2001-2020 年山西省地表物候的时空异质性

IF 2.1 3区 地球科学 Q2 GEOGRAPHY
Haipeng Zhao, Xiangzheng Deng, Zehao Wang
{"title":"2001-2020 年山西省地表物候的时空异质性","authors":"Haipeng Zhao, Xiangzheng Deng, Zehao Wang","doi":"10.1111/tgis.13219","DOIUrl":null,"url":null,"abstract":"Land surface phenology encompasses variations in the life cycle events of plants induced by seasonal changes in environmental factors, primarily meteorological conditions. This study leverages Google Earth Engine to extract a comprehensive time series of two‐band Enhanced Vegetation Index (EVI 2) from Landsat images. Utilizing relatively sparse data spanning from 2001 to 2020, a Bayesian hierarchical model is applied at a 30 m resolution to capture the continuous temporal evolution of phenology. The fitting results of this study demonstrate excellent performance, with annual correlation coefficients consistently exceeding 0.89. The findings indicate that between 2001 and 2020, the Start of Season in Shanxi advanced by an average of 0.79 days per year, the End of Season was delayed by an average of 0.83 days per year, and the Length of Season (LOS) extended by an average of 0.80 days per year. Spatial disparities in phenological periods in Shanxi are evident, with an average LOS of 192 days on 35–36° N and only 122 days on 40–41° N. Below 1200 m, phenological periods exhibit significant changes influenced by human activities, while between 1200 m and 2600 m, LOS shows a weak trend of shortening. Above 2600 m, there is a noticeable reduction in LOS. With an increasing slope, LOS increases from an average of 175 days to 187 days (>25°). This study, utilizing Shanxi as a case study, explores the spatiotemporal evolution characteristics of vegetation phenology, aiming to support fine land management and enhance agricultural productivity.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatial and temporal heterogeneity of land surface phenology in Shanxi Province from 2001 to 2020\",\"authors\":\"Haipeng Zhao, Xiangzheng Deng, Zehao Wang\",\"doi\":\"10.1111/tgis.13219\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Land surface phenology encompasses variations in the life cycle events of plants induced by seasonal changes in environmental factors, primarily meteorological conditions. This study leverages Google Earth Engine to extract a comprehensive time series of two‐band Enhanced Vegetation Index (EVI 2) from Landsat images. Utilizing relatively sparse data spanning from 2001 to 2020, a Bayesian hierarchical model is applied at a 30 m resolution to capture the continuous temporal evolution of phenology. The fitting results of this study demonstrate excellent performance, with annual correlation coefficients consistently exceeding 0.89. The findings indicate that between 2001 and 2020, the Start of Season in Shanxi advanced by an average of 0.79 days per year, the End of Season was delayed by an average of 0.83 days per year, and the Length of Season (LOS) extended by an average of 0.80 days per year. Spatial disparities in phenological periods in Shanxi are evident, with an average LOS of 192 days on 35–36° N and only 122 days on 40–41° N. Below 1200 m, phenological periods exhibit significant changes influenced by human activities, while between 1200 m and 2600 m, LOS shows a weak trend of shortening. Above 2600 m, there is a noticeable reduction in LOS. With an increasing slope, LOS increases from an average of 175 days to 187 days (>25°). This study, utilizing Shanxi as a case study, explores the spatiotemporal evolution characteristics of vegetation phenology, aiming to support fine land management and enhance agricultural productivity.\",\"PeriodicalId\":47842,\"journal\":{\"name\":\"Transactions in GIS\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions in GIS\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1111/tgis.13219\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions in GIS","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1111/tgis.13219","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 0

摘要

地表物候包括由环境因素(主要是气象条件)的季节性变化引起的植物生命周期事件的变化。本研究利用谷歌地球引擎从陆地卫星图像中提取双波段增强植被指数(EVI 2)的综合时间序列。利用 2001 年至 2020 年期间相对稀疏的数据,以 30 米的分辨率应用贝叶斯分层模型来捕捉物候的连续时间演变。该研究的拟合结果表明其性能卓越,年度相关系数一直超过 0.89。研究结果表明,从 2001 年到 2020 年,山西的物候期开始时间平均每年提前 0.79 天,物候期结束时间平均每年推迟 0.83 天,物候期长度(LOS)平均每年延长 0.80 天。山西物候期的空间差异明显,北纬 35-36° 的平均物候期为 192 天,而北纬 40-41° 的平均物候期仅为 122 天。在 1200 米以下,物候期受人类活动的影响变化明显,而在 1200 米至 2600 米之间,LOS 呈微弱的缩短趋势。在 2600 米以上,生命周期明显缩短。随着坡度的增加,LOS 从平均 175 天增加到 187 天(>25°)。本研究以山西为例,探讨了植被物候的时空演变特征,旨在支持土地精细化管理,提高农业生产力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial and temporal heterogeneity of land surface phenology in Shanxi Province from 2001 to 2020
Land surface phenology encompasses variations in the life cycle events of plants induced by seasonal changes in environmental factors, primarily meteorological conditions. This study leverages Google Earth Engine to extract a comprehensive time series of two‐band Enhanced Vegetation Index (EVI 2) from Landsat images. Utilizing relatively sparse data spanning from 2001 to 2020, a Bayesian hierarchical model is applied at a 30 m resolution to capture the continuous temporal evolution of phenology. The fitting results of this study demonstrate excellent performance, with annual correlation coefficients consistently exceeding 0.89. The findings indicate that between 2001 and 2020, the Start of Season in Shanxi advanced by an average of 0.79 days per year, the End of Season was delayed by an average of 0.83 days per year, and the Length of Season (LOS) extended by an average of 0.80 days per year. Spatial disparities in phenological periods in Shanxi are evident, with an average LOS of 192 days on 35–36° N and only 122 days on 40–41° N. Below 1200 m, phenological periods exhibit significant changes influenced by human activities, while between 1200 m and 2600 m, LOS shows a weak trend of shortening. Above 2600 m, there is a noticeable reduction in LOS. With an increasing slope, LOS increases from an average of 175 days to 187 days (>25°). This study, utilizing Shanxi as a case study, explores the spatiotemporal evolution characteristics of vegetation phenology, aiming to support fine land management and enhance agricultural productivity.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Transactions in GIS
Transactions in GIS GEOGRAPHY-
CiteScore
4.60
自引率
8.30%
发文量
116
期刊介绍: Transactions in GIS is an international journal which provides a forum for high quality, original research articles, review articles, short notes and book reviews that focus on: - practical and theoretical issues influencing the development of GIS - the collection, analysis, modelling, interpretation and display of spatial data within GIS - the connections between GIS and related technologies - new GIS applications which help to solve problems affecting the natural or built environments, or business
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信