The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth’s land cover at Landsat resolutions of 30 m.In this article,we describe the methods to cr...The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth’s land cover at Landsat resolutions of 30 m.In this article,we describe the methods to create global products of forest cover and cover change at Landsat resolutions.Nevertheless,there are many challenges in ensuring the creation of high-quality products.And we propose various ways in which the challenges can be overcome.Among the challenges are the need for atmospheric correction,incorrect calibration coefficients in some of the data-sets,the different phenologies between compila-tions,the need for terrain correction,the lack of consistent reference data for training and accuracy assessment,and the need for highly automated character-ization and change detection.We propose and evaluate the creation and use of surface reflectance products,improved selection of scenes to reduce phenological differences,terrain illumination correction,automated training selection,and the use of information extraction procedures robust to errors in training data along with several other issues.At several stages we use Moderate Resolution Spectro-radiometer data and products to assist our analysis.A global working prototype product of forest cover and forest cover change is included.展开更多
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 m...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.展开更多
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 us...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.展开更多
基金support from the NASATerrestrial Ecology Program which led to the creation of LEDAPS on which much of this work is based.We acknowledge the help of two people in particular from USGS EROS:Gyanesh Chander helped to identify the GLS 1990 images that have most recent USGS calibration coefficients(?50%of the GLS 1990 data-set).Rachel Headley helped us obtain the GLS data-sets.She also helped significantly with our reordering of the GLS 1990 images that had good calibration coefficients。
文摘The compilation of global Landsat data-sets and the ever-lowering costs of computing now make it feasible to monitor the Earth’s land cover at Landsat resolutions of 30 m.In this article,we describe the methods to create global products of forest cover and cover change at Landsat resolutions.Nevertheless,there are many challenges in ensuring the creation of high-quality products.And we propose various ways in which the challenges can be overcome.Among the challenges are the need for atmospheric correction,incorrect calibration coefficients in some of the data-sets,the different phenologies between compila-tions,the need for terrain correction,the lack of consistent reference data for training and accuracy assessment,and the need for highly automated character-ization and change detection.We propose and evaluate the creation and use of surface reflectance products,improved selection of scenes to reduce phenological differences,terrain illumination correction,automated training selection,and the use of information extraction procedures robust to errors in training data along with several other issues.At several stages we use Moderate Resolution Spectro-radiometer data and products to assist our analysis.A global working prototype product of forest cover and forest cover change is included.
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.
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.
