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Optimization of TECIS LiDAR waveform metrics for forest height estimation

Year: 2025
Type: article
Abstract: The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS), as the first orbiting satellite equipped with both waveform LiDAR and multi-angle imaging capabilities, has collected a large amount of waveform data. The waveform threshold and waveform index calculation methods determine the results of the waveform indices, thereby affecting canopy height estimation. To analyse this impact, we conducted a canopy height estimation study using airborne laser scanning (ALS) data in the northern forest region of China. First, we performed an effective selection of TECIS footprints and geographic location matching. Next, we calculated waveform indices using 24 waveform thresholds and 4 waveform index calculation methods, and analysed the bias between the relative height (RH) index and ALS percentile index. Then, we performed linear regression modelling of 10 waveform indices to estimate ALS 99th percentile height (P99). Finally, we discussed the bias between RH100 and ALS P99 under different conditions. The analysis found that stricter waveform threshold settings resulted in lower biases, and higher noise thresholds in the signal start part led to even lower biases. The RH index calculated based on waveform decomposition and signal cut-off points showed lower bias. ALS P99 modelling results indicated that, without differentiating slope, the optimal modeling result for waveform indices had a Poisson correlation coefficient(r) of 0.863. Data collected under high signal-to-noise ratio, low slope, and night-time conditions showed lower biases. This study provides a new perspective on TECIS waveform canopy height estimation from the signal processing angle, supporting large-scale applications of wide-footprint waveform data.
DOI: 10.1016/j.jag.2025.104700
Language: English
Open Access status: gold
Estimated APC paid: $3,550
OpenAlex ID: W4411725238
FWCI: 0.566
Cites: 34
Cited by: 2
Related to: 10