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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F24%3A00602202" target="_blank" >RIV/86652079:_____/24:00602202 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14310/24:00137665

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2352340924010679?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352340924010679?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.dib.2024.111105" target="_blank" >10.1016/j.dib.2024.111105</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 highresolution 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 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 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 combinaThis is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Název v anglickém jazyce

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

Popis výsledku anglicky

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 highresolution 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 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 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 combinaThis is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

<a href="/cs/project/EH22_008%2F0004635" target="_blank" >EH22_008/0004635: AdAgriF - Pokročilé metody redukce emisí a sekvestrace skleníkových plynů v zemědělské a lesní krajině pro mitigaci změny klimatu</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Data in Brief

ISSN

2352-3409

e-ISSN

2352-3409

Svazek periodika

57

Číslo periodika v rámci svazku

DEC

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

111105

Kód UT WoS článku

001359597600001

EID výsledku v databázi Scopus

2-s2.0-85209147920