基于深度学习的青藏高原生态安全格局时空变化与模拟预测

Land Pub Date : 2024-07-17 DOI:10.3390/land13071073
Longqing Liu, Shidong Zhang, Wenshu Liu, Hongjiao Qu, Luo Guo
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摘要

近二十年来,在自然和人为因素的共同作用下,青藏高原的生态环境和资源面临严重威胁,生态系统和居民生活受到深刻影响。因此,建立生态安全格局(ESP)对于应对气候变化、维护生态系统功能和可持续发展至关重要。本研究基于压力-状态-响应(PSR)模型,构建了青田县生态安全(ES)评价指标体系,对2000-2020年青田县生态安全进行了评价,并基于深度学习模型对2025-2035年青田县生态安全进行了预测。结合居民对生态安全的感知,对QTP的生态安全进行了综合评价。结果表明(1)2000-2020年,QTP的ES值持续上升,危险县和敏感县数量减少,其他县数量增加。总体空间分布上,东南部地区ES值较高,西北部和中部地区ES值较低。(2)2000-2020 年,QTP 热点和冷点均有所下降,热点主要集中在以云南省为代表的 QTP 东南部地区,冷点由西向东移动,主要集中在以青海省为代表的 QTP 中部地区。(3)长短期记忆(LSTM)模型具有较高的预测精度。根据 LSTM 预测,2025-2035 年青铜峡市 ES 值将持续上升,安全县数量将达到历史最高水平。空间分布上仍是东南部地区较高,西北部和中部地区较低。(4)通过分析居民对 25 个可能影响 "素质拓展计划 "ES 的潜在因素的认知,结果表明,居民普遍认为这些因素对 "素质拓展计划 "ES 有重要影响,其评价介于 "重要 "和 "非常重要 "之间。此外,這些因素與 ES 的預測值有顯著的相關性。研究结果将有助于提高我们对青田县整体生态环境的认识,为政府制定相关保护策略提供准确定位和合理帮助,为青田县的可持续发展奠定方法论和实践基础。
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Spatiotemporal Changes and Simulation Prediction of Ecological Security Pattern on the Qinghai–Tibet Plateau Based on Deep Learning
Over the past two decades, due to the combined effects of natural and human factors, the ecological environment and resources of the Qinghai–Tibet Plateau (QTP) have faced serious threats, profoundly impacting its ecosystem and the lives of its residents. Therefore, the establishment of the ecological security pattern (ESP) is crucial to cope with climate change, maintain ecosystem function, and sustainable development. Based on the Pressure–State–Response (PSR) model, this study constructed an evaluation index system for the ecological security (ES) of the QTP, evaluated the ES of the QTP during 2000–2020, and predicted the ES of the QTP during 2025–2035 based on the deep learning model. Combined with the residents’ perception of ES, the ES of the QTP was evaluated comprehensively. The results showed that: (1) From 2000 to 2020, the ES value of the QTP continued to rise, the number of dangerous and sensitive counties decreased, and the number of other counties increased. The overall spatial distribution features higher values in the southeast and lower values in the northwest and central regions. (2) From 2000 to 2020, both hot spots and cold spots on the QTP decreased, with the hot spots mainly concentrated in the southeast of the QTP, represented by Yunnan Province, and the cold spots shifting from west to east, mainly concentrated in the central QTP, represented by Qinghai Province. (3) The Long Short-Term Memory (LSTM) model demonstrates high prediction accuracy. Based on the prediction of LSTM, the ES value of the QTP will continue to rise from 2025 to 2035, and the number of safe counties will reach the highest level in history. The spatial distribution is still higher in the southeast and lower in the northwest and central regions. (4) By analyzing residents’ perception of 25 potential factors that may affect the ES of the QTP, the results show that residents generally believe that these factors have an important impact on ES, and their evaluation is between “important” and “very important”. In addition, there is a significant correlation between these factors and the predicted values of ES. The results of the study will help to improve our understanding of the overall ecological environment of the QTP, provide accurate positioning and reasonable help for the government to formulate relevant protection strategies, and lay a methodological and practical foundation for the sustainable development of the QTP.
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