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91.

基于归一化点源敏感性的GSSM振动影响评价

安其昌1,2,张景旭1,杨飞1,赵宏超1

(1.中国科学院长春光学精密机械与物理研究所,长春 130033;

2.中国科学院大学,北京 100039)

创新点说明:

1)深入研究了振动对于GSSM光学中继作用的影响,并建立与PSSn的联系;

2)引入统计矩的方法获得GSSM在任意可测载荷下的光学传递函数;

3)引入功率谱的方法获得GSSM在统计载荷下的光学传递函数。

中文摘要:

巨型可控科学反射镜系统(GSSM)是国际合作项目30 m望远镜(TMT)的三镜。为全面研究GSSM 在振动下的光学传递能力的降低,本文引入了归一化点源敏感性(PSSn)以实现在整个频域上全面地评价系统所受到的影响。首先,介绍了PSSn的基本概念和定义,推导了在大气视宁占主导情况下系统PSSn的计算方法,为GSSM的概念设计与误差估计提供理论依据。之后,对在可测的任意载荷及符合统计规律的载荷(如风载)这两类载荷下的系统光学传递函数的计算方法进行了研究。在任意载荷下使用的是所测得振动的高阶统计矩,通过数值的方法来获得系统的光学传递函数;而对于符合统计规律的载荷,使用功率谱来进行分析。通过实测结合仿真得到GSSM在上述两种情况下PSSn从0.996分别下降到0.994和0.991。本文的研究,建立了GSSM所受振动与PSSn之间的关系,可以为系统工程师在进行GSSM以及TMT的误差分配过程中提供参考。同时由于PSSn良好的合成特性,可以结合其他误差项一起考虑。

关键词:三十米望远镜;标准化点源敏感性;光学传递函数;机械振动

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Vegetation indices and transformations have been used extensively in forest change detection studies. In this study, we processed multitemporal normalized difference moisture index (NDMI) and tasseled cap wetness (TCW) data sets and compared their statistical relationships and relative efficiencies in detecting forest disturbances associated with forest type and harvest intensity at five, two and one year Landsat acquisition intervals. The NDMI and TCW were highly correlated (>0.95 r2) for all five image dates. There was no significant difference between TCW and NDMI for detecting forest disturbance. Using either a NDMI or TCW image differencing method, when Landsat image acquisitions were 5 years apart, clear cuts could be detected with nearly equal accuracy compared to images collected 2 years apart. Partial cuts had much higher commission and omission errors compared to clear cut. Both methods had 7-8% higher commission and 12-22% higher omission error to detect hardwood disturbance when it occurred in the first year of the 2-year interval (as compared to 1-year interval). Softwood and hardwood change detection errors were slightly higher at 2-year Landsat acquisition intervals compared to 1-year interval. For images acquired 1 and 2 years apart, NDMI forest disturbance commission and omission errors were slightly lower than TCW. The NDMI can be calculated using any sensor that has near-infrared and shortwave bands and is at least as accurate as TCW for detecting forest type and intensity disturbance in biomes similar to the Maine forest, particularly when Landsat images are acquired less than 2 years apart. Where partial cutting is the most dominant harvesting system as is currently the case in northern Maine, we recommend images collected every year to minimize (particularly omission) errors. However, where clear cuts or nearly complete canopy removal occurs, Landsat intervals of up to 5 years may be nearly as accurate in detecting forest change as 1 or 2 year intervals.  相似文献   
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