DOI: 10.57760/sciencedb.j00173.00014
title: Vegetation Height Prediction Dataset for Mountainous Forest Regions
title: 面向山地森林区域的植被高度预测数据集

description: Solemnly Declare: when using this data set to publish papers, books and other works, you must formally quote the papers to which this data set belongs:Citation: YU Cuilin, ZHONG Zixuan, PANG Hongyi, DING Yusheng, LAI Tao, Huang Haifeng, WANG Qingsong. Vegetation Height Prediction Dataset Oriented to Mountainous Forest Areas[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250941Authors: YU Cuilin, ZHONG Zixuan, PANG Hongyi, DING Yusheng, LAI Tao, Huang Haifeng, WANG QingsongAuthor Unit:School of Electronics and Communication Engineering, Sun Yat-sen UniversitySchool of Electronic Science and Engineering, Xiamen UniversityCorrespondent: WANG Qingsong,　wangqs5@mail.sysu.edu.cnOriginal link: 面向山地森林区域的植被高度预测数据集Funds: T The National Natural Science Foundation of China (62273365), Xiaomi Young Talents ProgramAbstract:   The Vegetation Height Prediction Dataset for Mountainous Forest Regions (VHP-Dataset) is a multi-source, standardized remote sensing dataset for supervised learning modeling. Using canopy height (RH95) from the GEDI L2A lidar product as the target variable, it integrates multi-source data including Landsat 8 multispectral imagery, the AW3D30 digital elevation model, CGLS-LC100 vegetation cover type data, and GFCC30TC vegetation cover data to construct an 18-dimensional input feature system encompassing spatial location, spectral features, normalization index, topographic structure, and vegetation cover information. Covering multiple typical mountainous forest regions, this dataset effectively characterizes vegetation height variations under complex terrain and diverse forest structures through unified spatial and elevation benchmarks, rigorous GEDI spot quality control, and consistent multi-source feature construction. System experiments demonstrate that the VHP-Dataset stably supports various machine learning and deep learning methods for cross-regional vegetation height prediction, providing a reliable standardized data foundation for mountainous forest canopy height inversion and model comparison studies.
description: Solemnly Declare: when using this data set to publish papers, books and other works, you must formally quote the papers to which this data set belongs:Citation: YU Cuilin, ZHONG Zixuan, PANG Hongyi, DING Yusheng, LAI Tao, Huang Haifeng, WANG Qingsong. Vegetation Height Prediction Dataset Oriented to Mountainous Forest Areas[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250941Authors: YU Cuilin, ZHONG Zixuan, PANG Hongyi, DING Yusheng, LAI Tao, Huang Haifeng, WANG QingsongAuthor Unit:School of Electronics and Communication Engineering, Sun Yat-sen UniversitySchool of Electronic Science and Engineering, Xiamen UniversityCorrespondent: WANG Qingsong,　wangqs5@mail.sysu.edu.cnOriginal link: 面向山地森林区域的植被高度预测数据集Funds: T The National Natural Science Foundation of China (62273365), Xiaomi Young Talents ProgramAbstract:   The Vegetation Height Prediction Dataset for Mountainous Forest Regions (VHP-Dataset) is a multi-source, standardized remote sensing dataset for supervised learning modeling. Using canopy height (RH95) from the GEDI L2A lidar product as the target variable, it integrates multi-source data including Landsat 8 multispectral imagery, the AW3D30 digital elevation model, CGLS-LC100 vegetation cover type data, and GFCC30TC vegetation cover data to construct an 18-dimensional input feature system encompassing spatial location, spectral features, normalization index, topographic structure, and vegetation cover information. Covering multiple typical mountainous forest regions, this dataset effectively characterizes vegetation height variations under complex terrain and diverse forest structures through unified spatial and elevation benchmarks, rigorous GEDI spot quality control, and consistent multi-source feature construction. System experiments demonstrate that the VHP-Dataset stably supports various machine learning and deep learning methods for cross-regional vegetation height prediction, providing a reliable standardized data foundation for mountainous forest canopy height inversion and model comparison studies.