Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. ES Evaluation Model
2.3.1. PSR Framework Construction
2.3.2. ES Comprehensive Index Calculation
- (1)
- Standardization of indicator values
- (2)
- Entropy weight method
- (3)
- Comprehensive index calculation
2.4. Cold Hot Spot Analysis
2.5. ES Prediction
2.6. Design of Human Perception Questionnaire for ES on QTP
2.7. Grey Correlation Analysis
3. Results
3.1. Temporal and Spatial Changes in ES on the QTP
3.2. Spatial Heterogeneity of ES on the QTP
3.3. ES Prediction of QTP
3.3.1. The Comparison of the Predictive Abilities of Different Prediction Models for the ES of the QTP
3.3.2. Prediction of the ES on the QTP by Different Prediction Models
3.4. Analysis of Residents’ Perception of ES of QTP
4. Discussion
4.1. Spatial Heterogeneity of ES on the QTP
4.2. ES Prediction of QTP
4.2.1. Comparison of Predictive Models
4.2.2. Application of Deep Learning Model in This Research
4.3. Policies and Recommendations
4.4. Deficiency and Prospect
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Name | Resolution | Source |
---|---|---|
Land use | 1000 m | Resource and Environmental Science Data Platform (https://www.resdc.cn (accessed on 12 September 2023)) |
GDP | 1000 m | |
Population density | 1000 m | |
Average annual precipitation | 1000 m | |
NDVI | 1000 m | |
DEM | 30 m | Geospatial Data Cloud (https://www.gscloud.cn/ (accessed on 12 September 2023)) |
Soil data | 1000 m | Chinese Soil Dataset (v1.1) of the World Soil Database (HWSD) |
Permafrost rate | 1000 m | National QTP Scientific Data Center (https://data.tpdc.ac.cn/ (accessed on 12 September 2023)) |
Socio-economic data | - | County statistical yearbooks of Tibet, Qinghai, Xinjiang, Gansu, Sichuan, and Yunnan |
Target Layer | Criterion Layer | Index Layer | Stats | Weight |
---|---|---|---|---|
ES in QTP (A) | Pressure | Permafrost ratio/% | - | 0.101266 |
Proportion of primary industry/% | - | 0.063291 | ||
Population/a | - | 0.075949 | ||
State | Slope/° | - | 0.088608 | |
Annual precipitation/mm | + | 0.075949 | ||
Degree of soil erosion | - | 0.063291 | ||
Output of animal husbandry/% | - | 0.088608 | ||
Response | GDP/2020 US$ | - | 0.088608 | |
Proportion of tertiary industry/% | + | 0.088608 | ||
Ecosystem service value/2020 US$ | + | 0.265822 |
Latent Variable | Observed Variable |
---|---|
Social governance | Supervision by public opinion |
Degree of control and treatment of air pollution sources and land desertification | |
Efforts to protect grasslands, wetlands, and natural forests | |
Environmental awareness of businesses | |
The intensity of punishment for environmental violations | |
Government supervision | Emergency plans for natural disasters |
Prevention and control of invasive alien species | |
Government policy support | |
Compensation and transfer payments for ecological protection | |
Social and economic development | Population density |
Level of education of inhabitants | |
Per capita income | |
Tourism’s economic benefit | |
Gross output value of tertiary industry | |
Degree of urbanization | |
Ecological protection | Biological diversity |
Vegetation coverage | |
Number of nature reserves | |
Ecological environmental quality monitoring | |
Ecological protection and high-quality development of rivers and lakes | |
Influence of traditional culture | Number of cultural landscapes |
Intangible cultural heritage projects | |
Impact of traditional animal husbandry on the environment | |
Environmental impact of traditional agriculture | |
Traditional concepts of ecological protection (religious belief, folk culture) |
RMSE | R2 | ||||||
---|---|---|---|---|---|---|---|
LR | RF | CNN | LSTM | LR | RF | CNN | LSTM |
0.02383 | 0.02110 | 0.02243 | 0.02036 | 0.81015 | 0.85124 | 0.83183 | 0.86149 |
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Liu, L.; Zhang, S.; Liu, W.; Qu, H.; Guo, L. Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning. Land 2024, 13, 1073. https://doi.org/10.3390/land13071073
Liu L, Zhang S, Liu W, Qu H, Guo L. Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning. Land. 2024; 13(7):1073. https://doi.org/10.3390/land13071073
Chicago/Turabian StyleLiu, Longqing, Shidong Zhang, Wenshu Liu, Hongjiao Qu, and Luo Guo. 2024. "Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning" Land 13, no. 7: 1073. https://doi.org/10.3390/land13071073
APA StyleLiu, L., Zhang, S., Liu, W., Qu, H., & Guo, L. (2024). Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning. Land, 13(7), 1073. https://doi.org/10.3390/land13071073