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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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 17; číslo 21; s. 3588
Hlavní autoři:	Makarichev, Viktor, Ramirez-Jaime, Andres, Porras-Diaz, Nestor, Vasilyeva, Irina, Lukin, Vladimir, Arce, Gonzalo, Okarma, Krzysztof
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.11.2025
Témata:	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
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Abstract	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.
AbstractList	What are the main findings? * The combination of Golomb–Rice coding with run-length encoding and context-adaptive binary arithmetic coding within the developed block-splitting framework provides a median compression ratio above 24 for HyperHeight LiDAR data. * The proposed methods provide lossless reconstruction and outperform standard NPZ and bit packing when compressing HyperHeight LiDAR data. The combination of Golomb–Rice coding with run-length encoding and context-adaptive binary arithmetic coding within the developed block-splitting framework provides a median compression ratio above 24 for HyperHeight LiDAR data. The proposed methods provide lossless reconstruction and outperform standard NPZ and bit packing when compressing HyperHeight LiDAR data. What are the implications of the main findings? * The proposed approach enables efficient, lossless onboard compression of large LiDAR datasets for satellite missions such as NASA CASALS. * Scalability and data transfer efficiency are improved, supporting real-time environmental monitoring and analysis. The proposed approach enables efficient, lossless onboard compression of large LiDAR datasets for satellite missions such as NASA CASALS. Scalability and data transfer efficiency are improved, supporting real-time environmental monitoring and analysis. 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. 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. What are the main findings? The combination of Golomb–Rice coding with run-length encoding and context-adaptive binary arithmetic coding within the developed block-splitting framework provides a median compression ratio above 24 for HyperHeight LiDAR data. The proposed methods provide lossless reconstruction and outperform standard NPZ and bit packing when compressing HyperHeight LiDAR data. What are the implications of the main findings? The proposed approach enables efficient, lossless onboard compression of large LiDAR datasets for satellite missions such as NASA CASALS. Scalability and data transfer efficiency are improved, supporting real-time environmental monitoring and analysis. 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.
Audience	Academic
Author	Ramirez-Jaime, Andres Lukin, Vladimir Vasilyeva, Irina Arce, Gonzalo Porras-Diaz, Nestor Makarichev, Viktor Okarma, Krzysztof
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Cites_doi	10.1109/MGRS.2020.3048443 10.1109/ICME.2007.4284761 10.1109/MGRS.2014.2352465 10.1109/SSP64130.2025.11073328 10.1201/9781315154381-5 10.1109/IGARSS53475.2024.10642000 10.1109/TGRS.2008.2009316 10.1117/12.3025299 10.3390/rs10030482 10.1109/DSMP.2016.7583539 10.1109/IGARSS46834.2022.9884418 10.2112/JCOASTRES-D-11-00017.1 10.1109/CISA60639.2024.10576270 10.1147/rd.326.0717 10.3390/rs17071215 10.3390/rs17040679 10.1117/1.OE.51.11.111712 10.1007/978-3-319-25691-7_16 10.1016/B978-0-12-809474-7.00001-X 10.3390/rs17050899 10.1007/978-1-84882-903-9 10.3390/s151024926 10.1109/30.125072 10.1109/IGARSS52108.2023.10281919 10.1029/2018EA000506 10.3390/sym11101274 10.1016/j.rse.2019.111262 10.3390/rs11111390 10.1109/IGARSS53475.2024.10641698 10.3390/rs8121039 10.1201/9781315097954 10.1038/s41586-020-2649-2
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SubjectTerms	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
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Title	On the Lossless Compression of HyperHeight LiDAR Forested Landscape Data
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