摘要
为了解决单基线PolInSAR在更宽的森林高度范围内反演森林高度误差大的问题,提出了多基线PolInSAR的基线选择方法。使用JPL/NASA于2016年2月27日在加蓬森林区域获得UAVSAR L波段5个轨道的多基线全极化PolInSAR数据反演森林高度,基于相干分离最大算法(Maximum coherence difference,MCD)使复相干达到最大分离,改进PROD方法与ECC方法,并对这两种方法进行对比分析,同时使用NASA于2016年3月4日获取的激光雷达数据LVIS RH100验证反演的森林高度。通过绘制两种基线选择方法对应的k z、冠层复相干幅度与LVIS RH100的密度图来评估ECC方法和PROD方法选择基线的差异,并结合获得的森林高度图、误差图、密度图,分析对比两种基线选择方法的优劣。反演的森林高度出现低估高大森林(误差为负值)、高估低矮森林(误差为正值)的现象,同时低矮与高大森林区域的误差较大,且ECC方法的低估或高估的程度比PROD方法大,精度低于PROD方法。ECC方法将相干区域的线性程度作为判断标准,PROD方法综合考虑了复相干的相干分离程度(相干直线的拟合效果)与复相干幅度,在一定程度上缓解了ECC方法低估高大森林与高估低矮森林的问题,反演的森林高度优于ECC方法,精度比ECC方法提高了9.63%。PROD方法更适用于反演低矮与高大森林,ECC方法更适用于反演中等高度的森林。
To accurately inverse the forest height over a wider range,it is necessary to study the baseline selection method for multi-baseline PolInSAR data to alleviate large errors in forest height inversion over a wider range from single-baseline,exploring a better baseline selection method.The UAVSAR L-band multi-baseline full PolInSAR data was used from five orbits obtained by JPL/NASA in Pongara,Gabon forest on February 27,2016.Based on the maximum coherence difference(MCD)coherent optimization algorithm to make complex coherence maximum separation,the PROD method and the ECC method were improved,compared and analyzed;and verified by using the LiDAR data LVIS RH100 obtained by NASA on March 4,2016.The error maps of the difference between the forest height and the LVIS RH100 inverted by the two baseline selection methods were plotted to analyze the results of the forest height inversion.And the density maps of the k z,canopy coherence amplitude corresponding to the two baseline selection methods and LVIS RH100 were plot to directly evaluate the difference between the ECC method and the PROD method selecting the baseline,and comparing and analyzing the pros and cons of the two baseline selection methods.Combined with these drawn graphs(forest height maps,error maps and density maps),the forest heights inverted by the two baseline selection methods were compared and analyzed.The error in low and high forest areas was large.The high forests were underestimated(the error was negative),and the forests in the low areas were overestimated(the error was positive).The underestimated or overestimation of the ECC method was greater than the PROD method,and the accuracy was inferior to the PROD method.The two methods had good consistency compared with the LVIS RH100 data.The linear equation of the ECC method and LVIS RH100 was y=0.50x+10.60 and R 2=0.69.The fitted linear equation of the PROD method was y=0.63x+8.21 and R 2=0.70.Validated with the LVIS RH100 data,the RMSEs of the ECC method and PROD method were 9.80 m and 8.86 m,r
作者
张建双
范文义
于颖
ZHANG Jianshuang;FAN Wenyi;YU Ying(School of Forestry,Northeast Forestry University,Harbin 150040,China;Key Laboratory of Sustainable Forest Ecosystem Management,Ministry of Education,Northeast Forestry University,Harbin 150040,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2019年第12期221-230,共10页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家重点研发计划项目(2017YFB0502700)
中央高校基本科研业务费专项资金项目(2572019CP12)
