• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

通过人工神经网络揭示与土地利用/土地覆盖变化相关的热特征动态。

Unveiling the Dynamics of Thermal Characteristics Related to LULC Changes via ANN.

作者信息

Khachoo Yasir Hassan, Cutugno Matteo, Robustelli Umberto, Pugliano Giovanni

机构信息

Department of Engineering, University of Naples Parthenope, 80143 Naples, Italy.

University of Benevento Giustino Fortunato, 82100 Benevento, Italy.

出版信息

Sensors (Basel). 2023 Aug 7;23(15):7013. doi: 10.3390/s23157013.

DOI:10.3390/s23157013
PMID:37571796
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10422488/
Abstract

Continuous and unplanned urbanization, combined with negative alterations in land use land cover (LULC), leads to a deterioration of the urban thermal environment and results in various adverse ecological effects. The changes in LULC and thermal characteristics have significant implications for the economy, climate patterns, and environmental sustainability. This study focuses on the Province of Naples in Italy, examining LULC changes and the Urban Thermal Field Variance Index (UTFVI) from 1990 to 2022, predicting their distributions for 2030. The main objectives of this research are the investigation of the future seasonal thermal characteristics of the study area by characterizing land surface temperature (LST) through the UTFVI and analyzing LULC dynamics along with their correlation. To achieve this, Landsat 4-5 Thematic Mapper (TM) and Landsat 9 Operational Land Imager (OLI) imagery were utilized. LULC classification was performed using a supervised satellite image classification system, and the predictions were carried out using the cellular automata-artificial neural network (CA-ANN) algorithm. LST was calculated using the radiative transfer equation (RTE), and the same CA-ANN algorithm was employed to predict UTFVI for 2030. To investigate the multi-temporal correlation between LULC and UTFVI, a cross-tabulation technique was employed. The study's findings indicate that between 2022 and 2030, there will be a 9.4% increase in built-up and bare-land areas at the expense of the vegetation class. The strongest UTFVI zone during summer is predicted to remain stable from 2022 to 2030, while winter UTFVI shows substantial fluctuations with a 4.62% decrease in the none UTFVI zone and a corresponding increase in the strongest UTFVI zone for the same period. The results of this study reveal a concerning trend of outward expansion in the built-up area of the Province of Naples, with central northern regions experiencing the highest growth rate, predominantly at the expense of vegetation cover. These predictions emphasize the urgent need for proactive measures to preserve and protect the diminishing vegetation cover, maintaining ecological balance, combating the urban heat island effect, and safeguarding biodiversity in the province.

摘要

持续且无规划的城市化,再加上土地利用土地覆盖(LULC)的负面变化,导致城市热环境恶化,并产生各种不利的生态影响。LULC和热特征的变化对经济、气候模式和环境可持续性具有重大影响。本研究聚焦于意大利那不勒斯省,考察了1990年至2022年期间的LULC变化和城市热场方差指数(UTFVI),并预测了其2030年的分布情况。本研究的主要目标是通过UTFVI表征地表温度(LST),并分析LULC动态及其相关性,从而研究研究区域未来的季节性热特征。为实现这一目标,使用了陆地卫星4 - 5专题制图仪(TM)和陆地卫星9业务陆地成像仪(OLI)的图像。LULC分类采用监督卫星图像分类系统进行,预测则使用细胞自动机 - 人工神经网络(CA - ANN)算法。LST使用辐射传输方程(RTE)计算,同样的CA - ANN算法用于预测2030年的UTFVI。为研究LULC和UTFVI之间的多时间相关性,采用了交叉列表技术。研究结果表明,在2022年至2030年期间,建成区和裸地区域将增加9.4%,代价是植被类别面积减少。预计夏季最强UTFVI区域在2022年至2030年期间将保持稳定,而冬季UTFVI则有大幅波动,无UTFVI区域减少4.62%,同期最强UTFVI区域相应增加。本研究结果揭示了那不勒斯省建成区向外扩张的令人担忧的趋势,中北部地区增长率最高,主要是以植被覆盖为代价。这些预测强调迫切需要采取积极措施来保护日益减少的植被覆盖,维持生态平衡,对抗城市热岛效应,并保护该省的生物多样性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/899beedc6ef8/sensors-23-07013-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/6abad386e4e4/sensors-23-07013-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/874943e021cb/sensors-23-07013-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/2fd142845ee8/sensors-23-07013-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/c5ca8a24ebbf/sensors-23-07013-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/3e0b9b8ec708/sensors-23-07013-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/cdf2342737b6/sensors-23-07013-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/b021b67aecae/sensors-23-07013-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/8fb0c892b6fd/sensors-23-07013-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/899beedc6ef8/sensors-23-07013-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/6abad386e4e4/sensors-23-07013-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/874943e021cb/sensors-23-07013-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/2fd142845ee8/sensors-23-07013-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/c5ca8a24ebbf/sensors-23-07013-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/3e0b9b8ec708/sensors-23-07013-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/cdf2342737b6/sensors-23-07013-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/b021b67aecae/sensors-23-07013-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/8fb0c892b6fd/sensors-23-07013-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c483/10422488/899beedc6ef8/sensors-23-07013-g009.jpg

