Analysis of Multi-Temporal ERS-1 SAR Data of Subarctic Tundra and Forest in the Northern Hudson Bay Lowland and Implications for Climate Studies

1999 ◽  
Vol 25 (1) ◽  
pp. 21-33 ◽  
Author(s):  
C.K. Duguay ◽  
W.R. Rouse ◽  
P.M. Lafleur ◽  
L.D. Boudreau ◽  
Y. Crevier ◽  
...  
2016 ◽  
Author(s):  
Rebecca Snyder ◽  
◽  
Dorothy Peteet ◽  
Jonathan Nichols ◽  
Sarah Finkelstein ◽  
...  

1998 ◽  
Vol 44 (146) ◽  
pp. 42-53 ◽  
Author(s):  
K. C. Partington

AbstractGlacier facies from the Greenland ice sheet and the Wrangell-St Elias Mountains, Alaska, are analyzed using multi-temporal synthetic aperture radar (SAR) data from the European Space Agency ERS-1 satellite. Distinct zones and facies are visible in multi-temporal SAR data, including the dry-snow facies, the combined percolation and wet-snow facies, the ice facies, transient melt areas and moraine. In Greenland and south-central Alaska, very similar multi-temporal signatures are evident for the same facies, although these facies are found at lower altitude in West Greenland where the equilibrium line appears to be found at sea level at 71°30?N during the year analyzed (1992-93), probably because of the cooling effect of the eruption of Mount Pinatubo. In Greenland, both the percolation and dry-snow facies are excellent distributed targets for sensor calibration, with backscatter coefficients stable to within 0.2 dB. However, the percolation facies near the top of Mount Wrangell are more complex and less easily delineated than in Greenland, and at high altitude the glacier facies have a multi-temporal signature which depends sensitively on slope orientation.


2019 ◽  
Vol 11 (13) ◽  
pp. 1619 ◽  
Author(s):  
Zhou Ya’nan ◽  
Luo Jiancheng ◽  
Feng Li ◽  
Zhou Xiaocheng

Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification.


2018 ◽  
Vol 44 (5) ◽  
pp. 447-461 ◽  
Author(s):  
Jujie Wei ◽  
Yonghong Zhang ◽  
Hong’an Wu ◽  
Bin Cui

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