• Title/Summary/Keyword: Sensing data

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Effects of spatial resolution on digital image to detect pine trees damaged by pine wilt disease

  • Lee, Seung-Ho;Cho, Hyun-Kook
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.260-263
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    • 2005
  • This study was carried out to investigate the effects of spatial resolutions on digital image for detecting pine trees damaged by pine wilt disease. Color infrared images taken from PKNU-3 multispectral airborne photographing system with a spatial resolution of 50cm was used as a basic data. Further test images with spatial resolutions of 1m, 2m and 4m were made from the basic data to test the detecting capacity on each spatial resolution. The test was performed with visual interpretation both on mono and stereo modus and compared with field surveying data. It can be conclude that it needs less than 1m spational resolutions or 1m spatial resolutions with stereo pair in order to detect pine trees damaged by pine wilt disease.

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COMBINED ACTIVE AND PASSIVE REMOTE SENSING OF HURRICANE OCEAN WINDS

  • Yueh, Simon H.
    • Proceedings of the KSRS Conference
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    • v.1
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    • pp.142-145
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    • 2006
  • The synergism of active and passive microwave techniques for hurricane ocean wind remote sensing is explored. We performed the analysis of Windsat data for Atlantic hurricanes in 2003-2005. The polarimetric third Stokes parameter observations from the Windsat 10, 18 and 37 GHz channels were collocated with the ocean surface winds from the Holland wind model, the NOAA HWind wind vectors and the Global Data Assimilation System (GDAS) operated by the National Center for Environmental Prediction (NCEP). The collocated data were binned as a function of wind speed and wind direction, and were expanded by sinusoidal series of the relative azimuth angles between wind and observation directions. The coefficients of the sinusoidal series, corrected for atmospheric attenuation, have been used to develop an empirical geophysical model function (GMF). The Windsat GMF for extreme high wind compares very well with the aircraft radiometer and radar measurements.

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Study on Application of Neural Network for Unsupervised Training of Remote Sensing Data (신경망을 이용한 원격탐사자료의 군집화 기법 연구)

  • 김광은;이태섭;채효석
    • Spatial Information Research
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    • v.2 no.2
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    • pp.175-188
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    • 1994
  • A competitive learning network was proposed as unsupervised training method of remote sensing data, Its performance and computational re¬quirements were compared with conventional clustering techniques such as Se¬quential and K - Means. An airborne remote sensing data set was used to study the performance of these classifiers. The proposed algorithm required a little more computational time than the conventional techniques. However, the perform¬ance of competitive learning network algorithm was found to be slightly more than those of Sequential and K - Means clustering techniques.

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A Study on the Implementation of Mobile Web Monitoring for Sensing Data Using Bluetooth Transmission (센싱 데이터의 블루투스 전송 및 모바일 웹 모니터링 구현에 관한 연구)

  • Hwang, In-Ki;Roh, Jae-sung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2012.10a
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    • pp.651-652
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    • 2012
  • Wireless monitoring and hybrid mobile web have become an emerging area of research in recent year. This paper deals with the design and implementation of mobile web page for sensing data monitoring. The mobile web monitoring system for sensing data transmission is implemented by 8-bit AVR microcontroller and bluetooth communication module.

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Analysis of Sea Surface Wind over the Complex Coastal Area Using SAR images (SAR영상에 의한 복잡해안지역 해상풍 분석)

  • Hwang, Hyo-Jung;Kim, Hyun-Goo
    • 한국신재생에너지학회:학술대회논문집
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    • 2009.11a
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    • pp.482-483
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    • 2009
  • This paper is foundation paper about national wind map verification using remote sensing, based on analysis of comparison between numerical simulation and remote sensing on complex coastal area of regional coast. As a result analysis using NCAR/NCEP, wind direction of numerical simulation and remote sensing is same. but, wind direction of some case is showed different. Such as this result, if it would be used without verification of analyzed data, present ability of occurring lots of error, and it will be verified based on using survey data or atmospheric data.

