This repository contains tools for deriving supraglacial lake bathymetry from NASA's Airborne Topographic Mapper (ATM) aerial imagery using NASA's Ames Stereo Pipeline (ASP), a suite of free and open source, automated geodesy and stereogrammetry tools. The repository is comprised of Jupyter notebooks, Python™ code and data files with the intention of publicly sharing tools and results as the project evolves. It is a living repository intended to invite people to contribute and comment and use the tools that are being developed.

This repository is within the spirit of NASA's Transform to Open Science (TOPS) program with the goal of transforming communities to an inclusive culture of open science.

Jupyter notebooks currently available in this repository (more to come):

• Tutorial: Step 1: Automatic detection of supraglacial lakes and surface classification using classic image segmentation methods such as Otsu multi-thresholding and Connected Component Analysis (CCA) on both natural-color imagery and NDWI_ice.
• Tutorial: Step 2: Automatic detection of supraglacial lakes using the AI-based Segment Anything Model (SAM) (Kirillov, A. et al., 2023).
• Tutorial: Conversion of CAMBOTv2 L0 natural-color (RGB) images to single-channel grayscale (luminance) images for use with the Ames Stereo Pipeline (ASP). The tutorial covers different ways of converting RGB to luminance.
• Tutorial: Known challenges for SfM for supraglacial hydrology: Notebook illustrating known challenges for SfM for supraglacial hydrology, such as caustic and lake ice cover.
• Tool: Conversion of CAMBOTv2 GPS antenna positions to the camera's focal plane position for use with the Ames Stereo Pipeline (ASP).
• Tool: Parse ASP camera calibration files using the TSAI distortion model and extract focal lengths $f_{u, v}$ as well as radial ($k_{1, 2}$) and tangential ($p_{1, 2}$) lens distortion parameters (see: ASP frame camera models).
• Tool: Toolbox for converting commonly used Metashape lens calibration formats to NASA's Ames Stereo Pipeline (ASP) Tsai format with tools for analysing a large number of Metashape error statics etc. The toolbox, written by C. Wayne Wright, contains an NBDEV enabled Jupyter notebook, a Python™ library module, and a pip package installer. It runs (and has been tested) in Google Colab, Windows, and Linux/WSL2.

Python™ code currently available in this repository (more to come):

• Parse ASP camera calibration files using the TSAI distortion model and extract intrinsic parameters, such as focal lengths $f_{u, v}$ , radial ($k_{1, 2, 3}$) and tangential lens distortion parameters ($p_{1, 2}$) , as well as extrinsic parameters such as camera pose (see: ASP frame camera models).
• Convert ASP residual output files to GeoPackage (GPKG) for plotting with GIS packages
• Convert ATM HDF5 lidar point clouds: convert ATM HDF5 lidar point clouds to ASCII CSV or GeoPackage (GPKG) for plotting with GIS packages.
• Calculate the index of refraction of water depending on temperature, wavelength, and salinity using Christopher Parrish's (2020) empirical model

Notebooks and repositories related to this project:
Lidar review tools from C. Wayne Wright using ATM supraglacial lake data as example:

• Lake surface detection
• Interactive Bokeh plots of lake surface

Recommended resources:

• Ames Stereo Pipeline user manual
• Ames Stereo Pipeline user group & support forum
• Jupyter-ready docker image with ASP pre-installed from the University of Washington Terrain Analysis and Cryosphere Observation Lab
• Ames Stereo Pipeline tutorials from the University of Washington Terrain Analysis and Cryosphere Observation Lab
• Christopher Parrish's (2020) empirical model for calculating the refractive index of water

Publications relevant to the Structure from Motion (SfM) Bathymetry repository:

• Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA’s Open Source Software for Deriving and Processing Terrain Data, Earth and Space Science, 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.
• Harpold, R., Yungel, J., Linkswiler, M., and Studinger, M.: Intra-scan intersection method for the determination of pointing biases of an airborne altimeter, International Journal of Remote Sensing, 37, 648–668, https://doi.org/10.1080/01431161.2015.1137989, 2016.
• Otsu, N.: A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Syst., Man, Cybern., 9, 62–66, https://doi.org/10.1109/TSMC.1979.4310076, 1979.
• Palaseanu-Lovejoy, M., Alexandrov, O., Danielson, J., and Storlazzi, C.: SaTSeaD: Satellite Triangulated Sea Depth Open-Source Bathymetry Module for NASA Ames Stereo Pipeline, Remote Sensing, 15, 3950, https://doi.org/10.3390/rs15163950, 2023.
• Shean, D. E., Alexandrov, O., Moratto, Z. M., Smith, B. E., Joughin, I. R., Porter, C., and Morin, P.: An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery, ISPRS Journal of Photogrammetry and Remote Sensing, 116, 101–117, https://doi.org/10.1016/j.isprsjprs.2016.03.012, 2016.
• Slocum, R. K., Wright, W., and Parrish, C.: Guidelines for Bathymetric Mapping and Orthoimage Generation using sUAS and SfM, An Approach for Conducting Nearshore Coastal Mapping, https://doi.org/10.25923/07MX-1F93, 2019.
• Studinger, M., Manizade, S. S., Linkswiler, M. A., and Yungel, J. K.: High-resolution imaging of supraglacial hydrological features on the Greenland Ice Sheet with NASA’s Airborne Topographic Mapper (ATM) instrument suite, The Cryosphere, 16, 3649–3668, https://doi.org/10.5194/tc-16-3649-2022, 2022.
• Yang, K. and Smith, L. C.: Internally drained catchments dominate supraglacial hydrology of the southwest Greenland Ice Sheet: Greenland Internally Drained Catchment, J. Geophys. Res. Earth Surf., 121, 1891–1910, https://doi.org/10.1002/2016JF003927, 2016.