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위성영상과 기후자료를 이용한 미중서부 옥수수와 콩의 수확량 추정
김나리,조재일,시바사키료스케,이양원 건국대학교 기후연구소 2014 기후연구 Vol.9 No.4
This paper described the estimation of corn and soybeans yields of four states in the US Midwest using time-series satellite imagery and climate dataset between 2001 and 2012. We first constructed a database for (1) satellite imagery acquired from Terra MODIS (Moderate Resolution Imaging Spectroradiometer) including NDVI (Normalized Di°erence Vegetation Index), EVI (Enhanced Vegetation Index), LAI (Leaf Area Index), FPAR (Fraction of Photosynthetically Active Radiation), and GPP (Gross Primary Productivity), (2) climate dataset created by PRISM (Parameter-Elevation Regressions on Independent Slopes Model) such as precipitation and mean temperature, and (3) US yield statistics of corn and soybeans. ˜en we built OLS (Ordinary Least Squares) regression models for corn and soybeans yields between 2001 and 2010 after correlation analyses and multicollinearity tests. These regression models were used in estimating the yields of 2011 and 2012. Comparisons with the US yield statistics showed the RMSEs (Root Mean Squared Errors) of 0.892 ton/ha and 1.095 ton/ha for corn yields in 2011 and 2012 respectively, and those of 0.320 ton/ha and 0.391 ton/ha for soybeans yields. ˜is result can be thought of as a good agreement with the in-situ statistics because the RMSEs were approximately 10% of the usual yields: 9 ton/ha for corn and 3 ton/ha for soybeans. Our approach presented a possibility for extending to more advanced statistical modeling of crop yields using satellite imagery and climate dataset.
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3차원 건물지도와 전파도달모형을 이용한 도심부 GPS의 위치정확도 모의
이양원,시바사키료스케 한국지도학회 2013 한국지도학회지 Vol.13 No.1
현재의 내비게이션 시스템은 대부분 미국의 GPS(Global Positioning System)로부터 전파를 수신하여 측위를 수행하고 있으며, 수신기 알고리듬과 안테나 성능의 발전으로 인해 위치정확도가 지속적으로 향상되고 있다. 그러나 도심부 빌딩숲에서는 GNSS(Global Navigation Satellite System) 전파가 고층건물에 가려져 도달하지 않거나 반사 및 회절에 의해 위치정확도가 매우 저하되기도 한다. 도심부의 GNSS 측위정확도를 현장 관측하는 데에는 많은 비용과 시간이 소요되므로, 이 연구에서는 3차원 건물지도와 전파도달모형 및 상관기모형을 이용하여 도심부 GNSS의 측위정확도를 모의하는 GPASS(GNSS Position Accuracy Simulation System)를 개발하고 그 가용성을 검증하고자 한다. 대표적 빌딩숲인 도쿄도청 부근을 대상으로 모의를 수행하고 현장 관측치와 비교한 결과, 측위율은 실측치와 4.6% 정도의 차이를 보였고 위치정확도는 실측치와 3.0% 정도의 차이를 나타냄으로써 상당히 정밀한 모의가 가능함이 확인되었다. 본 연구에서 개발한 GPASS는 3차원 건물지도를 이용하여 임의의 시공간에 대해 모의를 수행하므로, 내비게이션 서비스 품질의 시공간적 평가 및 개선을 위한 기초자료 산출에 활용될 수 있다. Current navigation systems provide position information using the satellite signals from the American GPS(Global Positioning System), and the GPS position accuracy is gradually improving by the developments of receiver algorithm and antenna performance. However, the accuracy is very limited in dense urban areas because the GNSS(Global Navigation Satellite System) signals are blocked, reflected, or diffracted by buildings. This paper describes the development and validation of GPASS(GNSS Position Accuracy Simulation System) that can simulate GNSS position accuracy for dense urban areas using 3-D building maps, signal propagation model, and the receiver correlator model. We carried out the feasibility tests for the area around the Tokyo Metropolitan Government Building famous for its skyscrapers. A good agreement was observed between our simulation results and the in-situ data: the difference of positioning rate was about 4.6%, and the difference of position accuracy was 3.0% approximately. The GPASS enables the evaluations of navigation serviceabilty on the spatial and temporal basis, which will be a useful reference for improving the service quality.
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위성영상과 기후자료를 이용한 미중서부 옥수수와 콩의 수확량 추정
김나리,조재일,시바사키료스케,이양원 건국대학교 기후연구소 2014 기후연구 Vol.9 No.4
This paper described the estimation of corn and soybeans yields of four states in the USMidwest using time-series satellite imagery and climate dataset between 2001 and 2012. We firstconstructed a database for (1) satellite imagery acquired from Terra MODIS (Moderate ResolutionImaging Spectroradiometer) including NDVI (Normalized Difference Vegetation Index), EVI (EnhancedVegetation Index), LAI (Leaf Area Index), FPAR (Fraction of Photosynthetically Active Radiation), andGPP (Gross Primary Productivity), (2) climate dataset created by PRISM (Parameter-Elevation Regressionson Independent Slopes Model) such as precipitation and mean temperature, and (3) US yield statistics ofcorn and soybeans. Then we built OLS (Ordinary Least Squares) regression models for corn and soybeansyields between 2001 and 2010 after correlation analyses and multicollinearity tests. These regressionmodels were used in estimating the yields of 2011 and 2012. Comparisons with the US yield statisticsshowed the RMSEs (Root Mean Squared Errors) of 0.892 ton/ha and 1.095 ton/ha for corn yields in 2011and 2012 respectively, and those of 0.320 ton/ha and 0.391 ton/ha for soybeans yields. This result can bethought of as a good agreement with the in-situ statistics because the RMSEs were approximately 10%of the usual yields: 9 ton/ha for corn and 3 ton/ha for soybeans. Our approach presented a possibility forextending to more advanced statistical modeling of crop yields using satellite imagery and climate dataset.
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