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Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau

ABSTRACT: 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.

SUBMITTER: Zou F

PROVIDER: S-EPMC8801810 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe Qinghai-Tibet Plateau is the "sensitive area" of climate change, and also the "driver" and "amplifier" of global change. The response and feedback of its carbon dynamics to climate change will significantly affect the content of greenhouse gases in the atmosphere. However, due to the unique geographical environment characteristics of the Qinghai-Tibet Plateau, there is still much controversy about its carbon source and sink estimation results. This study designed a new algorithm based on machine learning to improve the accuracy of carbon source and sink estimation by integrating multiple scale carbon input (net primary productivity, NPP) and output (soil heterotrophic respiration, Rh) information from remote sensing and ground observations. Then, we compared spatial patterns of NPP and Rh derived from the fusion of multiple scale data with other widely used products and tried to quantify the differences and uncertainties of carbon sink simulation at a regional scale.ResultsOur results indicate that although global warming has potentially increased the Rh of the Qinghai-Tibet Plateau, it will also increase its NPP, and its current performance is a net carbon sink area (carbon sink amount is 22.3 Tg C/year). Comparative analysis with other data products shows that CASA, GLOPEM, and MODIS products based on remote sensing underestimate the carbon input of the Qinghai-Tibet Plateau (30-70%), which is the main reason for the severe underestimation of the carbon sink level of the Qinghai-Tibet Plateau (even considered as a carbon source).ConclusionsThe estimation of the carbon sink in the Qinghai-Tibet Plateau is of great significance for ensuring its ecological barrier function. It can deepen the community's understanding of the response to climate change in sensitive areas of the plateau. This study can provide an essential basis for assessing the uncertainty of carbon sources and sinks in the Qinghai-Tibet Plateau, and also provide a scientific reference for helping China achieve "carbon neutrality" by 2060.

Project description:Assessing ecosystem vulnerability to climate change is critical for sustainable and adaptive ecosystem management. Alpine grasslands on the Qinghai-Tibetan Plateau are considered to be vulnerable to climate change, yet the ecosystem tends to maintain stability by increasing resilience and decreasing sensitivity. To date, the spatial pattern of grassland vulnerability to climate change and the mechanisms that vegetation applies to mitigate the impacts of climate change on grasslands by altering relevant ecosystem characteristics, especially sensitivity and resilience, remain unknown. In this study, we first assessed the spatial pattern of grassland vulnerability to climate change by integrating exposure, sensitivity, and resilience simultaneously, and then identified its driving forces. The results show that grasslands with high vulnerability were mainly located on the edges of the plateau, whereas alpine grasslands in the hinterlands of the plateau showed a low vulnerability. This spatial pattern of alpine grassland vulnerability was controlled by climatic exposure, and grassland sensitivity and resilience to climate change might also exacerbate or alleviate the degree of vulnerability. Climate change had variable impacts on different grassland types. Desert steppes were more vulnerable to climate change than alpine meadows and alpine steppes because of the high variability in environmental factors and their low ability to recover from perturbations. Our findings also confirm that grazing intensity, a quantitative index of the most important human disturbance on alpine grasslands in this plateau, was significantly correlated with ecosystem vulnerability. Moderate grazing intensity was of benefit for increasing grassland resilience and then subsequently reducing grassland vulnerability. Thus, this study suggests that future assessments of ecosystem vulnerability should not ignore anthropogenic disturbances, which might benefit environmental protection and sustainable management of grasslands on the Qinghai-Tibetan Plateau.

Project description:This study focuses on the vegetation dynamic caused by global environmental change in the eastern margin of the Qinghai-Tibet Plateau (EMQTP). The Qinghai-Tibet Plateau (QTP) is one of the most sensitive areas responding to global environmental change, particularly global climate change, and has been recognized as a hotspot for coupled studies on changes in global terrestrial ecosystems and global climates. An important component of terrestrial ecosystems, vegetation dynamic has become a key issue in global environmental change, and numerous case studies have been conducted on vegetation dynamic trends using multi-source data and multi-scale methods across different study periods. The EMQTP is regarded as a transitional area located between the QTP and the Sichuan basin, and has special geographical and climatic conditions. Although this area is ecologically fragile and sensitive to climate change, few studies about vegetation dynamics have been carried out in this area. Thus, in this study, we used long-term series datasets of GIMMS 3g NDVI and VGT/PROBA-V NDVI to analyze the vegetation dynamics and phenological changes from 1982 to 2018. Validation was performed based on Landsat NDVI and Vegetation Index & Phenology (VIP) data. The results reveal that the year 1998 was a vital turning point in the start of growing season (SGS) in vegetation ecosystems. Before this turning point, the SGS had an average slope of 9.2 days/decade, and after, the average slope was 3.9 days/decade. The length of growing season (LGS) was slightly prolonged between 1982 to 2015. Additionally, the largest national alpine wetland grassland experienced significant vegetation degradation; in autumn, the degraded area accounted for 63.4%. Vegetation degradation had also appeared in the arid valleys of the Yalong River and the Jinsha River. Through validation analysis, we found that the main causes of vegetation degradation are the natural degradation of wetland grassland and human activities, specifically agricultural development and residential area expansion.

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Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau

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