相似文献

1
Unveiling the Dynamics of Thermal Characteristics Related to LULC Changes via ANN.通过人工神经网络揭示与土地利用/土地覆盖变化相关的热特征动态。
Sensors (Basel). 2023 Aug 7;23(15):7013. doi: 10.3390/s23157013.
2
Quantitative assessment of land surface temperature and vegetation indices on a kilometer grid scale.公里网格尺度下地表面温度和植被指数的定量评估。
Environ Sci Pollut Res Int. 2023 Oct;30(49):107236-107258. doi: 10.1007/s11356-023-27418-y. Epub 2023 May 9.
3
Change detection in a rural landscape: A case study of processes and main driving factors along with its response to thermal environment in Farim, Iran.农村景观变化检测:以伊朗法里姆为例的过程和主要驱动因素及其对热环境的响应研究。
Environ Sci Pollut Res Int. 2023 Oct;30(49):107041-107057. doi: 10.1007/s11356-022-24504-5. Epub 2022 Dec 17.
4
Predicting the impacts of urban development on urban thermal environment using machine learning algorithms in Nanjing, China.利用机器学习算法预测中国南京城市发展对城市热环境的影响。
J Environ Manage. 2024 Apr;356:120560. doi: 10.1016/j.jenvman.2024.120560. Epub 2024 Mar 27.
5
Evaluation of seasonal ecological vulnerability using LULC and thermal state dynamics using Landsat and MODIS data: a case study of Prayagraj City, India (1987-2018).利用陆地卫星和中分辨率成像光谱仪数据通过土地利用/土地覆盖变化(LULC)和热状态动态评估季节性生态脆弱性:以印度普拉亚格拉杰市为例(1987 - 2018年)
Environ Sci Pollut Res Int. 2022 Nov;29(51):77502-77535. doi: 10.1007/s11356-022-21225-7. Epub 2022 Jun 9.
6
Assessing the land use dynamics and thermal environment using geospatial techniques in the industrial city of Chotanagpur Plateau Region, India.利用地理空间技术评估印度乔塔那格浦尔高原地区工业城市的土地利用动态和热环境。
Environ Monit Assess. 2024 Jun 11;196(7):609. doi: 10.1007/s10661-024-12752-6.
7
Predicting land use dynamics, surface temperature and urban thermal field variance index in mild cold climate urban area of Pakistan.预测巴基斯坦温和寒冷气候城市地区的土地利用动态、地表温度和城市热场方差指数。
Heliyon. 2024 Oct 1;10(19):e38787. doi: 10.1016/j.heliyon.2024.e38787. eCollection 2024 Oct 15.
8
Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh.利用多时相陆地卫星图像预测孟加拉国西北地区土地利用/土地覆盖变化及季节性地表温度
Heliyon. 2021 Jul 21;7(7):e07623. doi: 10.1016/j.heliyon.2021.e07623. eCollection 2021 Jul.
9
Impact of urban land use and land cover change on urban heat island and urban thermal comfort level: a case study of Addis Ababa City, Ethiopia.城市土地利用与土地覆盖变化对城市热岛及城市热舒适度的影响:以埃塞俄比亚亚的斯亚贝巴市为例
Environ Monit Assess. 2022 Sep 7;194(10):736. doi: 10.1007/s10661-022-10414-z.
10
Analysis and simulation of land cover changes and their impacts on land surface temperature in a lower Himalayan region.分析和模拟低喜马拉雅地区的土地覆盖变化及其对地表温度的影响。
J Environ Manage. 2019 Sep 1;245:348-357. doi: 10.1016/j.jenvman.2019.05.063. Epub 2019 May 31.

引用本文的文献

1
Variation of vegetation cover and the relationship with land surface temperature across Thailand (2007 to 2022).泰国2007年至2022年植被覆盖变化及其与地表温度的关系。
Sci Rep. 2025 Jul 30;15(1):27823. doi: 10.1038/s41598-025-13018-y.
2
Impact of Land Use and Land Cover (LULC) Changes on Carbon Stocks and Economic Implications in Calabria Using Google Earth Engine (GEE).利用谷歌地球引擎(GEE)研究土地利用和土地覆盖(LULC)变化对卡拉布里亚碳储量的影响及经济意义
Sensors (Basel). 2024 Sep 8;24(17):5836. doi: 10.3390/s24175836.

本文引用的文献

1
Low-Cost GNSS and PPP-RTK: Investigating the Capabilities of the u-blox ZED-F9P Module.低成本 GNSS 和 PPP-RTK:u-blox ZED-F9P 模块性能研究。
Sensors (Basel). 2023 Jul 1;23(13):6074. doi: 10.3390/s23136074.
2
Rubber expansion and age-class mapping in the state of Tripura (India) 1990-2021 using multi-year and multi-sensor data.利用多年多源数据对印度特里普拉邦(Tripura)的橡胶扩展和年龄层绘图进行研究(1990-2021 年)
Environ Monit Assess. 2023 Jan 31;195(2):348. doi: 10.1007/s10661-023-10942-2.
3
Modelling of Land Use/Cover and LST Variations by Using GIS and Remote Sensing: A Case Study of the Northern Pakhtunkhwa Mountainous Region, Pakistan.
利用 GIS 和遥感技术进行土地利用/覆盖和 LST 变化建模:以巴基斯坦北部开伯尔-普赫图赫瓦山区为例。
Sensors (Basel). 2022 Jun 30;22(13):4965. doi: 10.3390/s22134965.
4
An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: A heatwave event in Naples.基于城市能量平衡的机器学习方法在城市夜间地表热岛合成预测中的应用:那不勒斯热浪事件。
Sci Total Environ. 2022 Jan 20;805:150130. doi: 10.1016/j.scitotenv.2021.150130. Epub 2021 Sep 6.
5
Urban heat island and air pollution--an emerging role for hospital respiratory admissions in an urban area.城市热岛与空气污染——城市地区医院呼吸道疾病入院病例数增加的新因素
J Environ Health. 2010 Jan-Feb;72(6):32-5.
6
The kappa statistic in reliability studies: use, interpretation, and sample size requirements.可靠性研究中的kappa统计量:用途、解释及样本量要求。
Phys Ther. 2005 Mar;85(3):257-68.