Cloud Detection and Centroid Extraction of Laser Footprint Image of GF-7 Satellite Laser Altimetry

The laser altimeter loaded on the GaoFen-7(GF-7)satellite is designed to record the full waveform data and footprint image,which can obtain high-precision elevation control points for stereo image.The footprint camera equipped on the GF-7 laser altimetry system can capture the energy distribution at the time of laser emission and the image of the ground object where the laser falls,which can be used to judge whether the laser is affected by the cloud.At the same time,the centroid of laser spot on the footprint image can be extracted to monitor the change of laser pointing stability.In this manuscript,a data quality analysis scheme of laser altimetry based on footprint image is presented.Firstly,the cloud detection of footprint image is realized based on deep learning.The fusion result of the model is about 5%better than that of the traditional cloud detection algorithm,which can quickly and accurately determine whether the laser spot is affected by cloud.Secondly,according to the characteristics of footprint image,a threshold constrained ellipse fitting method for extracting the centroid of laser spot is proposed to monitor the pointing stability of long-period lasers.Based on the above method,the change of laser spot centroid since GF-7 satellite was put into operation is analyzed,and the conclusions obtained have certain reference significance for the quality control of satellite laser altimetry data and the analysis of pointing angle stability.

Denoising for satellite laser altimetry full-waveform data based on EMD-Hurst analysis

Full-waveform decomposition is crucial for obtaining accurate satelliteground distance,the accuracy of which is severely affected by noises.However,the traditional filters all depend on filtering parameters.This paper presents a new and adaptive method for denoising based on empirical mode decomposition(EMD)and Hurst analysis(EMD-Hurst).The noisy full-waveforms are first decomposed into their intrinsic mode functions(IMFs),and the Hurst exponent of each IMF is established by the detrended fluctuation analysis.The IMF is regarded as the highfrequency noise and is deleted if its Hurst exponent is≤0.5.Both simulated and real full-waveforms were conducted to validate and evaluate the method by comparing with six other IMF selection methods via metrics like waveform decomposition consistency ratio(CR),average error of decomposition parameters,and ICESat/GLAS waveformparameter product GLAH05.The comparisons show that:(1)under different SNR conditions,EMD-Hurst performs robustly and obtains a higher CR than other EMD based methods;(2)obtains the highest average CR and a relatively lower average error for the echo parameters;and(3)peak numbers and fitting accuracy for GLAH01 are more reasonable and precise than those of GLAH05,which could offer a good reference for the processing on future space-borne full-waveform data.

Potential and limitations of satellite laser altimetry for monitoring water surface dynamics:ICESat for US lakes

Elevation measurements from the Ice,Cloud and Land Elevation Satellite(ICESat)have been applied to monitor dynamics of lakes and other surface water bodies.Despite such potential,the true utility of ICEsat--more generally,satellite laser altimetry--for continuously tracking surface water dynamics over time has not been adequately assessed,especially in the continental or global contexts.This study analyzed elevation derived from ICESat data for the conterminous United States and examined the potential and limitations of satellite laser altimetry in monitoring the water level dynamics.Owing to a lack of spatially-explicit ground-based water-level data,the high-fidelity land elevation data acquired by airborne lidar were firstly resorted to quantify ICESat’s ranging accuracy.Trend and frequency analyses were then performed to evaluate how reliably ICESat could capture water-level dynamics over a range of temporal scales,as compared to in-situ gauge measurements.The analytical results showed that ICESat had a vertical ranging error of 0.16 m at the footprint level-an lower limit on the detectable range of water-level dynamics.The sparsity of data over time was identified as a major factor limiting the use of ICESat for water dynamics studies.Of all the US lakes,only 361 had reliable ICESat measurements for more than two flight passes.Even for those lakes with sufficient temporal coverage,ICESat failed to capture the true interannual water-level dynamics in 32%of the cases.Our frequency analysis suggested that even with a repeat cycle of two months,ICESat could capture only 60%of the variations in water-level dynamics for at most 34%of the US lakes.To capture 60%of the water-level variation for most of the US lakes,a weekly repeated cycle(e.g.,less than 5 d)is needed-a requirement difficult to meet in current designs of spaceborne laser altimetry.Overall,the results highlight that current or near-future satellite laser missions,though with high ranging accuracies,are unlikely to fulfill the general needs in remotely monitoring water surface dynamics for lakes or reservoirs.