High spatial and spectral resolution dataset of hyperspectral
look-up tables for 3.5 million traits and structural
combinations of Central European temperate broadleaf forests

J 2024

High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forests

HANOUSEK, Tomáš, Terézia SLANINÁKOVÁ, Tomáš REBOK a Růžena JANOUTOVÁ

Základní údaje

Originální název

High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forests

Autoři

HANOUSEK, Tomáš, Terézia SLANINÁKOVÁ, Tomáš REBOK a Růžena JANOUTOVÁ

Vydání

DATA IN BRIEF, NETHERLANDS, ELSEVIER, 2024, 2352-3409

Další údaje

Typ výsledku

Článek v odborném periodiku

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 1.200 v roce 2022

DOI

http://dx.doi.org/10.1016/j.dib.2024.111105

UT WoS

WOS:00135959760

Klíčová slova anglicky

LUT;Radiative transfer model;DART;Machine learning model;Synthetic spectral data;Leaf traits;Hyperspectral data

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 12. 2024 09:27, Mgr. Tomáš Hanousek

Anotace

V originále

Accurate retrieval of forest functional traits from remote sensing data is critical for monitoring forest health and productivity. To achieve sufficient accuracy using inverse methods it is essential to have representative database of simulated or measured spectral properties together with corresponding forest traits. However, existing datasets are often limited in scope, covering specific sites and times with simplified structures. This limitation hinders the development of generalizable machine learning models for trait prediction. To address this issue, we present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests. The dataset includes 3.5 million unique combinations of leaf biochemical and canopy structural characteristics of forest scenes together with a variety of sun geometry. The spectral data cover wavelengths from 450 nm to 2300 nm, with a resolution of 2 nm. The dataset is organised into two files: one capturing the average reflectance of all scene pixels and another focusing solely on sunlit leaf pixels. LUT were generated using the Discrete Anisotropic Radiative Transfer model version 5.10.0. Virtual forest scenes were based on 3D tree representations derived from Terrestrial Laser Scanning of European beech trees, adjusted to various leaf area index values and structural configurations to simulate natural forest variability. The reflectance data were processed using MATLAB and Python scripts, resulting in hyperspectral cubes that were processed to generate the LUT. The dataset can be used to train machine learning models, such as Random Forest and Support Vector Machines, for predicting forest functional traits and assisting in the calibration of remote sensing algorithms. The biggest advantage of the dataset is high spectral and spatial resolution, together with the high number of different trait combinations, which allows for adaptability to different times, locations, and hyper- and multispectral sensors, and can support up-coming hyperspectral satellite missions. ESA Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and NASA Surface Biology and Geology (SBG) future satellite missions can utilise this dataset to develop their product processors for monitoring forest traits.

Návaznosti

LM2023048, projekt VaV

Název: Česká infrastruktura sledování uhlíku

MUNI/A/1323/2022, interní kód MU

Název: Environmentální a socioekonomické změny v geografickém výzkumu

Investor: Masarykova univerzita, Environmentální a socioekonomické změny v geografickém výzkumu

MUNI/A/1469/2023, interní kód MU

Název: Geografický výzkum společenských a přírodních procesů v období změn

Investor: Masarykova univerzita, Geografický výzkum společenských a přírodních procesů v období změn

726/2023, interní kód MU

Název: ENVision: platforma pro analýzu přírodních ekosystémů s využitím leteckých a satelitních dat

Investor: CESNET, ENVision: platforma pro analýzu přírodních ekosystémů s využitím leteckých a satelitních dat

90254, velká výzkumná infrastruktura

Název: e-INFRA CZ II

90255, velká výzkumná infrastruktura

Název: ELIXIR CZ III

Citovat

HANOUSEK, Tomáš, Terézia SLANINÁKOVÁ, Tomáš REBOK a Růžena JANOUTOVÁ. High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forests. DATA IN BRIEF. NETHERLANDS: ELSEVIER, 2024, roč. 57, December 2024, 13 s. ISSN 2352-3409. Dostupné z: https://dx.doi.org/10.1016/j.dib.2024.111105.

@article{2452300,
   author = {Hanousek, Tomáš and Slanináková, Terézia and Rebok, Tomáš and Janoutová, Růžena},
   article_location = {NETHERLANDS},
   article_number = {December 2024},
   doi = {http://dx.doi.org/10.1016/j.dib.2024.111105},
   keywords = {LUT;Radiative transfer model;DART;Machine learning model;Synthetic spectral data;Leaf traits;Hyperspectral data},
   issn = {2352-3409},
   journal = {DATA IN BRIEF},
   title = {High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forests},
   url = {https://www.sciencedirect.com/science/article/pii/S2352340924010679},
   volume = {57},
   year = {2024}
}

TY  - JOUR
ID  - 2452300
AU  - Hanousek, Tomáš - Slanináková, Terézia - Rebok, Tomáš - Janoutová, Růžena
PY  - 2024
TI  - High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forests
JF  - DATA IN BRIEF
VL  - 57
IS  - December 2024
PB  - ELSEVIER
SN  - 23523409
KW  - LUT;Radiative transfer model;DART;Machine learning model;Synthetic spectral data;Leaf traits;Hyperspectral data
UR  - https://www.sciencedirect.com/science/article/pii/S2352340924010679
N2  - Accurate retrieval of forest functional traits from remote sensing data is critical for monitoring forest health and productivity. To achieve sufficient accuracy using inverse methods it is essential to have representative database of simulated or measured spectral properties together with corresponding forest traits. However, existing datasets are often limited in scope, covering specific sites and times with simplified structures. This limitation hinders the development of generalizable machine learning models for trait prediction. To address this issue, we present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests. The dataset includes 3.5 million unique combinations of leaf biochemical and canopy structural characteristics of forest scenes together with a variety of sun geometry. The spectral data cover wavelengths from 450 nm to 2300 nm, with a resolution of 2 nm. The dataset is organised into two files: one capturing the average reflectance of all scene pixels and another focusing solely on sunlit leaf pixels. LUT were generated using the Discrete Anisotropic Radiative Transfer model version 5.10.0. Virtual forest scenes were based on 3D tree representations derived from Terrestrial Laser Scanning of European beech trees, adjusted to various leaf area index values and structural configurations to simulate natural forest variability. The reflectance data were processed using MATLAB and Python scripts, resulting in hyperspectral cubes that were processed to generate the LUT. The dataset can be used to train machine learning models, such as Random Forest and Support Vector Machines, for predicting forest functional traits and assisting in the calibration of remote sensing algorithms. The biggest advantage of the dataset is high spectral and spatial resolution, together with the high number of different trait combinations, which allows for adaptability to different times, locations, and hyper- and multispectral sensors, and can support up-coming hyperspectral satellite missions. ESA Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and NASA Surface Biology and Geology (SBG) future satellite missions can utilise this dataset to develop their product processors for monitoring forest traits.
ER  -

HANOUSEK, Tomáš, Terézia SLANINÁKOVÁ, Tomáš REBOK a Růžena JANOUTOVÁ. High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forests. \textit{DATA IN BRIEF}. NETHERLANDS: ELSEVIER, 2024, roč.~57, December 2024, 13 s. ISSN~2352-3409. Dostupné z: https://dx.doi.org/10.1016/j.dib.2024.111105.

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