Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data

Nie, Sheng; Wang, Cheng; Xi, Xiaohuan; Luo, Shuai; Li, Guoyuan; Tian, Jing; Wang, Hongtao

doi:10.1364/oe.26.00a520

Cited by 91 publications

(42 citation statements)

References 40 publications

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“…The ER-2's flight altitude was 20 km above sea level (ASL). The MABEL data was acquired using a photon-counting Lidar with the pulse width of about 2 ns, the laser wavelength of 532 nm and 1064 nm, the laser energy of 5-7 µJ, the spot diameter of about 2 m, and variable pulse repetition frequency of 5-25 kHz [5,7,8,15]. In order to evaluate the performance of the ATLAS photon cloud data, NASA adjusted the photon data density, trajectory, and spatial resolution of the MABEL according to the parameters of the ATLAS instrument model to generate the MATLAS data.…”

Section: Datamentioning

confidence: 99%

“…ATLAS technology is still in the airborne simulation stage, lacking evaluation criteria for noise filtering. In previous research [12,15,20], visual inspection was used as a first-step evaluation standard for photon cloud filtering. In this work, the PSODBSCAN algorithm is applied to the MATLAS data at different laser beam intensities and pointing types, and both qualitative and quantitative analyses are conducted by visual inspection using the corresponding KML file.…”

Section: Accuracy Evaluationmentioning

confidence: 99%

“…Namely, R denotes the ratio of signal photons that are successfully detected to all the true signal photons, P denotes the ratio of true signal photons that are correctly classified to all the detected signal photons, and F denotes the harmonic mean of recall and precision. These three indicators are calculated using the reference classification data [15,20], and they are respectively given by:…”

Section: Accuracy Evaluationmentioning

confidence: 99%

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Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

et al. 2019

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The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), was launched successfully in 15 September 2018. The ATLAS represents a micro-pulse photon-counting laser system, which is expected to provide more comprehensive and scientific data for carbon storage. However, the ATLAS system is sensitive to the background noise, which poses a tremendous challenge to the photon cloud noise filtering. Moreover, the Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a commonly used algorithm for noise removal from the photon cloud but there has not been an in-depth study on its parameter selection yet. This paper presents an automatic photon cloud filtering algorithm based on the Particle Swarm Optimization (PSO) algorithm, which can be used to optimize the two key parameters of the DBSCAN algorithm instead of using the manual parameter adjustment. The Particle Swarm Optimization Density Based Spatial Clustering of Applications with Noise (PSODBSCAN) algorithm was tested at different laser intensities and laser pointing types using the MATLAS dataset of the forests located in Virginia, East Coast, and the West Coast, USA. The results showed that the PSODBSCAN algorithm and the localized statistical algorithm were effective in identifying the background noise and preserving the signal photons in the raw MATLAS data. Namely, the PSODBSCAN achieved the mean F value of 0.9759, and the localized statistical algorithm achieved the mean F value of 0.6978. For both laser pointing types and laser intensities, the proposed algorithm achieved better results than the localized statistical algorithm. Therefore, the PSODBSCAN algorithm could support the MATLAS photon cloud data noise filtering applicably without manually selecting parameters.

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Section: Datamentioning

confidence: 99%

Section: Accuracy Evaluationmentioning

confidence: 99%

Section: Accuracy Evaluationmentioning

confidence: 99%

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Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

et al. 2019

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“…However, studies focusing on the potential estimation of forest parameters by using the ATLAS-like data are still very limited. There are some findings including investigating the dryland ecosystem vegetation cover and biomass by using a combination of Landsat 8 and ATLAS-like data [34], the similarity comparison with airborne LiDAR system of the ICESat-2 laser altimetry for savanna ecosystem [28,35], and an automated approach for better estimating vegetation canopy height [36]. The study to explored potential for biomass retrieval over boreal ecosystems, using the radiative transfer model FLIGHT to simulate ICESat-2 [37].…”

Section: Introductionmentioning

confidence: 99%

Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest

Chen

Pang

et al. 2019

Remote Sensing

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ICESat-2 is the new generation of NASA’s ICESat (Ice, Cloud and land Elevation Satellite) mission launched in September 2018. We investigate the potential of forest parameter estimation using metrics from photon counting LiDAR data, using an integrated dataset including photon counting LiDAR data from SIMPL (the Slope Imaging Multi-polarization Photon-counting LiDAR), airborne small footprint LiDAR data from G-LiHT and a stem map in Howland Research Forest, USA. First, we propose a noise filtering method based on a local outlier factor (LOF) with elliptical search area to separate the ground and canopy surfaces from noise photons. Next, a co-registration technique based on moving profiling is applied between SIMPL and G-LiHT data to correct geolocation error. Then, we calculate height metrics from both SIMPL and G-LiHT. Finally, we investigate the relationship between the two sets of metrics, using a stem map from field measurement to validate the results. Results of the ground and canopy surface extraction show that our methods can detect the potential signal photons effectively from a quite high noise rate environment in relatively rough terrain. In addition, results from co-registration between SIMPL and G-LiHT data indicate that the moving profiling technique to correct the geolocation error between these two datasets achieves favorable results from both visual and statistical indicators validated by the stem map. Tree height retrieval using SIMPL showed error of less than 3 m. We find good consistency between the metrics derived from the photon counting LiDAR from SIMPL and airborne small footprint LiDAR from G-LiHT, especially for those metrics related to the mean tree height and forest fraction cover, with mean R 2 value of 0.54 and 0.6 respectively. The quantitative analyses and validation with field measurements prove that these metrics can describe the relevant forest parameters and contribute to possible operational products from ICESat-2.

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“…Nie et al (2018) proposed a local statistical analysis algorithm based on strong beam laser type MATLAS data as experimental data. The algorithm establishes the frequency histogram based on the elevation of all photon data.…”

mentioning

confidence: 99%

Review of Noise Filtering Algorithm for Photon Data

Huang

Xing

Qin

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. As a continuation of Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1)/Geoscience Laser Altimeter System (GLAS), the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) , which is equipped with the Advanced Topographic Laser Altimeter (ATLAS) system, was successfully launched in 2018. Since ICESat-1/GLAS has facilitated scientific results in the field of forest structure parameter estimation, how to use the ICESat-2/ATLAS photon cloud data to estimate forest structure parameters has become a hotspot in the field of spaceborne photon data application. However, due to the weak photon characteristics of the ICESat-2/ATLAS system, the system is extremely susceptible to noise, which poses a challenge for its subsequent accurate estimation of forest structural parameters. Aiming to filter out the noise photons, the paper introduces the advantages of the spaceborne lidar system ICESat-2/ATLAS than ICESat-1/GLAS. The paper summarizes the research of the simulated photon-counting lidar (PCL) noise filtering algorithm and noise filtering on spaceborne.

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Estimating the vegetation canopy height using micro-pulse photon-counting LiDAR data

Cited by 91 publications

References 40 publications

Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest

Review of Noise Filtering Algorithm for Photon Data

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