Global Elevation Inversion for Multiband Spaceborne Lidar: Predevelopment of Forest Canopy Height

Haowei Zhang,Wei Gong, Hu He,Yue Ma,Weibiao Chen,Jiqiao Liu,Ge Han, Zhiyu Gao, Wanqi Zhong,Xin Ma

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing（2025）SCI 3区

Wuhan Univ | China Petr & Chem Corp | Tianjin Inst Marine Instrumentat

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Abstract

Compared to single-band spaceborne lidars such as the global ecosystem dynamics investigation (GEDI) and Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), multiband spaceborne lidars improve the detection of the canopy and ground. However, research on geographic elevation inversion with multi-band spaceborne lidars is limited, especially in developing algorithms that fully utilize multiple wavelengths for accurate measurements. A high-precision multiband fusion algorithm (MBFA) is proposed for global geographic elevation inversion for multiband spaceborne lidar of China's Daqi-1 satellite (DQ-1), enhancing the ranging capability of the 1572 nm channel by approximately 5 times. Compared with ICESat-2, GEDI and airborne scanning data in Finland, the geographic elevation results of MBFA showed average biases of -0.09, 0.1, and -0.95 m, with root mean square errors (RMSE) of 3.68, 4.51, and 7.70 m, respectively. Accurate forest canopy heights can be obtained using the decomposed signal approach in MBFA, which has been verified in Finland. The validation results (R-2 = 0.72, RMSE = 1.38 m, BIAS = -0.75 m) demonstrate the DQ-1 satellite's effectiveness in measuring canopy height. The results indicate that the MBFA model has potential for global forest canopy height extraction and carbon sink research. The proposed MBFA can also provide guide for high-precision inversion of future multiband lidar satellites.

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Key words

Laser radar,Forestry,Satellites,Accuracy,Data mining,Vegetation mapping,Carbon,Remote sensing,Monitoring,Laser pulses,Aerosol and carbon dioxide detection lidar (ACDL),active monitoring,multiband lidar

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
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