中国植被覆盖度时空演变及其对气候变化和城市化的响应
Arid Land Geography（2023）
哈尔滨工业大学 | 哈尔滨工业大学(深圳)
Abstract
植被覆盖变化不仅与气候因子密切相关,而且也受人类活动的影响.目前,从省级尺度研究中国植被时空变化特征以及定量分析气候因子结合人类活动对植被覆盖影响研究仍较少.基于Google Earth Engine(GEE)平台和2000—2020年Landsat数据及同期气候与夜间灯光数据,采用像元二分法、线性回归分析、变异系数、偏相关分析和贡献度模型等方法对中国植被覆盖度时空演变及其对气候变化和城市化的响应进行了分析.结果表明:(1)2000—2020年中国植被覆盖度以0.32％·a-1的速率增长.植被覆盖区域以高覆盖度为主,面积占研究区域的38％,总体呈现从东南至西北递减的趋势.(2)黄土高原、云南省、西藏自治区和新疆维吾尔自治区西部植被覆盖度呈现增长趋势.植被年际波动在南部比北部、东部比西部稳定.黑龙江省植被覆盖度最高,为91.7％;新疆维吾尔自治区最低,为14.4％;宁夏回族自治区植被覆盖度以0.98％·a-1的速率增长,植被得到显著改善.(3)气候因子和城市化对植被覆盖度的影响存在明显空间差异性.气温和降水量对中国北部地区植被覆盖度的影响分别为负相关和正相关,城市化主要影响经济较为发达的省份.气温是宁夏回族自治区的主要贡献因子,平均贡献度为84.3％;降水量是台湾省的主要贡献因子,平均贡献度为71.7％;城市化贡献度最大的城市为上海,平均贡献度为26.5％.
More
PPT
View via Publisher
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 performanceWe propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generationThe 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 ChineseExperimental 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 performanceUpload 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
