Preprints
https://doi.org/10.5194/essd-2022-210
https://doi.org/10.5194/essd-2022-210
 
08 Jul 2022
08 Jul 2022
Status: this preprint is currently under review for the journal ESSD.

A 250m annual alpine grassland AGB dataset over the Qinghai-Tibetan Plateau (2000–2019) based on in-situ measurements, UAV images, and MODIS Data

Huifang Zhang1,2,3, Zhonggang Tang2, Binyao Wang2, Hongcheng Kan2, Yi Sun1,2, Yu Qin3, Baoping Meng1,2, Meng Li1,2, Jianjun Chen4, Yanyan Lv1,2, Jianguo Zhang1,2, Shuli Niu5, and Shuhua Yi1,2 Huifang Zhang et al.
  • 1Institute of Fragile Eco-environment, Nantong University, 999 Tongjing Road, Nantong, Jiangsu, 226007, China
  • 2School of Geographic Science, Nantong University, 999 Tongjing Road, Nantong, Jiangsu, 226007, China
  • 3State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, 320 Donggang West Road, Lanzhou 730000, China
  • 4College of Geomatics and Geoinformation, Guilin University of Technology, 12 Jiangan Road, Guilin 541004, China
  • 5Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

Abstract. The alpine grassland ecosystem accounts for 53 % of the Qinghai-Tibet Plateau (QTP) area, which is an important ecological protection barrier, but fragile and highly vulnerable to climate change. Therefore, continuous monitoring of the aboveground biomass (AGB) of grassland is necessary. Although many studies have mapped the spatial distribution of AGB over 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 re-estimate the grassland AGB over the QTP from 2000 to 2019. The innovations were as follows: 1) In the aspect of ground data collection, the spatial scale matching among the traditional ground quadrat sampling, UAV photos, and MODIS pixels was fully considered. From 2015 to 2019, 906 pairs of ground-UAV sample data at the quadrat scale and 2,602 sets of UAV data matching the MODIS pixel scale were collected. A total of more than 37,000 UAV photos were captured at the height of 20 meters. Therefore, the ground validation samples was sufficient and scale matched. 2) In terms of model construction, the traditional quadrat scale (0.25 m2) was successfully upscaled to the MODIS pixel scale (6,2500 m2) based on the random forest method and stepwise upscaling scheme. Compared with previous studies, the scale matching of independent and dependent variables was realized, effectively reducing the impact of scale mismatch. At the pixel scale, the AGB value estimated by UAV had a more linear correlation with the MODIS vegetation indices than the traditional sampling method. The multi-year independent cross-validation results showed that the constructed pixel scale AGB estimation had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g/m2. Our dataset provides an important input parameter for a comprehensive understanding of the QTP in the process of global climate change. The dataset is available from the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Terre.tpdc.272587, Zhang et al., 2022).

Huifang Zhang et al.

Status: open (until 04 Sep 2022)

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Huifang Zhang et al.

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A 250m annual alpine grassland AGB dataset over the Qinghai-Tibetan Plateau (2000-2019) based on in-situ measurements, UAV images, and MODIS Data Huifang Zhang, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Shuli Niu, Shuhua Yi https://doi.org/10.11888/Terre.tpdc.272587

Huifang Zhang et al.

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Short summary
The accuracy of regional grassland AGB is always limited by insufficient ground measurements and large spatial gaps with satellite pixels. This paper used more than 37,000 UAV images as bridges to successfully obtain AGB values matching MODIS pixels. The new AGB estimation model had good robustness with an average R2 of 0.83 and RMSE of 34.13 g/m2. Our new dataset provides important input parameters for a comprehensive understanding of the QTP during global climate change.