Spatiotemporal prediction of alpine wetlands under multi-climate scenarios in the west of Sichuan, China

The Kappa coefficients for Landsat 8 and Sentinel 2 were 0.89 and 0.91, respectively. Among the four SDMs, MAXENT achieved a higher accuracy (α = 91.6%) for the actual wetland compared to the thematic overlay analysis. The area under the curve (AUC) of the MAXENT model simulations for wetland spatial distribution were all greater than 0.80. This suggests that incorporating the SDM model into land change simulations has high generalizability and significant advantages on a large scale. Furthermore, simulation results reveal that between 2021 and 2100 years, with increasing emission concentrations, highly suitable areas for wetland development exhibit significant spatial differentiation. In particular, wetland areas in high-altitude regions are expected to increase, while low-altitude regions will markedly shrink. The changes in the future spatial distribution of wetlands show a high level of consistency with historical climate changes, with warming being the main driving force behind the spatiotemporal changes in alpine wetlands in western Sichuan, especially evident in the central high-altitude and northern low-altitude areas.

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Publication Details

Journal
PeerJ
Published
2024-11-27
DOI
https://doi.org/10.7717/peerj.18586
Citations
2
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
1.84
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article

Spatiotemporal prediction of alpine wetlands under multi-climate scenarios in the west of Sichuan, China

Haijun Wang, Ji Luo, Onanong Phewnil, Xiangdong Kong et al.
2 citations
PeerJ
Species Distribution and Climate Change
1.84
article

Spatiotemporal prediction of alpine wetlands under multi-climate scenarios in the west of Sichuan, China

Haijun Wang, Ji Luo, Onanong Phewnil, Xiangdong Kong, Tianhui Xie, Pengju Li, Xiyong Chen
article en
2 citations

Abstract

The Kappa coefficients for Landsat 8 and Sentinel 2 were 0.89 and 0.91, respectively. Among the four SDMs, MAXENT achieved a higher accuracy (α = 91.6%) for the actual wetland compared to the thematic overlay analysis. The area under the curve (AUC) of the MAXENT model simulations for wetland spatial distribution were all greater than 0.80. This suggests that incorporating the SDM model into land change simulations has high generalizability and significant advantages on a large scale. Furthermore, simulation results reveal that between 2021 and 2100 years, with increasing emission concentrations, highly suitable areas for wetland development exhibit significant spatial differentiation. In particular, wetland areas in high-altitude regions are expected to increase, while low-altitude regions will markedly shrink. The changes in the future spatial distribution of wetlands show a high level of consistency with historical climate changes, with warming being the main driving force behind the spatiotemporal changes in alpine wetlands in western Sichuan, especially evident in the central high-altitude and northern low-altitude areas.

PeerJVol. 12
Kasetsart University (TH), Sichuan University of Science and Engineering (CN)
Climate action
Openalex Percentile: Top 11%
Species Distribution and Climate Change
1.84
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Spatiotemporal prediction of alpine wetlands under multi-climate scenarios in the west of Sichuan, China — Haijun Wang, Ji Luo, et al. · PeerJ (2024) | TGRS Research Map | TGRS