• Korean Journal of Remote Sensing
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• v.30 no.5
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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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• v.25 no.4
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• pp.311-320
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• 2009

This study was carried out to estimate forest biomass and to produce forest biomass thematic map for Muju county by combining field data from the 5$^{th}$ National Forest Inventory (2006-2007) and satellite data. For estimating forest biomass, two methods were examined using a Landsat TM-5(taken on April 28th, 2005) and field data: multi-variant regression modeling and t-Nearest Neighbor (k-NN) technique. Estimates of forest biomass by the two methods were compared by a cross-validation technique. The results showed that the two methods provide comparatively accurate estimation with similar RMSE (63.75$\sim$67.26ton/ha) and mean bias ($\pm$1ton/ha). However, it is concluded that the k-NN method for estimating forest biomass is superior in terms of estimation efficiency to the regression model. The total forest biomass of the study site is estimated 8.4 million ton, or 149 ton/ha by the k-NN technique.

• Korean Journal of Remote Sensing
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• v.21 no.4
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• pp.253-271
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• 2005

We have analyzed surface water temperature and seasonal stratification of lakes in the Han river system using time-series Landsat images and in situ measurement data. Using NASA equation, at-satellite temperature is derived from 29 Landsat-5 TM and Landsat-7 ETM+ images obtained from 1994 to 2004, and was compared with in situ surface temperature on river-type dam lakes such as Paro, Chuncheon, Euiam, Chongpyong, Paldang, and with 10m-depth temperature on lake-type dam lake Soyang. Although the in situ temperature at the time of satellite data acquisition was interpolated from monthly measurements, the number of images with standard deviation of temperature difference (at-satellite temperature - in situ interpolated temperature) less than $2^{\circ}C$ was 24 on which a novel statistical atmospheric correction could be applied. The correlation coefficient at Lake Soyang was 0.915 (0.950 after correction) and 0.951-0.980 (0.979-0.997 after correction) at other lakes. This high correlation implies that there exist a mixed layer in the shallow river-like dam lakes due to physical mixing from continuous influx and efflux, and the daily and hourly temperature change is not fluctuating. At Lake Soyang, an anomalous temperature difference was observed from April to July where at-satellite temperature is $3-5^{\circ}C$ higher than in situ interpolated temperature. Located in the uppermost part of the Han river system and its influx is governed only by natural precipitation, Lake Soyang develops stratification during this time with rising sun elevation and no physical mixture from influx in this relatively dry season of the year.

• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.83-94
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• 1998

To have a basic idea on the spectral reflectance signature in paddy rice canopy, we measured spectral reflectance from paddy rice canopy(Ilpumbyeo) using spectroradiometer (GER Inc. SFOV : 0.35~2.50 ${\mu}{\textrm}{m}$) in situ weekly or biweekly from transplanting to ripening stage. Spectral reflectance of the visible range (0.4~0.7 ${\mu}{\textrm}{m}$) was decreased to below 5% and then slightly increased again after heading stage in rice canopy. Meanwhile spectral reflectance of the near-infrared range (0.7~1.1 ${\mu}{\textrm}{m}$) was increased to 40~50% and then decreased a great deal after panicle initiation stage in rice canopy. Landsat TM equivalent band set ($\bar{p}$$_{TMi}$) was created by averaging spectral reflectance values to the real TM bands. Correlation analysis between the rice crop variables (LAI, total dry matter) and TM equivalent band set ($\bar{p}$$_{TMi}$) showed that LAI and total dry matter of rice were highly correlated with visible bands such as $\bar{p}$$_{TM1}$, $\bar{p}$$_{TM2}$, and $\bar{p}$$_{TM3}$. Ratio values ($\bar{p}$$_{TMi}$/$\bar{p}$$_{TMi}$) such as $\bar{p}$$_{TM4}$/$\bar{p}$$_{TM2}$ were also highly correlated with rice crop variables such as LAI and total dry matter.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.61-73
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• 2014

