Analysis of MABEL Bathymetry in Keweenaw Bay and Implications for ICESat-2 ATLAS

Forfinski-Sarkozi, Nicholas A.; Parrish, Christopher

doi:10.3390/rs8090772

Cited by 56 publications

(24 citation statements)

References 55 publications

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“…To evaluate the performance of the photon classification algorithm, the simulated ICESat-2 data (MATLAS data) were investigated. The MATLAS data were generated from the Multiple Altimeter Beam Experimental Lidar (MABEL) data [30][31][32][33][34], which is an airborne simulator of ATLAS using a NASA ER-2 aircraft flying at an altitude of 20 km. At that altitude, the laser footprint diameter is 2 m compared to the 14 m footprint of ATLAS.…”

Section: Matlas Datamentioning

confidence: 99%

A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

et al. 2018

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The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission employs a micro-pulse photon-counting LiDAR system for mapping and monitoring the biomass and carbon of terrestrial ecosystems over large areas. In preparation for ICESat-2 data processing and applications, this paper aimed to develop and validate an effective algorithm for better estimating ground elevation and vegetation height from photon-counting LiDAR data. Our new proposed algorithm consists of three key steps. Firstly, the noise photons were filtered out using a noise removal algorithm based on localized statistical analysis. Secondly, we classified the signal photons into canopy photons and ground photons by conducting a series of operations, including elevation frequency histogram building, empirical mode decomposition (EMD), and progressive densification. At the same time, we also identified the top of canopy (TOC) photons from canopy photons by percentile statistics method. Thereafter, the ground and TOC surfaces were generated from ground photons and TOC photons by cubic spline interpolation, respectively. Finally, the ground elevation and vegetation height were estimated by retrieved ground and TOC surfaces. The results indicate that the noise removal algorithm is effective in identifying background noise and preserving signal photons. The retrieved ground elevation is more accurate than the retrieved vegetation height, and the results of nighttime data are better than those of the corresponding daytime data. Specifically, the root-mean-square error (RMSE) values of ground elevation estimates range from 2.25 to 6.45 m for daytime data and 2.03 to 6.03 m for nighttime data. The RMSE values of vegetation height estimates range from 4.63 to 8.92 m for daytime data and 4.55 to 8.65 m for nighttime data. Our algorithm performs better than the previous algorithms in estimating ground elevation and vegetation height due to lower RMSE values. Additionally, the results also illuminate that the photon classification algorithm effectively reduces the negative effects of slope and vegetation coverage. Overall, our paper provides an effective solution for estimating ground elevation and vegetation height from micro-pulse photon-counting LiDAR data.

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Section: Matlas Datamentioning

confidence: 99%

A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

et al. 2018

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“…Because water clarity, and, hence, depth mapping performance for any bathymetric lidar tends to vary temporally in many locations, ICESat-2's repeat cycle of 91 days and the pointing strategy for distributed measurements in the mid-latitudes provide opportunities to explore seasonal dynamics and temporal characteristics for sites of interest. Previous work by the research team [17][18][19] investigated the bathymetric mapping potential of ICESat-2, using data from the Multiple Altimeter Beam Experimental Lidar (MABEL), an engineering testbed for ATLAS, and laser-radar equation-based modelling. However, prior to launch, a number of sensor parameters were only roughly known, such that precise quantification of ATLAS's bathymetric mapping capability was limited.…”

Section: Introductionmentioning

confidence: 99%

Validation of ICESat-2 ATLAS Bathymetry and Analysis of ATLAS’s Bathymetric Mapping Performance

