최근 3차원 영상 데이터 활용 기술이 주목받으며 레이저 스캐너, 깊이 카메라와 같은 장비를 활용하여 작물의 생육을 측정하려는 연구가 시도되고 있다. 작물의 생육 특성을 측정할 때 3차원 영상 데이터를 활용한다면 평면 데이터에서 측정하지 못한 구조와 형태 정보를 이용할 수 있는 장점이 있다. 본 연구에서는 콩의 생육 특성을 3차원 영상 데이터를 활용하여 추정하였다. 깊이 카메라를 이용하여 콩의 개화시(R1), 착협기(R3), 종실비대기(R5) 에 촬영하고 3차원 데이터로 개체의 초장과 엽면적을 추정하고 실측 값과 비교하였다. 초장 추정을 위해 평면에 투영된 개체의 무게 중심을 이용하여 원줄기의 x, y 좌표 위치를 지정하였는데 눈으로 보고 지정한 원줄기의 위치와 무게 중심 점의 x, y 좌표 위치는 높은 결정 계수를 보였다. 초장 추정의 경우 콩의 구조와 형태가 발달함에 따라 3차원 영상에서 지면으로부터 개체 상단 지점 간 거리를 이용하는 방법은 실측과 추정 값간 오차가 컸다. 엽면적 추정을 위해서 3차원 위치 값을 갖는 개체 표면 점들을 높이에 따라 분할하고 각 높이 구간의 면적을 계산하였다. 3차원 데이터 병합 과정에서 늘어난 점 개수로 인해 각 높이 구간에서 계산된 면적이 증가하였기 때문에 추정 값은 과대평가되었다. 향후 3차원 영상을 이용한 보다 정밀한 생육 조사를 위해서는 작물 고유의 생육변수 특성을 고려한 데이터 전처리 과정과 분석 방법 개발이 필요할 것으로 사료된다.

비점원 오염은 점원 오염처럼 고정된 배출원이 존재하는 것이 아니라 불특정 장소에서 오염물질이 광역에 걸쳐 배출되는 것으로서, 대표적으로 지면피복 그 자체가 오염원으로 작용할 수 있다. 최근 우리나라 일부 지역에서는 여러 가지 원인으로 지면피복 변화가 급격히 발생하였는데, 이는 위성원격탐사를 통해 연속적 시공간에서 효과적으로 파악 가능하다. 본 연구에서는 NDVI (Normalized Difference Vegetation Index) 위성자료에 공간통계기법을 적용하여 우리나라 지면피복변화의 핫스팟을 추출하고, 실제 오염측정치를 이용하여 비점원 오염 특성을 지면피복변화와 관련하여 살펴보았다. 2003년과 2011년의 비교에서는 4군데의 지면피복변화 핫스팟이 탐지되었는데, 이 중 오염부하량 자료가 충분히 존재하는 새만금 핫스팟과 화성호 핫스팟 유역에 대한 분석 결과, 비점원 오염의 유출이 집중되는 하구 부분에서 오염부하량의 상당한 증가가 발견되었고, 특히 화성호 핫스팟의 경우 농지 증가의 영향이 컸던 것으로 나타났다. 이처럼 지면피복변화의 핫스팟 추출을 통해 국토변화를 모니터링하고 핫스팟 유역의 비점원 오염부하량이 지역적으로 어떠한 특성을 보이는지 분석하는 것은, 국토 보전 및 개발에 있어 유용한 참고자료가 될 것으로 사료된다.

The COMS (Communication, Ocean and Meteorological Satellite) has been used in numerical weather prediction and meteorological monitoring over East Asia and Oceania since it has been launched in 2010. For more active utilization in climate research, the COMS level 3 products should be available in appropriate spatial and temporal resolutions. We compared different methods to generate monthly sea surface temperature (SST) products from the COMS time-series data. We employed three techniques for aggregating the time series, which are arithmetic mean, timeslot average, and moving average, and also used mean ensemble of them. Each level 3 dataset around South Korea was compared with monthly SST product from the Moderate Resolution Imaging Spectroradiometer (MODIS) of Aqua satellite during April 2011-March 2014. The timeslot average showed better root mean squared difference (RMSD) during the initial operational period of the COMS, when the retrieved values could be somewhat unstable. Daytime aggregations were derived more accurately by using the arithmetic mean or moving average, and the accuracy of nighttime aggregation was improved by the mean ensemble. Also, the timeslot average presented reasonable results particularly for coastlines where the standard deviation and missing value ratio were greater than normal. Because an optimal aggregation technique was variable depending on spatial and temporal conditions, we should be careful in selecting appropriate method for generation of the COMS level 3 products according to research objectives.

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.

In this study, we produced satellite-based drought indices such as NDVI(normalized difference vegetation index), NDWI(normalized difference water index), and NDDI(normalized difference drought index) and extracted time-series principal components from the drought indices using EOF(empirical orthogonal function) method to examine their relationships with climatic variables. We found that the first principal components of the drought indices for Sonid Right Banner, Inner Mongolia were dominant in cold seasons and were closely related to low temperature, little rainfall, and high surface albedo. The satellite-based drought indices and their EOF analyses can be utilized for the studies of cold-season drought and warm-season drought as well.

Wildfires are recently increasing in frequencies and intensities worldwide. Hence, reliable and continuous monitoring of sudden occurrences of wildfire is demanded, and geostationary meteorological satellites are an alternative to detection of wildfire in large areas. We currently have two geostationary meteorological satellites for the Korean Peninsula: the Korean COMS(Communication, Ocean and Meteorological Satellite) and the Japanese MTSAT(Multifunctional Transport Satellite). However, neither of them provides satellite products for wildfire detection although the MODIS(Moderate Resolution Imaging Spectroradiometer) on polar-orbiting satellites has been operated for wildfire detection for a decade. In this study, we applied the MODIS algorithm for wildfire detection to the COMS and the MTSAT in order to evaluate the detection performances for South Korea. Both satellites were successful in detection of big fires, but the COMS was better in detecting small fires because of its higher saturation temperature of 350 K approx. at 4-μm band. The comparison results will be informative for an emergency plan of COMS and for the preparation of next-generation geostationary meteorological satellite.

The CMDPS(COMS Meteorological Data Processing System) provides 16 meteorological products by processing satellite data obtained from 1 visible and 4 infrared channels. It is expected that the COMS(Communication, Ocean and Meteorological Satellite) products will contribute to numerical weather prediction and climate change monitoring. For more active utilization of the products, we first need a scheme for the quality management based on outlier detection. This paper describes a method for detecting spatial and temporal outliers from the COMS products using Moran scatterplot and wavelet transform. The applicability of our method was made sure through the tests for seven products such as sea surface temperature, land surface temperature, aerosol optical depth, total precipitable water, upper tropospheric humidity, cloud top height, and cloud top temperature. Their quality was estimated very good in terms of our spatio-temporal statistical approach. For future work, we need more comprehensive study on the outlier detection, particularity focusing on the threshold setting in accordance with physical characteristics of each product.