Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jan 17:12:760551.
doi: 10.3389/fpls.2021.760551. eCollection 2021.

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

Fengli Zou  1 , Qingwu Hu  1 , Haidong Li  2 , Jie Lin  3 , Yichuan Liu  1 , Fulin Sun  4
Affiliations
Free PMC article

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

Fengli Zou et al. Front Plant Sci. .
Free PMC article

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.

Keywords: Qinghai-Tibetan Plateau; change analysis; disturbance; grassland; temporal-deep neural network classification.

PubMed Disclaimer

Conflict of interest statement

FS was employed by the company GeoScene Information Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Land cover type and field survey points are located on the Qinghai-Tibet Plateau (A). The location of the Qinghai-Tibet Plateau (B).
FIGURE 2
FIGURE 2
Methodological overview.
FIGURE 3
FIGURE 3
Time profile of annual growth knowledge-based features of NIRv grassland and forest in the Qinghai-Tibet Plateau region.
FIGURE 4
FIGURE 4
Different characteristics of natural land cover types on the Qinghai-Tibet Plateau in GA and DI indicators.
FIGURE 5
FIGURE 5
DNN model training process.
FIGURE 6
FIGURE 6
Flow chart of grassland classification.
FIGURE 7
FIGURE 7
Establish analysis rules for grassland disturbance changes and corresponding change types in DI and GA indices.
FIGURE 8
FIGURE 8
Grassland spatial distribution map from 2001 to 2017.
FIGURE 9
FIGURE 9
The percentage of grassland area in the Qinghai-Tibet Plateau area from 2001 to 2017.
FIGURE 10
FIGURE 10
Distribution map of grassland disturbance change in the Qinghai-Tibet Plateau from 2001 to 2017.
FIGURE 11
FIGURE 11
Changes in temperature and precipitation in Qinghai and Tibetan from 2001 to 2017.
FIGURE 12
FIGURE 12
The annual average DI values of grassland with elevation in different altitudinal zones from 2001 to 2017.
FIGURE 13
FIGURE 13
Correlation between the annual average value of grassland DI in the main distribution areas of grassland disturbance degradation types on the Qinghai-Tibet Plateau and the corresponding annual livestock production.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Ahmed O. S., Wulder M. A., White J. C., Hermosilla T., Coops N. C., Franklin S. E. (2017). Classification of annual non-stand replacing boreal forest change in Canada using Landsat time series: a case study in northern Ontario. Remote Sens. Lett. 8 29–37.
    1. Arino O., Gross D., Ranera F., Bourg L., Weber J. L. (2008). “GlobCover: ESA service for global land cover from MERIS,” in Proceedings of the 2007 IEEE International Geoscience & Remote Sensing Symposium, (Barcelona: ).
    1. Avissar R., Dias P. L. S., Dias M. A. F., Nobre C. (2002). The large-scale biosphere-atmosphere experiment in amazonia (LBA): insights and future research needs. J. Geophys. Res. 107:8086.
    1. Badgley G., Field C. B., Berry J. A. (2017). Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv. 3:e1602244. 10.1126/sciadv.1602244 - DOI - PMC - PubMed
    1. Bartholome E., Belward A. S. (2005). GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26 1959–1977.

LinkOut - more resources