On the Lossless Compression of HyperHeight LiDAR Forested Landscape Data

Satellite Light Detection and Ranging (LiDAR) systems produce high-resolution data essential for confronting critical environmental challenges like climate change, disaster management, and ecological conservation. A HyperHeight Data Cube (HHDC) is a novel representation of LiDAR data. HHDCs are stru...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 17; no. 21; p. 3588
Main Authors: Makarichev, Viktor, Ramirez-Jaime, Andres, Porras-Diaz, Nestor, Vasilyeva, Irina, Lukin, Vladimir, Arce, Gonzalo, Okarma, Krzysztof
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2025
Subjects:
Algorithms
Analysis
Arithmetic coding
Binary codes
Climate change
Climatic changes
Compression
Compression ratio
context-adaptive binary arithmetic encoding
Data compression
Data structures
Data transfer (computers)
Data transmission
Datasets
Disaster management
Emergency preparedness
Entropy
Environmental monitoring
Environmental research
Height
hyperheight data cube (HHDC)
Ice sheets
Lidar
light detection and ranging (LiDAR)
lossless compression
Mathematical analysis
Neon
Octrees
Optical radar
Photons
Real time
Remote sensing
Research facilities
Rice codes
run-length encoding
Sparsity
Splitting
Tensors
Terrain models
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary:Satellite Light Detection and Ranging (LiDAR) systems produce high-resolution data essential for confronting critical environmental challenges like climate change, disaster management, and ecological conservation. A HyperHeight Data Cube (HHDC) is a novel representation of LiDAR data. HHDCs are structured three-dimensional tensors, where each cell captures the number of photons detected at specific spatial and height coordinates. These data structures preserve the detailed vertical and horizontal information essential for ecological and topographical analyses, particularly Digital Terrain Models and canopy height profiles. In this paper, we investigate lossless compression techniques for large volumes of HHDCs to alleviate constraints on onboard storage, processing resources, and downlink bandwidth. We analyze several methods, including bit packing, Rice coding (RC), run-length encoding (RLE), and context-adaptive binary arithmetic coding (CABAC), as well as their combinations. We introduce the block-splitting framework, which is a simplified version of octrees. The combination of RC with RLE and CABAC within this framework achieves a median compression ratio greater than 24, which is confirmed by the results of processing two large sets of HHDCs simulated using the Smithsonian Environmental Research Center NEON data.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs17213588