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RISS 인기검색어
- 검색키워드
- 저자 : Quackenbush, Lindi J. (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
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Manqi Li,Jungho Im,Quackenbush, Lindi J.,Tao Liu IEEE 2014 IEEE journal of selected topics in applied earth o Vol.7 No.7
<P>In response to the need for a better understanding of biosphere-atmosphere interactions as well as carbon cycles, there is a high demand for monitoring key forest parameters such as biomass and carbon stock. These monitoring tasks provide insight into relevant biogeochemical processes as well as anthropogenic impacts on the environment. Recent advances in remote sensing techniques such as Light Detection and Ranging (LiDAR) enable scientists to nondestructively identify structural and biophysical characteristics of forests. This study quantified forest biomass and carbon stock at the plot level from small-footprint full-waveform LiDAR data collected over a montane mixed forest in September 2011, using seven modeling methods: ordinary least squares, generalized additive model, Cubist, bagging, random forest, boosted regression trees, and support vector regression (SVR). Results showed that higher percentiles of canopy height and intensity made significant contributions to the predictions, while other explanatory variables related to canopy geometric volume, structure, and canopy coverage were generally not as important. Boosted regression trees provided the highest accuracy for model calibration, whereas SVR and ordinary least squares performed slightly better than the other models in model validation. In this study, the simple ordinary least squares approach performed just as well as any advanced machine learning method.</P>
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Yoo, Cheolhee,Im, Jungho,Park, Seonyoung,Quackenbush, Lindi J. Elsevier 2018 ISPRS journal of photogrammetry and remote sensing Vol.137 No.-
<P><B>Abstract</B></P> <P>Urban air temperature is considered a significant variable for a variety of urban issues, and analyzing the spatial patterns of air temperature is important for urban planning and management. However, insufficient weather stations limit accurate spatial representation of temperature within a heterogeneous city. This study used a random forest machine learning approach to estimate daily maximum and minimum air temperatures (T<SUB>max</SUB> and T<SUB>min</SUB>) for two megacities with different climate characteristics: Los Angeles, USA, and Seoul, South Korea. This study used eight time-series land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS), with seven auxiliary variables: elevation, solar radiation, normalized difference vegetation index, latitude, longitude, aspect, and the percentage of impervious area. We found different relationships between the eight time-series LSTs with T<SUB>max</SUB>/T<SUB>min</SUB> for the two cities, and designed eight schemes with different input LST variables. The schemes were evaluated using the coefficient of determination (R<SUP>2</SUP>) and Root Mean Square Error (RMSE) from 10-fold cross-validation. The best schemes produced R<SUP>2</SUP> of 0.850 and 0.777 and RMSE of 1.7 °C and 1.2 °C for T<SUB>max</SUB> and T<SUB>min</SUB> in Los Angeles, and R<SUP>2</SUP> of 0.728 and 0.767 and RMSE of 1.1 °C and 1.2 °C for T<SUB>max</SUB> and T<SUB>min</SUB> in Seoul, respectively. LSTs obtained the day before were crucial for estimating daily urban air temperature. Estimated air temperature patterns showed that T<SUB>max</SUB> was highly dependent on the geographic factors (e.g., sea breeze, mountains) of the two cities, while T<SUB>min</SUB> showed marginally distinct temperature differences between built-up and vegetated areas in the two cities.</P>
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The MODIS ice surface temperature product as an indicator of sea ice minimum over the Arctic Ocean
Kang, D.,Im, J.,Lee, M.I.,Quackenbush, L.J. American Elsevier Pub. Co 2014 Remote sensing of environment Vol.152 No.-
This study examines the relationship between sea ice extent and ice surface temperature (IST) between 2000 and 2013 using daily IST products from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The empirical prediction of September sea ice extent using its trend and two climate variables-IST and wind vorticity-exhibits a statistically significant relationship (R=0.97) with a time lag, where IST maximum in summer (June-July) corresponds to the sea ice extent minimum in September. This suggests that IST may serve as an indicator of the basin-wide heat energy accumulated in the Arctic by solar radiation and large-scale atmospheric heat transport from lower latitudes. The process of inducing higher IST is related to the change of atmospheric circulation over the Arctic. Averaged IST and 850hPa relative vorticity of the polar region show a significant negative correlation (-0.57) in boreal summer (June-August), suggesting a weakening of the polar vortex in the case of warmer-than-normal IST conditions. Weakening of the polar vortex is accompanied by above-normal surface pressure. Minimum sea ice extent in September was successfully predicted by both multiple linear regression and machine learning support vector regression using preceding summer IST and wind vorticity along with the trend of sea ice extent (R<SUP>2</SUP>~0.95, cross validation RMSE of 3-4x10<SUP>5</SUP>km<SUP>2</SUP>, and relative cross validation RMSE of 5-8%).
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Kim, Miae,Lee, Junghee,Han, Daehyeon,Shin, Minso,Im, Jungho,Lee, Junghye,Quackenbush, Lindi J.,Gu, Zhu IEEE 2018 IEEE journal of selected topics in applied earth o Vol.11 No.12
<P>Researchers constantly seek more efficient detection techniques to better utilize enhanced image resolution in accurately detecting and monitoring land cover. Recently, convolutional neural networks (CNNs) have shown high performances comparable to or even better than widely used and adopted machine learning techniques. The aim of this study is to investigate the application of CNNs for land cover classification by using two-dimensional (2-D) spectral curve graphs from multispectral satellite images. The land cover classification was conducted in Concord, New Hampshire, USA, and South Korea by using multispectral images acquired from 30-m Landsat-8 and 500-m Geostationary Ocean Color Imager images. For the construction of input data specific to CNNs, two seasons (winter and summer) of multispectral bands were transformed into 2-D spectral curve graphs for each class. Land cover classification results of CNNs were compared with the results of support vector machines (SVMs) and random forest (RFs). The CNNs model showed higher performance than RFs and SVMs in both study sites. The examination of land cover classification maps demonstrates a good agreement with reference maps, Google Earth images, and existing global scale land cover map, especially for croplands. Using the spectral curve graph could incorporate the phenological cycles on classifying the land cover types. This study shows that the use of a new transformation of spectral bands into a 2-D form for application in CNNs can improve land cover classification performance.</P>
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