星载LiDAR与HJ-1A/HSI高光谱数据联合估测区域森林冠层高度

doi:10.11707/j.1001-7488.20160517

Abstract

Abstract:

[Objective] In this study, ICESat-GLAS waveforms was combined with HJ-1A/HSI hyperspectral imageries to realize regional estimation of forest canopy height. [Method] We extracted parameters(waveform length W and the terrain slope parameter TS)from ICESat-GLAS waveforms, and built the forest canopy height model with W and TS. The model was used to calculate forest canopy height within each GLAS footprint of the study area. For HSI imageries, the minimum noise fraction(MNF)method was applied to decrease noise and reduce the dimensionality of HSI imageries and the first three MNF components(MNF1, MNF2, MNF3)were selected for further research. Afterwards, the SVR method was applied to establish the relationship between GLAS estimated forest canopy height and the three MNF components, and accordingly the full covered regional forest canopy height map was produced. [Result] The results showed that there was a significant linear relationship between TS and terrain slope(R²=0.78). The R² and RMSE value of the forest canopy height model built by W and TS were 0.78 and 2.51 m, respectively, and the validation results were R²=0.85 and RMSE=1.67 m. The R² and RMSE of SVR model were 0.70 and 3.62 m, respectively, with the validation results of R²=0.67, RMSE=4.42 m. The estimation error of the forest canopy height map was calculated and analyzed by field sample data, and the maximum, minimum and mean value of estimation error were 7.10 m, 0.07 m and 1.78 m, respectively. The standard deviation was 1.49 m, as well as Q1 and Q3 were 0.75 m and 2.31 m, respectively. [Conclusion] TS can perfectly reflect terrain slope. In addition, the linear relationship model built in the study overcomes the difficultly explaining problem of logarithm model in flat area. The study demonstrated that it holds great potential to estimate regional forest canopy height by combining ICESat-GLAS waveforms and HJ-1A/HSI hyperspectral imageries, which overcome the disadvantage of ICESat-GLAS in the aspect of regional estimation caused by its discrete distribution and improve the estimation accuracy.

Key words: ICESat-GLAS waveforms data, HJ-1A/HSI hyperspectral imageries, forest canopy height, terrain slope, support vector regression method

CLC Number:

S771.8

Qiu Sai, Xing Yanqiu, Tian Jing, Ding Jianhua. Forest Canopy Height Estimation of Large Area Using Spaceborne LIDAR and HJ-1A/HSI Hyperspectral Imageries[J]. Scientia Silvae Sinicae, 2016, 52(5): 142-149.

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Forest Canopy Height Estimation of Large Area Using Spaceborne LIDAR and HJ-1A/HSI Hyperspectral Imageries

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