The Potential Impact of Vertical Sampling Uncertainty on ICESat-2/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems

Neuenschwander, A. L.; Magruder, Lori A.

doi:10.3390/rs8121039

Cited by 81 publications

(45 citation statements)

References 25 publications

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“…The ATLAS laser beams within a pair of beams had different transmit energies, which improved the measurement accuracy within the dynamic detector range for given variations in surface reflectance [2]. The MATLAS simulated data included different laser intensities and laser pointing types and was based on the MABEL data.…”

Section: Datamentioning

confidence: 99%

“…The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) that was successfully launched on 15 September 2018, is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), and the main payload of the system is the micro-pulse photon-counting system [1]. Therefore, the system is expected to provide a reliable estimation of carbon storage [2]. Unlike the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud, and land Elevation Satellite-1 (ICESat-1), the ATLAS laser emission mode has six beams grouped in three beam pairs, each of which is composed of two different beam energies to increase system dynamic range.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…ATLAS, a LIDAR system, is a photon-sensitive surface. This technology has many advantages in spatial altimetry (Neuenschwander And Magruder, 2016). ATLAS instrument has two laser operating at 532 nm with transmitter optics for beam steering and a diffractive optical element that divides the signal into 6 separate beams as well as a wavelength tracking system and receivers for start pulse detection.…”

Section: Adm-aeolus Satellitementioning

confidence: 99%

History and Applications of Space-Borne Lidars

Fouladinejad¹,

Matkan²,

Hajeb³

et al. 2019

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

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Abstract. LIDAR (Light Detection and Ranging) is a laser altimeter system that determines the distance by measuring pulse travel time. The data from the LIDAR systems provide unique information on the vertical structure of land covers. Compared to ground-based and airborne LIDARs providing a high-resolution digital surface model, space-borne LIDARs can provide important information about the vertical profile of the atmosphere in a global scale. The overall objective of these satellites is to study the elevation changes and the vertical distribution of clouds and aerosols. In this paper an overview on the space-borne laser scanner satellites are accomplished and their applications are introduced. The first space-borne LIDAR is the ICESat (Ice, Cloud and land Elevation Satellite) satellite carrying the GLAS instrument which was launched in January 2003. The CALIPSO (the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, 2006), CATS-ISS (the Cloud-Aerosol Transport System, 2015), ADM-Aeolus (Atmospheric Dynamics Mission, 2018), and ICESat-2 (Ice, Cloud and land Elevation Satellite-2, 2018) satellites were respectively lunched and began to receive information about the vertical structure of the atmosphere and land cover. In addition, two ACE (The Aerosol-Cloud-Ecosystems, 2022) and EarthCARE (Earth Clouds, Aerosols and Radiation Explorer, 2021) space-borne satellites were planned for future. The data of the satellites are increasingly utilized to improve the numerical weather predictions (NWP) and climate modeling.

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“…Several attempts have been made to separate the signal photons from the noise photons by methods such as spatial statistical based detection algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), ellipsoidal histogramming method, and a framework to retrieve ground and canopy height [25][26][27][28][29]. In addition, some studies focused on the application rather than the data pre-processing showed some promising results, especially for vegetation studies [30][31][32][33]. However, studies focusing on the potential estimation of forest parameters by using the ATLAS-like data are still very limited.…”

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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The Potential Impact of Vertical Sampling Uncertainty on ICESat-2/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems

Cited by 81 publications

References 25 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

History and Applications of Space-Borne Lidars

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

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