自"九五"以来中国地震局在中国大陆不同地区建立了大量的数字化地震观测台站,为地震预测和数字地震学的研究工作提供了海量的可靠的数据资源。利用这些数字化波形数据目前已开展了大量的地震预测研究工作,取得一系列成果,为...自"九五"以来中国地震局在中国大陆不同地区建立了大量的数字化地震观测台站,为地震预测和数字地震学的研究工作提供了海量的可靠的数据资源。利用这些数字化波形数据目前已开展了大量的地震预测研究工作,取得一系列成果,为地震预测提供了新的思路和方法。为此,在地震预测和数字地震学相关研究工作中,数字波形分析方法及其相关处理软件对于地震波信息的挖掘变得越来越重要,基于此原因研制了《数字波形地震预测分析软件系统——wavePRED Version 2007》。通过一段时间的检验和应用,认为该软件可作为地震预测和数字地震学研究工作中一款分析工具。本文首先概述了利用数字波形进行地震预测工作的重要性及其存在的一些问题,然后介绍了wavePRED系统的研制意义、设计目标、研制思路、系统具有的一些特点、构建系统的框架体系、操作界面、技术特点、方法与功能等,给出了该系统的应用实例,最后对软件中存在的问题进行了讨论,也对软件的未来前途进行了美好展望。展开更多
The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance.Forest aboveground biomass(AGB) is an important component in the land surface carbon cycl...The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance.Forest aboveground biomass(AGB) is an important component in the land surface carbon cycle.In this study,using forest inventory data and forest distribution data,the AGB was estimated for forest in Daxinganlin in northeastern China by combining charge-coupled device(CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast(HJ-1) and Geoscience Laser Altimeter System(GLAS) waveform data from the Ice,Cloud and land Elevation Satellite(ICESat).The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing(CQ) Forest Center,which was calculated from forest inventory data.The resulted statistical regression models have a R2=0.68 for conifer and R2=0.71 for broadleaf forests.These models were used to estimate biomass for all GLAS footprints of forest located in the study area.All GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models(R2=0.55 and R2=0.52 for needle and broadleaf respectively).Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 data.Biomass data obtained from forest inventory data of the Zhuanglin(ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data(R2=0.71).About 80% of biomass samples had an error less than 20 t ha-1,and the mean error of all validation samples is 5.74 t ha-1.The pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this area.It was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estimate forest biomass with good precision,and the biomass data can be used as input data for future carbon budget analysis.展开更多
文摘自"九五"以来中国地震局在中国大陆不同地区建立了大量的数字化地震观测台站,为地震预测和数字地震学的研究工作提供了海量的可靠的数据资源。利用这些数字化波形数据目前已开展了大量的地震预测研究工作,取得一系列成果,为地震预测提供了新的思路和方法。为此,在地震预测和数字地震学相关研究工作中,数字波形分析方法及其相关处理软件对于地震波信息的挖掘变得越来越重要,基于此原因研制了《数字波形地震预测分析软件系统——wavePRED Version 2007》。通过一段时间的检验和应用,认为该软件可作为地震预测和数字地震学研究工作中一款分析工具。本文首先概述了利用数字波形进行地震预测工作的重要性及其存在的一些问题,然后介绍了wavePRED系统的研制意义、设计目标、研制思路、系统具有的一些特点、构建系统的框架体系、操作界面、技术特点、方法与功能等,给出了该系统的应用实例,最后对软件中存在的问题进行了讨论,也对软件的未来前途进行了美好展望。
基金supported by National Basic Research Program of China (Grant No.2007CB714404)National Natural Science Foundation of China (Grant Nos.40701124,40930530)
文摘The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance.Forest aboveground biomass(AGB) is an important component in the land surface carbon cycle.In this study,using forest inventory data and forest distribution data,the AGB was estimated for forest in Daxinganlin in northeastern China by combining charge-coupled device(CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast(HJ-1) and Geoscience Laser Altimeter System(GLAS) waveform data from the Ice,Cloud and land Elevation Satellite(ICESat).The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing(CQ) Forest Center,which was calculated from forest inventory data.The resulted statistical regression models have a R2=0.68 for conifer and R2=0.71 for broadleaf forests.These models were used to estimate biomass for all GLAS footprints of forest located in the study area.All GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models(R2=0.55 and R2=0.52 for needle and broadleaf respectively).Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 data.Biomass data obtained from forest inventory data of the Zhuanglin(ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data(R2=0.71).About 80% of biomass samples had an error less than 20 t ha-1,and the mean error of all validation samples is 5.74 t ha-1.The pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this area.It was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estimate forest biomass with good precision,and the biomass data can be used as input data for future carbon budget analysis.
