基于TanDEM-X数据的林分平均高反演方法研究
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摘要
以TanDEM-X/TerraSAR-X HH单极化干涉对和GF-2遥感数据为基础,提出结合极化干涉与混合像元分解技术的改进差分法来反演林分平均高,并利用外业数据进行精度验证。结果表明:以植被丰度校正冠层高度模型,林分平均高的估测精度和R~2值得到大幅提高,均方根误差也随之降低。因此,本研究提出的方法能有效降低林分低郁闭度产生的混合像元作用对林分平均高反演的影响,提高林分平均高的反演精度。
An improved differential method combining polarization interference and mixed pixel decomposition techniques is proposed to invert the average height of stands based on TanDEM-X/TerraSAR-X HH singlepolarization interference pairs and GF-2 remote sensing data. And use the field data to verify accuracy. The results show that the canopy height model is corrected by vegetation abundance, the estimation accuracy and R~2 value of the average height of the stand are greatly improved, and the root mean square error is also reduced. Therefore,the method proposed in this study can effectively reduce the influence of the mixed pixel function produced by the low canopy density of the stand on the average high inversion of the stand, and improve the average high inversion accuracy of the stand.
An improved differential method combining polarization interference and mixed pixel decomposition techniques is proposed to invert the average height of stands based on TanDEM-X/TerraSAR-X HH singlepolarization interference pairs and GF-2 remote sensing data. And use the field data to verify accuracy. The results show that the canopy height model is corrected by vegetation abundance, the estimation accuracy and R~2 value of the average height of the stand are greatly improved, and the root mean square error is also reduced. Therefore,the method proposed in this study can effectively reduce the influence of the mixed pixel function produced by the low canopy density of the stand on the average high inversion of the stand, and improve the average high inversion accuracy of the stand.
引文
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[11]杜凯,林辉,龙江平,等.基于GVB模型改进算法的PolInSAR林分高度反演[J].中南林业科技大学学报,2018,38(1):49-54.
[12]Karila K,Vastaranta M,Karjalainen M,et al.TandemX interferometry in the prediction of forest inventory attributes in managed boreal forests[J].Remote Sensing of Environment,2015,159:259-268.
[13]蓝金辉,邹金霖,郝彦爽,等.高光谱遥感影像混合像元分解研究进展[J].遥感学报,2018,22(1):13-27.
[14]庞勇,李增元,陈尔学,等.干涉雷达技术用于林分高估测[J].遥感学报,2003,7(1):8-13.
[15]Kenyi L W,Dubayah R,Hofton M,et al.Comparative analysis of SRTM-NED vegetation canopy height to LIDAR-derived vegetation canopy metrics[J].International Journal of Remote Sensing,2009,30(11):2797-2811.
[16]Rignot E.Dual-frequency interferometric SAR observations of a tropical rain-forest[J].Geophysical Research Letters,1996,23(9):993-996.
[17]Cloude S R.极化建模与雷达遥感应用[M].北京:电子工业出版社,2015.
[18]Woodhouse I H.微波遥感导论[M].董晓龙,徐星欧,徐曦煜,译.北京:科学出版社,2014.
[19]Miliaresis G,Delikaraoglou D.Effects of percent tree canopy density and DEM misregistration on SRTM/NED vegetation height estimates[J].Remote Sensing,2009,1(2):36-49.
[20]Praks J,Kugler F,Papathanassiou K P,et al.Height estimation of boreal forest:interferometric model-based inversion at L-and X-band versus HUTSCAT profiling scatterometer[J].IEEE Geoscience&Remote Sensing Letters,2007,4(3):466-470.
[2]王超,张红,刘智.星载合成孔径雷达干涉测量[M].北京:科学出版社,2002.
[3]张晓莉,赵鹏祥,高凌寒,等.基于ArboLiDAR的林分自动分割研究与应用[J].中南林业科技大学学报,2017,37(11):76-83.
[4]孙华,鞠洪波,张怀清,等.基于Worldview-2影像的林木冠幅提取与树高反演[J].中南林业科技大学学报,2014,34(10):45-50.
[5]张王菲,陈尔学,李增元,等.干涉、极化干涉SAR技术森林高度估测算法研究进展[J].遥感技术与应用,2017,32(6):983-997.
[6]岳彩荣,肖虹雁,曹霸.基于PolInSAR森林高度反演研究[J].西南林业大学学报,2016,36(3):137-143.
[7]章皖秋,岳彩荣,刘晓英.基于TerraSAR-X/TanDEM-X干涉DEM的森林冠层高度估测[J].西南林业大学学报,2016,36(6):64-72.
[8]Kugler F,Schulze D,Hajnsek I,et al.TanDEM-X polInSAR performance for forest height estimation[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(10):6404-6422.
[9]Neumann M,Ferro-Famil L,Reigber A.Estimation of forest structure,ground,and canopy layer characteristics from multibaseline polarimetric interferometric SAR data[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(3):1086-1104.
[10]谈璐璐,杨立波,杨汝良.基于ESPRIT算法的极化干涉SAR植被高度反演研究[J].测绘学报,2011,40(3):296-300.
[11]杜凯,林辉,龙江平,等.基于GVB模型改进算法的PolInSAR林分高度反演[J].中南林业科技大学学报,2018,38(1):49-54.
[12]Karila K,Vastaranta M,Karjalainen M,et al.TandemX interferometry in the prediction of forest inventory attributes in managed boreal forests[J].Remote Sensing of Environment,2015,159:259-268.
[13]蓝金辉,邹金霖,郝彦爽,等.高光谱遥感影像混合像元分解研究进展[J].遥感学报,2018,22(1):13-27.
[14]庞勇,李增元,陈尔学,等.干涉雷达技术用于林分高估测[J].遥感学报,2003,7(1):8-13.
[15]Kenyi L W,Dubayah R,Hofton M,et al.Comparative analysis of SRTM-NED vegetation canopy height to LIDAR-derived vegetation canopy metrics[J].International Journal of Remote Sensing,2009,30(11):2797-2811.
[16]Rignot E.Dual-frequency interferometric SAR observations of a tropical rain-forest[J].Geophysical Research Letters,1996,23(9):993-996.
[17]Cloude S R.极化建模与雷达遥感应用[M].北京:电子工业出版社,2015.
[18]Woodhouse I H.微波遥感导论[M].董晓龙,徐星欧,徐曦煜,译.北京:科学出版社,2014.
[19]Miliaresis G,Delikaraoglou D.Effects of percent tree canopy density and DEM misregistration on SRTM/NED vegetation height estimates[J].Remote Sensing,2009,1(2):36-49.
[20]Praks J,Kugler F,Papathanassiou K P,et al.Height estimation of boreal forest:interferometric model-based inversion at L-and X-band versus HUTSCAT profiling scatterometer[J].IEEE Geoscience&Remote Sensing Letters,2007,4(3):466-470.
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