Based on Landsat image,the Landsat Ecosystem Disturbance Adaptive Processing System(LEDAPS)uses radiation change detection method for image processing and offers the surface reflectivity products for ecosystem carbon ...Based on Landsat image,the Landsat Ecosystem Disturbance Adaptive Processing System(LEDAPS)uses radiation change detection method for image processing and offers the surface reflectivity products for ecosystem carbon sequestration and carbon reserves.As the accumulation of massive remote sensing data especially for the Landsat image,the traditional serial LEDAPS for image processing has a long cycle that make a lot of difficulties in practical application.For this problem,this paper design a high performance parallel LEDAPS processing method based on MPI.The results not only aimed to improve the calculation speed and save computing time,but also considered the load balance between the flexibly extended computing nodes.Results show that the highest speed ratio of parallelized LEDAPS reached 7.37 when the number of MPI process is 8.It effectively improves the ability of LEDAPS to handle massive remote sensing data and reduces the forest carbon stocks calculation cycle by using the remote sensing images.展开更多
Forest losses or gains have long been recognized as critical processes modulating the carbon flux between the biosphere and the atmosphere. Timely, accurate and spatially explicit information on forest disturbance and...Forest losses or gains have long been recognized as critical processes modulating the carbon flux between the biosphere and the atmosphere. Timely, accurate and spatially explicit information on forest disturbance and recovery history is required for assessing the effectiveness of existing forest management. The major objectives of our research focused on testing the mapping efficacy of the vegetation change tracker （VCT） model over a forested area in China. We used a new version of VCT algorithm built upon the Landsat time series stacks （LTSS）. The LTSS consisted of yearly image acquisitions to map forest disturbance history from 1987 to 2011 over the Ning-Zhen Mountains, Jiangsu Province of east China. The LTSS consisted of TM and ETM＋ scenes with different projec- tions due to distinct data sources （Beijing remote sensing ground station and the USGS EROS Center）. The valida- tion results of the disturbance year maps showed that most spatial agreement measures ranged from 70 to 86 %, comparable with the VCT accuracies reported for many places in USA. Very low accuracies were identified in 1995 （38.3 %） and 1992 （56.2 %） in the current analysis. These resulted from the insensitivity of the VCT algorithm to detect low intensity disturbances and also from the mis- registration errors of the image pairs. Major forest distur- bance types existing in our study area were identified as agricultural expansion （39.8 %）, urbanization （24.9 %）, forest management practice （19.3 %）, and mining （12.8 %）. In general, there was a gradual decreasing trend in forest cover throughout this region, caused principally by China＇s economic, demographic, environmental and political policies and decisions, as well as some weather events. While VCT has largely been used to assess long term changes and trends in the USA, it has great potential for assessing landscape level change elsewhere throughout the world.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
基金Special Project of Fundamental Research Funds for Central Public Welfare Research Institutes(IFRIT201104)
文摘Based on Landsat image,the Landsat Ecosystem Disturbance Adaptive Processing System(LEDAPS)uses radiation change detection method for image processing and offers the surface reflectivity products for ecosystem carbon sequestration and carbon reserves.As the accumulation of massive remote sensing data especially for the Landsat image,the traditional serial LEDAPS for image processing has a long cycle that make a lot of difficulties in practical application.For this problem,this paper design a high performance parallel LEDAPS processing method based on MPI.The results not only aimed to improve the calculation speed and save computing time,but also considered the load balance between the flexibly extended computing nodes.Results show that the highest speed ratio of parallelized LEDAPS reached 7.37 when the number of MPI process is 8.It effectively improves the ability of LEDAPS to handle massive remote sensing data and reduces the forest carbon stocks calculation cycle by using the remote sensing images.
基金funded by the following grants:the Forestry Public Welfare Project(201304208)the‘‘948’’Project sponsored by the State Forestry Administration(SFA)of China(2014-4-25)+4 种基金the National Natural Science Foundation of China(31270587,31100414)the PAPD(Priority Academic Program Development)of Jiangsu provincial universitiesperformed while the lead author held a scholarship sponsored the CSC(China Scholarship Council)(201208320553)at the department of Geographical Sciences,University of Marylandawardee of the 2012 Youth Backbone Teachers Support Plan of Jiangsu Provincethe 2012 Youth Talents Support Plan of Nanjing Forestry University
文摘Forest losses or gains have long been recognized as critical processes modulating the carbon flux between the biosphere and the atmosphere. Timely, accurate and spatially explicit information on forest disturbance and recovery history is required for assessing the effectiveness of existing forest management. The major objectives of our research focused on testing the mapping efficacy of the vegetation change tracker （VCT） model over a forested area in China. We used a new version of VCT algorithm built upon the Landsat time series stacks （LTSS）. The LTSS consisted of yearly image acquisitions to map forest disturbance history from 1987 to 2011 over the Ning-Zhen Mountains, Jiangsu Province of east China. The LTSS consisted of TM and ETM＋ scenes with different projec- tions due to distinct data sources （Beijing remote sensing ground station and the USGS EROS Center）. The valida- tion results of the disturbance year maps showed that most spatial agreement measures ranged from 70 to 86 %, comparable with the VCT accuracies reported for many places in USA. Very low accuracies were identified in 1995 （38.3 %） and 1992 （56.2 %） in the current analysis. These resulted from the insensitivity of the VCT algorithm to detect low intensity disturbances and also from the mis- registration errors of the image pairs. Major forest distur- bance types existing in our study area were identified as agricultural expansion （39.8 %）, urbanization （24.9 %）, forest management practice （19.3 %）, and mining （12.8 %）. In general, there was a gradual decreasing trend in forest cover throughout this region, caused principally by China＇s economic, demographic, environmental and political policies and decisions, as well as some weather events. While VCT has largely been used to assess long term changes and trends in the USA, it has great potential for assessing landscape level change elsewhere throughout the world.
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.
