基于TanDEM-X相干系数的森林高度估测方法

doi:10.11707/j.1001-7488.20200604

Abstract

Abstract:

Objective: Using single polarization TanDEM-X InSAR data, we studied the forest height estimation method with InSAR coherence amplitude, and analyzed the effects of different DEMs spatial resolutions (one is LiDAR DEM of 5 m resolution and the other is SRTM DEM of 30 m resolution) on forest height estimation accuracy. Method: First, non-volumetric decorrelation was corrected from the observed coherence in order to obtain the volumetric decorrelation (γ_Vol), and then based on the SINC model, the amplitude of γ_Vol was used to estimate forest height. Then forest height inversion results were validated against LiDAR CHM data and compared with the method based on phase difference. Plot sizes of 15 m×15 m, 30 m×30 m, 50 m×50 m and 100 m×100 m were selected to analyze the effects of spatial interpolation. Result: The effects of DEMs in two kinds of resolutions (5 m×5 m, 30 m×30 m) on inversion accuracy are small, when plot size is 100 m×100 m, the R² are 0.54 and 0.51, the RMSEs are 2.38 m and 2.51 m, and the overall accuracies are 77.19% and 75.99%, respectively. Although the method based on phase difference has a better performance than the SINC model over different plot sizes, its results are obviously underestimated and should be calibrated with some known forest height samples. If the plot size is set as 100 m×100 m, the R² is 0.79, the RMSEs before and after calibration are 2.57 m and 1.63 m, the overall accuracies are 75.44% and 84.41%, respectively. Conclusion: The forest height estimation method using SINC model with the TanDEM-X InSAR coherence amplitude can obtain a good performance even if using a low spatial resolution DEM, such as SRTM DEM of 30 m resolution for terrain compensation and geo-coding. Although the accuracy of SINC model is less accurate than that of phase difference method, it requires neither plot measurements for model calibration nor high-resolution DEM for terrain compensation and geo-coding, so it has the potential for large-scale forest height mapping and higher value for practical applications.

Key words: TanDEM-X, forest height, InSAR, SINC model

CLC Number:

S758

Yaxiong Fan,Erxue Chen,Zengyuan Li,Lei Zhao,Wangfei Zhang,Yudong Jin,Lijie Cai. Forest Height Estimation Method Using TanDEM-X Interferometric Coherence Data[J]. Scientia Silvae Sinicae, 2020, 56(6): 35-46.

Figures/Tables 15

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Fig.14

Fig.15

References 0

	陈尔学, 李增元, 庞勇, 等. 基于极化合成孔径雷达干涉测量的平均树高提取技术. 林业科学, 2007. 43 (4): 66- 70.
	Chen E X , Li Z Y , Pang Y , et al. Polarimetric synthetic aperture radar interferometry mean tree height extraction technique. Scientia Silvae Sinicae, 2007. 43 (4): 66- 70.
	冯琦, 陈尔学, 李增元, 等. 机载X-波段双天线InSAR数据森林树高估测方法. 遥感技术与应用, 2016. 31 (3): 551- 557.
	Feng Q , Chen E X , Li Z Y , et al. Forest height estimation from airborne X-band single-pass InSAR data. Remote Sensing Technology and Application, 2016. 31 (3): 551- 557.
	穆喜云. 2015.森林地上生物量遥感估测方法研究.呼和浩特:内蒙古农业大学博士学位论文.
	Mu X Y. 2015. A study on the estimating method of forest above ground biomass based on remote sensing data. Hohhot: PhD thesis of Inner Mongolia Agricultural University.[in Chinese]
	Chen H , Cloude S R , Goodenough D G . Forest canopy height estimation using TanDEM-X coherence data. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016. 9 (7): 3177- 3188.
	Cloude S R. 2010. Polarisation: applications in remote sensing. UK: Oxford University Press.
	Cloude S R , Chen H , Goodenough D G . Forest height estimation and validation using TanDEM-X PolInSAR. IEEE Geoscience and Remote Sensing Symposium, 2014. 1889- 1892.
	Krieger G , Moreira A , Fiedler H , et al. TanDEM-X:a satellite formation for high-resolution SAR interferometry. IEEE Transactions on Geoscience & Remote Sensing, 2007. 45 (11): 3317- 3341.
	Kugler F , Schulze D , Hajnsek I , et al. TanDEM-X Pol-InSAR performance for forest height estimation. IEEE Transactions on Geoscience & Remote Sensing, 2014. 52 (10): 6404- 6422.
	Olesk A , Praks J , Antropov O , et al. Interferometric SAR coherence models for characterization of hemiboreal forests using TanDEM-X data. Remote Sensing, 2016. 8 (9): 700. doi: 10.3390/rs8090700
	Olesk A , Voormansik K , Vain A , et al. Seasonal differences in forest height estimation from interferometric TanDEM-X coherence data. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015. 8 (12): 5565- 5572.
	Praks J , Hallikainen M , Antropov O , et al. Boreal forest tree height estimation from interferometric TanDEM-X images. IEEE Geoscience and Remote Sensing Symposium, 2012. 1262- 1265.
	Sadeghi Y , St-Onge B , Leblon B , et al. Mapping forest canopy height using TanDEM-X DSM and airborne LiDAR DTM. IEEE Geoscience and Remote Sensing Symposium, 2014. 76- 79.
	Seymour M S , Cumming I G . Maximum likelihood estimation for SAR interferometry. IEEE Geoscience and Remote Sensing Symposium, 1994. 2272- 2275.
	Soja M J , Persson H J , Ulander L M H . Estimation of forest biomass from two-level model inversion of single-pass InSAR data. IEEE Transactions on Geoscience & Remote Sensing, 2015. 53 (9): 5083- 5099.
	Soja M J , Ulander L M H . Digital canopy model estimation from TanDEM-X interferometry using high-resolution LiDAR DEM. IEEE Geoscience and Remote Sensing Symposium, 2013. 165- 168.
	Soja M J , Ulander L M H . Two-level forest model inversion of interferometric TanDEM-X data. 10^th European Conference on Synthetic Aperture Radar, 2014. 1- 4.
	Treuhaft R N , Madsen S N , Moghaddam M , et al. Vegetation characteristics and underlying topography from interferometric radar. Radio Science, 1996. 31 (6): 1449- 1485. doi: 10.1029/96RS01763

[1]	Li Lan, Chen Erxue, Li Zengyuan, Ren Chong, Zhao Lei, Gu Xinzhi. Forest Above-Ground Biomass Estimation Based on Multi-Baseline InSAR Tomography [J]. Scientia Silvae Sinicae, 2017, 53(11): 85-93.
[2]	Wang Xinshuang, Chen Erxue, Li Zengyuan, Yao Wanqiang, Zhao Lei. Multi-Temporal and Dual-Polarization SAR for Forest Land Type Classification [J]. Scientia Silvae Sinicae, 2014, 50(3): 83-91.
[3]	Pang Yong;Li Zengyuan;Chen Erxue;Sun Guoqing;. Lidar Remote Sensing Technology and Its Application in Forestry [J]. Scientia Silvae Sinicae, 2005, 41(3): 129-136.

Forest Height Estimation Method Using TanDEM-X Interferometric Coherence Data

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 0

Related Articles 3

Recommended Articles

Metrics

Comments