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您的检索式：作者名="Eric F.Vermote"
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| 1 | Development of time series stacks of Landsat images for reconstructing forest disturbance history显示文摘Forest dynamics is highly relevant to a broad range of earth science studies,many of which have geographic coverage ranging from regional to global scales.While the temporally dense Landsat acquisitions available in many regions provide a unique opportunity for understanding forest disturbance history dating back to 1972,large quantities of Landsat images will need to be analysed for studies at regional to global scales.This will not only require effective change detection algorithms,but also highly automated,high level preprocessing capabilities to produce images with subpixel geolocation accuracies and best achievable radiometric consistency,a status called imagery-ready-to-use(IRU).This paper describes a streamlined approach for producing IRU quality Landsat time series stacks(LTSS).This approach consists of an image selection protocol,high level preprocessing algorithms and IRU quality verification procedures.The high level preprocessing algorithms include updated radiometric calibration and atmospheric correction for calculating surface reflectance and precision registration and orthorectification routines for improving geolocation accuracy.These automated routines have been implemented in the Landsat Ecosystem Disturbance Adaptive System(LEDAPS)designed for processing large quantities of Landsat images.Some characteristics of the LTSS developed using this approach are discussed. | Chengquan Huang Samuel N.Goward Jeffrey G.Masek Feng Gao Eric F.Vermote Nancy Thomas Karen Schleeweis Robert E.Kennedy Zhiliang Zhu Jeffery C.Eidenshink John R.G.Townshend | 2009 | International Journal of Digital Earth2009，2，3： | 5 |
| 2 | Automatic sub-pixel co-registration of Landsat-8 Operational Land Imager and Sentinel-2A Multi-Spectral Instrument images using phase correlation and machine learning based mapping显示文摘This study investigates misregistration issues between Landsat-8/Operational Land Imager and Sentinel-2A/Multi-Spectral Instrument at 30 m resolution,and between multi-temporal Sentinel-2A images at 10 m resolution using a phase-correlation approach and multiple transformation functions.Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed.Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart.Overall,misregistration of up to 1.6 pixels at 30 m resolution between Landsat-8 and Sentinel-2A images,and 1.2 pixels and 2.8 pixels at 10 m resolution between multi-temporal Sentinel-2A images from the same and different orbits,respectively,were observed.The non-linear random forest regression used for constructing the mapping function showed best results in terms of root mean square error(RMSE),yielding an average RMSE error of 0.07±0.02 pixels at 30 m resolution,and 0.09±0.05 and 0.15±0.06 pixels at 10 m resolution for the same and adjacent Sentinel-2A orbits,respectively,for multiple tiles and multiple conditions.A simpler 1st order polynomial function(affine transformation)yielded RMSE of 0.08±0.02 pixels at 30 m resolution and 0.12±0.06(same Sentinel-2A orbits)and 0.20±0.09(adjacent orbits)pixels at 10 m resolution. | Sergii Skakun Jean-Claude Roger Eric F.Vermote Jeffrey G.Masek Christopher O.Justice | 2017 | International Journal of Digital Earth2017，10，12： | 0 |
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