This study reviewed an application of water quality estimation using an Airborne Hyperspectral Imagery (A-HSI) and tested a part of Han River water quality (especially suspended solid) estimation with available in-situ data. The estimation of water quality was processed two methods. One is using observation data as downwelling radiance to water surface and as scattering and reflectance into water body. Other is linear regression analysis with water quality in-situ measurement and upwelling data as at-sensor radiance (or reflectance). Both methods drive meaningful results of RS estimation. However it has more effects on the auxiliary dataset as water quality in-situ measurement and water body scattering measurement. The test processed a part of Han River located Paldang-dam downstream. We applied linear regression analysis with AISA eagle hyperspectral sensor data and water quality measurement in-situ data. The result of linear regression for a meaningful band combination shows $-24.847+0.013L_{560}$ as 560 nm in radiance (L) with 0.985 R-square. To comparison with Multispectral Imagery (MSI) case, we make simulated Landsat TM by spectral resampling. The regression using MSI shows -55.932 + 33.881 (TM1/TM3) as radiance with 0.968 R-square. Suspended Solid (SS) concentration was about 3.75 mg/l at in-situ data and estimated SS concentration by A-HIS was about 3.65 mg/l, and about 5.85mg/l with MSI with same location. It shows overestimation trends case of estimating using MSI. In order to upgrade value for practical use and to estimate more precisely, it needs that minimizing sun glint effect into whole image, constructing elaborate flight plan considering solar altitude angle, and making good pre-processing and calibration system. We found some limitations and restrictions such as precise atmospheric correction, sample count of water quality measurement, retrieve spectral bands into A-HSI, adequate linear regression model selection, and quantitative calibration/validation method through the literature review and test adopted general methods.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.109-110
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• 2013

도심화는 녹지의 제거로 인해 야기괴는 다양한 환경문제를 일으킨다. 이러한 도심화 현상은 종종 원격탐사영상을 이용하여 평가되는데, 대표적으로 정규식생지수와 지표온도를 통해 이루어진다. 그러나 식생지수는 두 개의 광학채널을 이용하기 때문에 식생이 아닌 도심지에서 식생과 같은 지수를 나타나는 오류가 발생한다. 이에 본 연구에서는 도심지에서 정규식생지수와 지표온도가 도심지 표면의 물질의 혼합에 따라 어떻게 변화하는지 관찰하였다.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.579-581
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• 2003

Recently, the environment of the Ariake Sea, Japan has changed drastically. In this study, the result of sea survey, synchronizing the passage time of the Landsat in August 2002 was collated with the satellite data to develop the evaluating equation for transparency and sea surface temperature (SST). By Applying these equations to 5 satellite images of the same season, the transparency and SST in summer of 1985, 1991, 1996 and 2000 is estimated. Consequently, the transparency had increased until 2000 and then decreased in 2002. The SST, on the other hand, shows no remarkable trend.

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.197-205
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• 2004

Wetlands are lands with a mixture of water, herbaceous or woody vegetation and wet soil. And linear spectral mixture analysis (LSMA) is one of the most often used methods in handling the spectral mixture problem. This study aims to test LSMA is an enhanced routine for classification of wetland land-covers in Paldang reservoir and vicinity (paldang Reservoir) using Landsat TM and ETM+ imagery. In the LSMA process, reference endmembers were driven from scatter-plots of Landsat bands 3, 4 and 5, and a series of endmember models were developed based on green vegetation (GV), soil and water endmembers which are the main indicators of wetlands. To consider phenological characteristics of Paldang Reservoir, a soil endmember was subdivided into bright and dark soil endmembers in spring and a green vegetation (GV) endmember was subdivided into GV tree and GV herbaceous endmembers in fall. We found that LSMA fractions improved the classification accuracy of the wetland land-cover. Four endmember models provided better GV and soil discrimination and the root mean squared (RMS) errors were 0.011 and 0.0039, in spring and fall respectively. Phenologically, a fall image is more appropriate to classify wetland land-cover than spring's. The classification result using 4 endmember fractions of a fall image reached 85.2 and 74.2 percent of the producer's and user's accuracy respectively. This study shows that this routine will be an useful tool for identifying and monitoring the status of wetlands in Paldang Reservoir.

• Journal of Korea Water Resources Association
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• v.36 no.6
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• pp.1059-1068
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• 2003

Geographic Information System (GIS) combined with Universal Soil Loss Equation (USLE) was used to estimate the soil erosion of Asan Bay experiment watershed in Korea. Spatial data for each USLE factors were obtained from the Landsat-5 TM remote sensing images and 1/25,000 scale digital contour maps. Sediment yield to Asan Bay was estimated by the sediment delivery ratio and trap efficiency. The estimated sediment yield was compared with observed on the Asan and Sapgyo estuary sub-watershed within Asan Bay experimental watershed for the period from 1981 to 2000. The calculated total annual sediment yields from Asan and Sapgyo estuary sub-watershed to Asan Bay were 5,665tonnes/yr and 6,766tonnes/yr, respectively. The measured sediment yields were 12,937tonnes/yr and 12,395tonnes/yr, respectively on an average.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.519-524
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• 1999

In order to assess the estimtions of CN for a small agricultural watershed using the satellite image, TM image from Landsat-5 was classsified by MLC. CN for each pixels in the image was estimaed using the results. For the estimation enhancing , it was tried that each land use area in a pixel was estimated by the mixel assumption and the averaged CN by weight areas. Those resutls were applied for the actual hydrologic analyses were highly concerned with the observed runoff discharge and more enhanced on the mixel assumption.