Zou Fengli, Hu Qingwu, Li Haidong, Lin Jie, Liu Yichuan, Sun Fulin
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.
Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, China.
Front Plant Sci. 2022 Jan 17;12:760551. doi: 10.3389/fpls.2021.760551. eCollection 2021.
Grassland is the vegetation type with the widest coverage on the Qinghai-Tibet Plateau. Under the influence of multiple factors, such as global climate change and human activities, grassland is undergoing temporal and spatially different disturbances and changes, and they have a significant impact on the grassland ecosystem of the Qinghai-Tibet Plateau. Therefore, timely and dynamic monitoring of grassland disturbances and distinguishing the reasons for the changes are essential for ecological understanding and management. The purpose of this research is to propose a knowledge-based strategy to realize grassland dynamic distribution mapping and analysis of grassland disturbance changes in the region that are suitable for the Qinghai-Tibet Plateau. The purpose of this study is to propose an analysis algorithm that uses first annual mapping and then establishes temporal disturbance rules, which is applicable to the integrated exploration of disturbance changes in highland-type grasslands. The characteristic indexes of greenness and disturbance indices in the growing period were constructed and integrated with deep neural network learning to dynamically map the grassland for many years. The overall accuracy of grassland mapping was 94.11% and that of Kappa was 0.845. The results show that the area of grassland increased by 11.18% from 2001 to 2017. Then, the grassland disturbance change analysis method is proposed in monitoring the grassland distribution range, and it is found that the area of grassland with significant disturbance change accounts for 10.86% of the total area of the Qinghai-Tibet Plateau, and the disturbance changes are specifically divided into seven types. Among them, the type of degradation after disturbance mainly occurs in Tibet, whereas the main types of vegetation greenness increase in Qinghai and Gansu. At the same time, the study finds that climate change, altitude, and human grazing activities are the main factors affecting grassland disturbance changes in the Qinghai-Tibet Plateau, and there are spatial differences.
草原是青藏高原覆盖范围最广的植被类型。在全球气候变化和人类活动等多种因素影响下，草原正经历着时空各异的干扰与变化，这些对青藏高原的草原生态系统产生了重大影响。因此，及时动态监测草原干扰并区分变化原因对于生态理解和管理至关重要。本研究的目的是提出一种基于知识的策略，以实现适合青藏高原地区的草原动态分布制图及草原干扰变化分析。本研究旨在提出一种先进行年度制图再建立时间干扰规则的分析算法，适用于高原型草原干扰变化的综合探究。构建了生长期绿度特征指标和干扰指数，并与深度神经网络学习相结合，对多年草原进行动态制图。草原制图的总体精度为94.11%，Kappa系数为0.845。结果表明，2001年至2017年草原面积增加了11.18%。然后，在监测草原分布范围时提出了草原干扰变化分析方法，发现干扰变化显著的草原面积占青藏高原总面积的10.86%，干扰变化具体分为七种类型。其中，干扰后退化类型主要发生在西藏，而青海和甘肃植被绿度增加的类型为主。同时，研究发现气候变化、海拔和人类放牧活动是影响青藏高原草原干扰变化的主要因素，且存在空间差异。