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Study on Response of Ecosystem to the East Asian Monsoon in Eastern China Using LAI Data Derived from Remote Sensing Information

  • Zhang, Jiahua;Yao, Fengmei;Fu, Congbin
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.1298-1300
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    • 2003
  • Based on the Leaf Area Index (LAI) data derived from remote sensing information and eco-climate data, the responses of regional ecosystem variations in seasonal and interannual scales to the East Asian monsoon are studied in this paper. It is found that the vegetation ecosystems of eastern China are remarkably correlated with the East Asian monsoon in seasonal and interannual scales. In the seasonal timescale, the obvious variations of the vegetation ecosystems occur with the development of the East Asian monsoon from the south in the spring to the north in the autumn. In the interannual scale, high LAI appears in the strong East Asian monsoon year, whereas low LAI is related to the weak East Asian monsoon year. These further lead to the characteristic of 'onsoon-driven ecosystem' in the eastern China monsoon region, which can be revealed by LAI.

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Pasture estimating with climate change over Mongolia using climate and NOAA/NDVI data

  • Erdenetuya, M.;Khudulmur, S.;Bolortsetseg, B.;Natsagdorj, L.;Batima, P.
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.120-122
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    • 2003
  • Geographical position and associated climatic influences can be a negative environmental condition that affects sustainable use of land resources, especially pastoral livestock production. Vegetation condition of the country is sensitively changes upon climate changes and human impacts. Within last 60 years data the annual air temperature has increased in 1.66 degrees in average and the total precipitation amount had almost no change. The main goal of this work is to relate climate change within last 20 years with pasture condition, estimated by NOAA/NDVI data set.

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Vegetation Classification Using Seasonal Variation MODIS Data

  • Choi, Hyun-Ah;Lee, Woo-Kyun;Son, Yo-Whan;Kojima, Toshiharu;Muraoka, Hiroyuki
    • Korean Journal of Remote Sensing
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    • v.26 no.6
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    • pp.665-673
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    • 2010
  • The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.

USING TRMM SATELLITE C BAND DATA TO RETRIEVE SOIL MOISTURE ON THE TffiETAN PLATEAU

  • Chang Tzu-Yin;Liou Yuei-An
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.737-740
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    • 2005
  • Soil moisture, through its dominance in the exchange of energy and moisture between the land and atmosphere, plays a crucial role in influencing atmospheric circulation. To identify the crucial role, it is a common agreement that knowledge of land surface processes and development of remote sensing techniques are of great important scientific issues. This research uses TRMM satellite C band (10.65 GHz) data to retrieve soil moisture on the Tibetan Plateau in Mainland China. Two retrieval schemes that are implemented include the t-(J) model and the R model. The latter one is developed based on a land surface process and radiobrightness (R) model for bare soil and vegetated terrain. Compared with the in situ ground measurements, the soil moisture retrieved from the R model and the t-(J) model with vegetation information obviously appear more accurate than that derived from bare soil model. Retrieved soil moisture contents from the two inversion models, R model and t-(J) model, have a similar trend, but the former appears to be superior in terms of correlation coefficient and bias compared with in situ data. In the future, we will apply the R model with the TRMM 10.65 GHz brightness temperature to monitor long-term soil moisture variation over Tibet Plateau.

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Analysis on Wind Profile Characteristics in a Sublayer of Atmospheric Boundary Layer over a Semi-Complex Terrain - LIDAR Remote Sensing Campaign at Pohang Accelerator Laboratory (준복잡지형 대기경계층 저층 풍속분포 특성분석 - 포항가속기 라이다 원격탐사 캠페인을 중심으로)

  • Kim, Hyun-Goo
    • Journal of Environmental Science International
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    • v.21 no.2
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    • pp.145-152
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    • 2012
  • The mean wind speed and turbulence intensity profiles in the atmospheric boundary layer were extracted from a LIDAR remote sensing campaign in order to apply for CFD validation. After considering the semi-steady state field data requirements to be used for CFD validation, a neutral atmosphere campaign period, in which the main wind direction and the power-law exponent of the wind profile were constantly maintained, was chosen. The campaign site at the Pohang Accelerator Laboratory, surrounded by 40~50m high hills, with an apartment district spread beyond the hills, is to be classified as a semi-complex terrain. Nevertheless, wind speed profiles measured up to 100m above the ground fitted well into a theoretical-experimental logarithmic-law equation. The LIDAR remote-sensing data of the sub-layer of the atmospheric boundary layer has been proven to be superior to the data obtained by conventional extrapolation of the wind profile with 2 or 3 anemometer measurements.