气候变化领域集成服务门户(CCPortal): A 250 m annual alpine grassland AGB dataset over the Qinghai-Tibet Plateau (2000-2019) in China based on in situ measurements, UAV photos, and MODIS data

CCPortal

DOI	10.5194/essd-15-821-2023
	A 250 m annual alpine grassland AGB dataset over the Qinghai-Tibet Plateau (2000-2019) in China based on in situ measurements, UAV photos, and MODIS data
	Zhang, Huifang; Tang, Zhonggang; Wang, Binyao; Kan, Hongcheng; Sun, Yi; Qin, Yu; Meng, Baoping; Li, Meng; Chen, Jianjun; Lv, Yanyan; Zhang, Jianguo; Niu, Shuli; Yi, Shuhua
发表日期	2023
ISSN	1866-3508
EISSN	1866-3516
起始页码	821
结束页码	846
卷号	15 期号:2
英文摘要	The alpine grassland ecosystem accounts for 53 % of the Qinghai-Tibet Plateau (QTP) area and is an important ecological protection barrier, but it is fragile and vulnerable to climate change. Therefore, continuous monitoring of grassland aboveground biomass (AGB) is necessary. Although many studies have mapped the spatial distribution of AGB for the QTP, the results vary widely due to the limited ground samples and mismatches with satellite pixel scales. This paper proposed a new algorithm using unmanned aerial vehicles (UAVs) as a bridge to estimate the grassland AGB on the QTP from 2000 to 2019. The innovations were as follows: (1) in terms of ground data acquisition, spatial-scale matching among the traditional ground samples, UAV photos, and MODIS pixels was considered. A total of 906 pairs between field-harvested AGB and UAV sub-photos and 2602 sets of MODIS pixel-scale UAV data (over 37 000 UAV photos) were collected during 2015-2019. Therefore, the ground validation samples were sufficient and scale-matched. (2) In terms of model construction, the traditional quadrat scale (0.25 m(2)) was successfully upscaled to the MODIS pixel scale (62 500 m2) based on the random forest and stepwise upscaling methods. Compared with previous studies, the scale matching of independent and dependent variables was achieved, effectively reducing the impact of spatial-scale mismatch. The results showed that the correlation between the AGB values estimated by UAV and MODIS vegetation indices was higher than that between field-measured AGB and MODIS vegetation indices at the MODIS pixel scale. The multi-year validation results showed that the constructed MODIS pixel-scale AGB estimation model had good robustness, with an average R-2 of 0.83 and RMSE of 34.13 g m(-2). Our dataset provides an important input parameter for a comprehensive understanding of the role of the QTP under global climate change. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (; H. Zhang et al., 2022).
英文关键词	ABOVEGROUND BIOMASS; VEGETATION INDEXES; GEOSTATISTICAL APPROACH; AUTOMATED CROP; IMAGERY; SOIL; ALGORITHMS; PATTERN; COVER; LEAF
WOS研究方向	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000930551700001
来源期刊	EARTH SYSTEM SCIENCE DATA
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283386
作者单位	Nantong University; Nantong University; Chinese Academy of Sciences; Guilin University of Technology; Chinese Academy of Sciences; Institute of Geographic Sciences & Natural Resources Research, CAS
推荐引用方式 GB/T 7714	Zhang, Huifang,Tang, Zhonggang,Wang, Binyao,et al. A 250 m annual alpine grassland AGB dataset over the Qinghai-Tibet Plateau (2000-2019) in China based on in situ measurements, UAV photos, and MODIS data[J],2023,15(2).
APA	Zhang, Huifang.,Tang, Zhonggang.,Wang, Binyao.,Kan, Hongcheng.,Sun, Yi.,...&Yi, Shuhua.(2023).A 250 m annual alpine grassland AGB dataset over the Qinghai-Tibet Plateau (2000-2019) in China based on in situ measurements, UAV photos, and MODIS data.EARTH SYSTEM SCIENCE DATA,15(2).
MLA	Zhang, Huifang,et al."A 250 m annual alpine grassland AGB dataset over the Qinghai-Tibet Plateau (2000-2019) in China based on in situ measurements, UAV photos, and MODIS data".EARTH SYSTEM SCIENCE DATA 15.2(2023).