WeChat Mini Program
Old Version Features

基于SDR模型的抚仙湖流域生态安全空间分异特征研究

Acta Ecologica Sinica(2023)

云南师范大学能源与环境科学学院

Cited 0|Views11
Abstract
抚仙湖流域作为我国重要的战略水资源储备区,生态安全地位重要.本研究以该流域为研究对象,采用"状态-隐患-响应"模型,探索性空间分析法,基于格网尺度分析其 1987-2020 年生态安全空间分异特征.结果表明:(1)抚仙湖流域生态安全空间差异性明显,生态安全状况以中度安全(Ⅳ级)与高安全(Ⅴ级)状态为主,主要分布于流域四周,生态不安全(Ⅰ级)、较不安全(Ⅱ级)、临界安全(Ⅲ级)成片地集中于流域南北岸及东岸中部的人口农业密集区.(2)研究区生态安全空间集聚效应明显,全局空间自相关系数较高,且逐期上升,集聚效应增强,并以高高(HH),低低(LL)集聚为主,HH区域集中于流域西北,东南,西岸中部片区,LL区域分布于流域北岸人口密集区及南岸的农业地带.(3)研究区生态安全在不同土地利用类型、坡度、人口密度上空间分异规律明显,生态不安全(Ⅰ级)在建设用地分布居多,生态较不安全(Ⅱ级)和生态临界安全(Ⅲ级)以耕地分布为主,生态中度安全(Ⅳ)和高安全(Ⅴ级)主要分布在林地.生态安全分别与坡度、人口密度存在明显的分异特征,坡度增加,生态安全水平高,人口密度变大,生态安全质量越低.
More
Key words
ecological security evaluation,grid scale,SDR model,Fuxian Lake Basin,spatial differentiation
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined