High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forestsNational Repository
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 correspon...
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Elsevier
2024-12-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924010679 |
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| author | Tomáš Hanousek Terézia Slanináková Tomáš Rebok Růžena Janoutová |
| author_facet | Tomáš Hanousek Terézia Slanináková Tomáš Rebok Růžena Janoutová |
| author_sort | Tomáš Hanousek |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-42ba00c73829404bb837e610bd043c02 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-42ba00c73829404bb837e610bd043c022024-11-17T04:52:03ZengElsevierData in Brief2352-34092024-12-0157111105High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forestsNational RepositoryTomáš Hanousek0Terézia Slanináková1Tomáš Rebok2Růžena Janoutová3Global Change Research Institute of the Czech Academy of Sciences, Bělidla 986/4a, 603 00 Brno, Czech Republic; Department of Geography, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic; Corresponding author at: Global Change Research Institute of the Czech Academy of Sciences, Bělidla 986/4a, 603 00 Brno, Czech Republic.Institute of Computer Science, Masaryk University, Šumavská 416/15, 602 00 Brno, Czech RepublicInstitute of Computer Science, Masaryk University, Šumavská 416/15, 602 00 Brno, Czech RepublicGlobal Change Research Institute of the Czech Academy of Sciences, Bělidla 986/4a, 603 00 Brno, Czech RepublicAccurate 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.http://www.sciencedirect.com/science/article/pii/S2352340924010679LUTRadiative transfer modelDARTMachine learning modelSynthetic spectral dataLeaf traits |
| spellingShingle | Tomáš Hanousek Terézia Slanináková Tomáš Rebok 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 forestsNational Repository Data in Brief LUT Radiative transfer model DART Machine learning model Synthetic spectral data Leaf traits |
| 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 forestsNational Repository |
| title_full | High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forestsNational Repository |
| title_fullStr | High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forestsNational Repository |
| title_full_unstemmed | High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forestsNational Repository |
| title_short | High spatial and spectral resolution dataset of hyperspectral look-up tables for 3.5 million traits and structural combinations of Central European temperate broadleaf forestsNational Repository |
| title_sort | high spatial and spectral resolution dataset of hyperspectral look up tables for 3 5 million traits and structural combinations of central european temperate broadleaf forestsnational repository |
| topic | LUT Radiative transfer model DART Machine learning model Synthetic spectral data Leaf traits |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924010679 |
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