Skip to main content
Dryad logo

A dataset of sulfur content and density of vegetation on the Tibetan Plateau

Citation

Zhao, Wenzong; He, Nianpeng (2022), A dataset of sulfur content and density of vegetation on the Tibetan Plateau, Dryad, Dataset, https://doi.org/10.5061/dryad.44j0zpch1

Abstract

As an important part of China's terrestrial ecosystem, the variation and distribution of sulfur in the vegetation of the Tibetan Plateau (TP) will have a profound impact on the national and even global sulfur cycle. We collected and sorted out the field survey and test data of the research group from 2019 to 2020. This dataset encompasses forest, grassland, shrubland, desert and other major ecosystem types, including the average sulfur content, density and storage data of different vegetation types and plant organs. The establishment of this data set provides important basic data for the assessment of regional vegetation biomass and sulfur reserves and the optimization of sulfur cycle model.

Methods

Field sampling was conducted during July to August in 2019–2020. The Tibetan Plateau (TP) was sampled on a grid basis (0.5° × 0.5° geographic grids), with three randomly set sample squares in each grid. We collected plant samples of different organs (leaves, branches, stems and roots) covering a total of 680  sites of forest (20 m × 20 m), shruband (20 m × 20 m), grassland (1 m × 1 m) and desert (1 m × 1 m)quadrats, and recorded the basic information (longitude, latitude, altitude, etc.) of the sites. These plant samples are classified, cleaned, dried, weighed and ground. An inductively coupled plasma emission spectrometer (ICP-OES) was used to measure the sulfur content in plant samples. Finally, we calculated the sulfur density and storage of different vegetation types.

Funding

National Natural Science Foundation of China, Award: 31988102

National Natural Science Foundation of China, Award: 32001186

the Second Tibetan Plateau Scientific Expedition and Research Program, Award: 2019QZKK060602

National Science and Technology Basic Resources Survey Program of China, Award: 2019FY101300