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塔吉克斯坦典型草地无人机地上生物量产品

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中亚地区拥有全世界最大的连片草原,塔吉克斯坦作为中亚五国的典型代表,其地表的草地植被状况具有重要研究意义。本研究针对塔吉克斯坦的 5 个草地典型站点于 2022年开展了无人机草地资源调查,数据采样过程严格遵照标准规范进行,基于固定翼无人机于晴朗天气下搭载光学相机同时采集了多光谱与RGB图像,在航拍结束后通过在地面布设样方刈割植被后处理得到地上生物量数据。使用地面实测生物量数据与无人机多光谱以及RGB信息以广义加性模型建立关系,利用交叉验证对模型数据质量进行控制,实现高精度生物量建模,并将生物量点采样扩展到面尺度上,得到生物量产品分辨率为 1m。本数据集可用于中亚典型草原小区域植被生物量的分析和卫星遥感植被产品的多尺度对比校正,有效弥补了地面观测与常规遥感监测的尺度匹配问题。
A dataset of UAV aboveground biomass of typical grassland in Tajikistan
Central Asia boasts the largest contiguous grassland in the world.Tajikistan,as a typical representative of the five Central Asian countries,holds significant research value for studying the status of its grassland vegetation.In this research,we conducted a UAV survey of grassland resources at five typical sites in Tajikistan,adhering strictly to standard specifications for data sampling.Using a fixed-wing UAV equipped with optical cameras,we captured both multi-spectral and RGB images during clear weather conditions.In the following of the aerial photography,we collected aboveground biomass data by placing quadrants on the ground and cutting the vegetation.The ground-measured aboveground biomass and UAV data products were then input into a generalized additive model to build an aboveground biomass inversion model.Cross validation has been used to ensure the quality of the model,resulting in high-precision biomass modeling.Subsequently,the UAV aboveground biomass product can be produced at a resolution of 1m.This dataset can be applied to the analysis of the aboveground biomass in Central Asia's grasslands at local scale.Besides,it can also be used to validate satellite products,contributing to address scale biases between traditional measurements and satellite observations.

UAVabove ground biomass(AGB)multispectralRGBTajikistan

潘天力、叶虎平、张心昱、任小丽、廖小罕、巴音达来、Safarov Mustafo、Okhonniyozov Mekhrovar

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中国科学院地理科学与资源研究所,北京 100101

香港科技大学工程学院,香港 999077

中国民航局民航低空地理信息与航路重点实验室,北京 100101

中国科学院新疆生态与地理研究所,乌鲁木齐 830011

中国科学院中亚生态与环境研究中心,乌鲁木齐 830011

中亚生态与环境研究中心(杜尚别),杜尚别 734024

中国科学院西北生态环境资源研究院,兰州 730000

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无人机 地上生物量 多光谱 RGB 塔吉克斯坦

2024

中国科学数据(中英文网络版)

中国科学数据(中英文网络版)

CSTPCD
ISSN:2096-2223
年,卷(期):2024.9(3)