et al. 2019

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NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was launched in September, 2018. The satellite carries a single instrument, ATLAS (Advanced Topographic Laser Altimeter System), a green wavelength, photon-counting lidar, enabling global measurement and monitoring of elevation with a primary focus on the cryosphere. Although bathymetric mapping was not one of the design goals for ATLAS, pre-launch work by our research team showed the potential to map bathymetry with ICESat-2, using data from MABEL (Multiple Altimeter Beam Experimental Lidar), NASA’s high-altitude airborne ATLAS emulator, and adapting the laser-radar equation for ATLAS specific parameters. However, many of the sensor variables were only approximations, which limited a full assessment of the bathymetric mapping capabilities of ICESat-2 during pre-launch studies. Following the successful launch, preliminary analyses of the geolocated photon returns have been conducted for a number of coastal sites, revealing several salient examples of seafloor detection in water depths of up to ~40 m. The geolocated seafloor photon returns cannot be taken as bathymetric measurements, however, since the algorithm used to generate them is not designed to account for the refraction that occurs at the air–water interface or the corresponding change in the speed of light in the water column. This paper presents the first early on-orbit validation of ICESat-2 bathymetry and quantification of the bathymetric mapping performance of ATLAS using data acquired over St. Thomas, U.S. Virgin Islands. A refraction correction, developed and tested in this work, is applied, after which the ICESat-2 bathymetry is compared against high-accuracy airborne topo-bathymetric lidar reference data collected by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The results show agreement to within 0.43—0.60 m root mean square error (RMSE) over 1 m grid resolution for these early on-orbit data. Refraction-corrected bottom return photons are then inspected for four coastal locations around the globe in relation to Visible Infrared Imaging Radiometer Suite (VIIRS) Kd(490) data to empirically determine the maximum depth mapping capability of ATLAS as a function of water clarity. It is demonstrated that ATLAS has a maximum depth mapping capability of nearly 1 Secchi in depth for water depths up to 38 m and Kd(490) in the range of 0.05–0.12 m−1. Collectively, these results indicate the great potential for bathymetric mapping with ICESat-2, offering a promising new tool to assist in filling the global void in nearshore bathymetry.

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“…Airborne bathymetric LiDAR, which can achieve spatial resolutions of ≤1 m and vertical resolutions of ≤15 cm (Brock et al, 2004;Zawada & Brock, 2009), is emerging as the premier technology for creating high-resolution digital terrain models for coral reefs and other shallow-water environments (Brock et al, 2004;Costa et al, 2009;Pittman et al, 2013;Purkis & Kohler, 2008;Walker et al, 2008;Zawada & Brock, 2009). However, the cost of LiDAR is still prohibitive for large-scale surveys, although ICESat-2 might eventually make LiDAR bathymetry more accessible (Forfinski-Sarkozi & Parrish, 2016). Likewise, drones, coupled with structure-from-motion (SfM) and fluid lensing technology (Chirayath & Earle, 2016) enable bathymetric mapping at centimeter-scale resolution (Casella et al, 2017;Hamylton, 2017;Purkis, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Simple Method for Extracting Water Depth From Multispectral Satellite Imagery in Regions of Variable Bottom Type

Geyman

Maloof

2019

Earth and Space Science

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Satellite imagery offers an efficient and cost‐effective means of estimating water depth in shallow environments. However, traditional empirical algorithms for calculating water depth often are unable to account for varying bottom reflectance, and therefore yield biased estimates for certain benthic environments. We present a simple method that is grounded in the physics of radiative transfer in seawater, but made more robust through the calibration of individual color‐to‐depth relationships for separate spectral classes. Our cluster‐based regression (CBR) algorithm, applied to a portion of the Great Bahama Bank, drastically reduces the geographic structure in the residual and has a mean absolute error of 0.19 m with quantified uncertainties. Our CBR bathymetry is 3–5 times more accurate than existing models and outperforms machine learning protocols at extrapolating beyond the calibration data. Finally, we demonstrate how comparison of CBR with traditional models sensitive to bottom type reveals the characteristic length scales of biosedimentary facies belts.

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Analysis of MABEL Bathymetry in Keweenaw Bay and Implications for ICESat-2 ATLAS

Cited by 56 publications

References 55 publications

A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data

Validation of ICESat-2 ATLAS Bathymetry and Analysis of ATLAS’s Bathymetric Mapping Performance

A Simple Method for Extracting Water Depth From Multispectral Satellite Imagery in Regions of Variable Bottom Type

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