Cargando…

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

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 o...

Descripción completa

Detalles Bibliográficos
Autores principales: Zou, Fengli, Hu, Qingwu, Li, Haidong, Lin, Jie, Liu, Yichuan, Sun, Fulin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8801810/
https://www.ncbi.nlm.nih.gov/pubmed/35111172
http://dx.doi.org/10.3389/fpls.2021.760551
_version_ 1784642546216992768
author Zou, Fengli
Hu, Qingwu
Li, Haidong
Lin, Jie
Liu, Yichuan
Sun, Fulin
author_facet Zou, Fengli
Hu, Qingwu
Li, Haidong
Lin, Jie
Liu, Yichuan
Sun, Fulin
author_sort Zou, Fengli
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8801810
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-88018102022-02-01 Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau Zou, Fengli Hu, Qingwu Li, Haidong Lin, Jie Liu, Yichuan Sun, Fulin Front Plant Sci Plant Science 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. Frontiers Media S.A. 2022-01-17 /pmc/articles/PMC8801810/ /pubmed/35111172 http://dx.doi.org/10.3389/fpls.2021.760551 Text en Copyright © 2022 Zou, Hu, Li, Lin, Liu and Sun. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Zou, Fengli
Hu, Qingwu
Li, Haidong
Lin, Jie
Liu, Yichuan
Sun, Fulin
Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau
title Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau
title_full Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau
title_fullStr Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau
title_full_unstemmed Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau
title_short Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau
title_sort dynamic disturbance analysis of grasslands using neural networks and spatiotemporal indices fusion on the qinghai-tibet plateau
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8801810/
https://www.ncbi.nlm.nih.gov/pubmed/35111172
http://dx.doi.org/10.3389/fpls.2021.760551
work_keys_str_mv AT zoufengli dynamicdisturbanceanalysisofgrasslandsusingneuralnetworksandspatiotemporalindicesfusionontheqinghaitibetplateau
AT huqingwu dynamicdisturbanceanalysisofgrasslandsusingneuralnetworksandspatiotemporalindicesfusionontheqinghaitibetplateau
AT lihaidong dynamicdisturbanceanalysisofgrasslandsusingneuralnetworksandspatiotemporalindicesfusionontheqinghaitibetplateau
AT linjie dynamicdisturbanceanalysisofgrasslandsusingneuralnetworksandspatiotemporalindicesfusionontheqinghaitibetplateau
AT liuyichuan dynamicdisturbanceanalysisofgrasslandsusingneuralnetworksandspatiotemporalindicesfusionontheqinghaitibetplateau
AT sunfulin dynamicdisturbanceanalysisofgrasslandsusingneuralnetworksandspatiotemporalindicesfusionontheqinghaitibetplateau