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您的检索式：作者名="Samuel N.Goward"
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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 | Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US显示文摘Using the NASA Earth Exchange platform,the North American Forest Dynamics(NAFD)project mapped forest history wall-to-wall,annually for the contiguous US(1986–2010)using the Vegetation Change Tracker algorithm.As with any effort to identify real changes in remotely sensed time-series,data gaps,shifts in seasonality,misregistration,inconsistent radiometry and cloud contamination can be sources of error.We discuss the NAFD image selection and processing stream(NISPS)that was designed to minimize these sources of error.The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration,unreliability of the pre-2009 L5 cloud cover assessments algorithm,missing growing-season imagery and paucity of clear views.Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study.Over 150,000+Landsat images were considered for the NAFD project.Optimally,one high quality cloud-free image in each year or a total of 12,152 images would be used.However,to accommodate data gaps and cloud/shadow contamination 23,338 images were needed.In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products. | Karen Schleeweis Samuel N.Goward Chengquan Huang John L.Dwyer Jennifer L.Dungan Mary A.Lindsey Andrew Michaelis Khaldoun Rishmawi Jeffery G.Masek | 2016 | International Journal of Digital Earth2016，9，10： | 0 |
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