中国草原放牧强度长期高分辨率数据集。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Daju Wang, Qiongyan Peng, Xiangqian Li, Wen Zhang, Xiaosheng Xia, Zhangcai Qin, Peiyang Ren, Shunlin Liang, Wenping Yuan
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引用次数: 0

摘要

放牧是对草原的重大人为干扰,会影响草原的功能和组成,并影响碳预算和温室气体排放。然而,由于缺乏长期的高分辨率放牧强度数据(即单位面积上的牲畜数量),对放牧影响的准确评估受到了限制。本研究利用牲畜普查数据和卫星植被指数,首次建立了中国西部七个牧区省份从 1980 年到 2022 年的长期高分辨率放牧强度(LHGI)数据集。LHGI数据集有效捕捉了放牧强度的空间变化,在73个地点的验证显示相关系数(R2)为0.78。从 1980 年到 2022 年,县级验证的平均 R2 值为 0.73 ± 0.03。该数据集是估算草地碳循环和畜牧系统甲烷排放量的重要资源,也有助于草地管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A long-term high-resolution dataset of grasslands grazing intensity in China.

Grazing is a significant anthropogenic disturbance to grasslands, impacting their function and composition, and affecting carbon budgets and greenhouse gas emissions. However, accurate evaluations of grazing impacts are limited by the absence of long-term high-resolution grazing intensity data (i.e., the number of livestock per unit area). This study utilized census livestock data and a satellite-based vegetation index to develop the first Long-term High-resolution Grazing Intensity (LHGI) dataset of grassland in seven pastoral provinces in western China from 1980 to 2022. The LHGI dataset effectively captured spatial variations in grazing intensity, with validation at 73 sites showing a correlation coefficient (R2) of 0.78. The county-level validation showed an averaged R2 values of 0.73 ± 0.03 from 1980 to 2022. This dataset serves as a vital resource for estimating grassland carbon cycling and livestock system CH4 emissions, as well as contributing to grassland management.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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