage of image differencing technique which is one of the changed detection technique, it choose more dynamic critical value taking advantage of middle and low resolution satellite data. As a result, it is effective that takes advantage of NDVI value more than reflection value and method to decide change standard is effective that take advantage of statistics.

Water stress monitoring using NDWI around deserts of China and Mongolia

The fluctuation of vegetation water condition around desert area is one of most important parameters to interpret the desertification expansion. United Nations reported that about 35 million square kilometers of land are subject to desertification. Historically, many parts of China have been suffered from severe desertification. This paper attempts an analysis for spatio-temporal variation characteristics of vegetation drought status around China and Mongolia desert with remotely sensed data. Time series images (1 January, 1999 - 31 December 2006) obtained from SPOT/VEGETATION were used to monitor inter-annual variability of water condition. SPOT/VEGETATION satellite, which has a fine temporal resolution and sensitive to vegetation growth, could be very useful to detect large scale dynamics of environmental changes and desertification progress. The main objective of the study is analyzing water status around China and Mongolia desert and predicting a risk area of desertification. In this study, NDWI (Normalized Difference Water Index) is used to monitor vegetation water condition (drought status) over the study area. To interpret the relationship between vegetation drought status and vigor, NDVI (Normalized Difference Vegetation Index) was employed in ensemble with NDWI. Annual total precipitation from NCEP/NCAR reanalysis data is used as subsidiary data. The study area from 73°36´E to 120°41´E longitude and from 30°81´N to 52°13´N longitude in northern China and whole Mongolia. NDWI value around desert has a range from -0.05 to -0.35 and NDWI values are decreased during the study period. Each year precipitation patterns are similar to yearly mean NDWI value. The study detected several areas where NDWI is dramatically decreased for 8 years, especially northeast part of Mongolian Gobi desert and southeast part of China Taklamakan desert.