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您的检索式：作者名="Christopher O.Justice"
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| 1 | 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 |
| 2 | Spatial and temporal intercomparison of four global burned area products显示文摘We characterize the agreement and disagreement of four publically available burned products(Fire CCI,Copernicus Burnt Area,MODIS MCD45A1,and MODIS MCD64A1)at a finer spatial and temporal scale than previous assessments using a grid of three-dimensional cells defined both in space and in time.Our analysis,conducted using seven years of data(2005–2011),shows that estimates of burned area vary greatly between products in terms of total area burned,the location of burning,and the timing of the burning.We use regional and monthly units for analysis to provide insight into the variation between products that can be lost when considering products yearly and/or globally.Comparison with independent,contemporaneous MODIS active fire observations provides one indication of which products most reasonably capture the burning regime.Our results have implications for the use of global burned area products in fire ecology,management and emissions applications. | Michael L.Humber Luigi Boschetti Louis Giglio Christopher O.Justice | 2019 | International Journal of Digital Earth2019，12，4： | 0 |
| 3 | Agricultural growing season calendars derived from MODIS surface reflectance显示文摘In order to secure the necessary image acquisitions for global agricultural monitoring applications,we must first articulate Earth observation(EO)requirements for diverse agricultural landscapes and cropping systems.Crucial to this task is the identification of agricultural growing season timing at a meaningful spatial scale,so as to better define the necessary periods of image acquisition.To this end,10 years of MODIS Terra Surface Reflectance imagery have been used to determine phenological transition dates including start of season,peak period,and end of season at 0.5°globally.This is the first set of global,satellite-derived,cropland-specific calendar dates for major field crops within a 0.5°,herein called agricultural growing season calendars Preliminary comparison against ground-based crop-specific calendars is performed,highlighting the utility of this approach for articulating growing season timing and its interannual and within-region variability.This research provides critical inputs for defining the EO requirements for the Global Agricultural Monitoring initiative(GEOGLAM),an effort by the Group on Earth Observations(GEO)to synergize existing national and regional observation systems for improved agricultural production and food security monitoring. | Alyssa K.Whitcraft Inbal Becker-Reshef Christopher O.Justice | 2015 | International Journal of Digital Earth2015，8，3： | 0 |
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