Exploring the Influence of Various Land Use Land Cover on Land Surface Temperature of Coastal Tourism Areas in Bali Using Landsat 9

Q3 Environmental Science
I. Diara, K. Susila, W. Wiyanti, I. Sunarta, T. B. Kusmiyarti, M. Saifulloh
{"title":"Exploring the Influence of Various Land Use Land Cover on Land Surface Temperature of Coastal Tourism Areas in Bali Using Landsat 9","authors":"I. Diara, K. Susila, W. Wiyanti, I. Sunarta, T. B. Kusmiyarti, M. Saifulloh","doi":"10.5755/j01.erem.80.2.34693","DOIUrl":null,"url":null,"abstract":"The Bali Tourism area represents complex environments where human activities intersect with natural landscapes, resulting in diverse land use land cover (LULC) patterns. However, understanding the dynamics of LULC in these areas and its interaction with land surface temperature (LST) remains a challenge. This study addresses this gap by investigating LULC mapping in urban tourist destinations and its influence on LST variations. The research problem focuses on exploring the relationship between various land cover types and LST variations. The main objective is to assess the interaction of LULC variations with LST in urban tourist environments. To achieve this goal, an integrated approach combining remote sensing techniques and machine learning will be employed. LULC mapping will utilize support vector machine (SVM) techniques with datasets sourced from multi-channel data, and spectral indices such as enhanced built-up and bareness index (EBBI) and normalized differences vegetation index (NDVI) derived from Landsat 9. The findings present a vivid overview of the research area, where built-up areas dominate, and spanning 108.61 km². Other land cover classifications include rice fields/grasslands, plantation/perennial plants, barren land, mangrove forests, shrublands, and water bodies, accurately mapped with high precision (overall accuracy = 88.52% and Kappa = 81%). Maximum LST values peak in built-up and barren areas, reaching 29.89°C and 29.28°C, respectively, while other land cover types exhibit comparatively lower values. Our analysis of the spectral index used in LULC classification uncovers a positive correlation with EBBI (R2 = 37.78%) and a negative correlation with NDVI (R2 = 10.69%, based on a substantial sample size of 67 869 pixels. We strongly urge future researchers to leverage high-resolution data for localized urban studies and stress the critical importance of enforcing stringent spatial planning regulations to safeguard green spaces, thus ensuring ecological equilibrium for future generations.","PeriodicalId":11703,"journal":{"name":"Environmental Research, Engineering and Management","volume":" 43","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Research, Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5755/j01.erem.80.2.34693","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 0

Abstract

The Bali Tourism area represents complex environments where human activities intersect with natural landscapes, resulting in diverse land use land cover (LULC) patterns. However, understanding the dynamics of LULC in these areas and its interaction with land surface temperature (LST) remains a challenge. This study addresses this gap by investigating LULC mapping in urban tourist destinations and its influence on LST variations. The research problem focuses on exploring the relationship between various land cover types and LST variations. The main objective is to assess the interaction of LULC variations with LST in urban tourist environments. To achieve this goal, an integrated approach combining remote sensing techniques and machine learning will be employed. LULC mapping will utilize support vector machine (SVM) techniques with datasets sourced from multi-channel data, and spectral indices such as enhanced built-up and bareness index (EBBI) and normalized differences vegetation index (NDVI) derived from Landsat 9. The findings present a vivid overview of the research area, where built-up areas dominate, and spanning 108.61 km². Other land cover classifications include rice fields/grasslands, plantation/perennial plants, barren land, mangrove forests, shrublands, and water bodies, accurately mapped with high precision (overall accuracy = 88.52% and Kappa = 81%). Maximum LST values peak in built-up and barren areas, reaching 29.89°C and 29.28°C, respectively, while other land cover types exhibit comparatively lower values. Our analysis of the spectral index used in LULC classification uncovers a positive correlation with EBBI (R2 = 37.78%) and a negative correlation with NDVI (R2 = 10.69%, based on a substantial sample size of 67 869 pixels. We strongly urge future researchers to leverage high-resolution data for localized urban studies and stress the critical importance of enforcing stringent spatial planning regulations to safeguard green spaces, thus ensuring ecological equilibrium for future generations.
利用大地遥感卫星 9 号探索各种土地利用土地覆盖对巴厘岛沿海旅游区地表温度的影响
巴厘岛旅游区环境复杂,人类活动与自然景观交错,形成了多种多样的土地利用、土地覆被 (LULC) 模式。然而,了解这些地区 LULC 的动态及其与地表温度 (LST) 的相互作用仍然是一项挑战。本研究通过调查城市旅游目的地的 LULC 图谱及其对 LST 变化的影响来填补这一空白。研究问题的重点是探索各种土地覆被类型与 LST 变化之间的关系。主要目标是评估城市旅游环境中 LULC 变化与 LST 之间的相互作用。为实现这一目标,将采用遥感技术与机器学习相结合的综合方法。LULC 制图将利用支持向量机(SVM)技术,数据集来自多通道数据,光谱指数来自 Landsat 9,如增强的建筑与裸露指数(EBBI)和归一化差异植被指数(NDVI)。 研究结果生动地展示了研究区域的概况,其中建筑区占主导地位,面积达 108.61 平方公里。其他土地覆被分类包括稻田/草地、种植园/多年生植物、荒地、红树林、灌木林和水体,测绘精度高(总体精度 = 88.52%,Kappa = 81%)。建筑密集区和贫瘠地区的 LST 值最高,分别达到 29.89°C 和 29.28°C,而其他土地覆被类型的 LST 值相对较低。我们对 LULC 分类中使用的光谱指数进行了分析,发现它与 EBBI 呈正相关(R2 = 37.78%),与 NDVI 呈负相关(R2 = 10.69%,基于 67 869 个像素的大量样本)。我们强烈呼吁未来的研究人员利用高分辨率数据进行本地化城市研究,并强调执行严格的空间规划法规以保护绿地的极端重要性,从而确保子孙后代的生态平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Environmental Research, Engineering and Management
Environmental Research, Engineering and Management Environmental Science-Environmental Engineering
CiteScore
2.40
自引率
0.00%
发文量
32
期刊介绍: First published in 1995, the journal Environmental Research, Engineering and Management (EREM) is an international multidisciplinary journal designed to serve as a roadmap for understanding complex issues and debates of sustainable development. EREM publishes peer-reviewed scientific papers which cover research in the fields of environmental science, engineering (pollution prevention, resource efficiency), management, energy (renewables), agricultural and biological sciences, and social sciences. EREM’s topics of interest include, but are not limited to, the following: environmental research, ecological monitoring, and climate change; environmental pollution – impact assessment, mitigation, and prevention; environmental engineering, sustainable production, and eco innovations; environmental management, strategy, standards, social responsibility; environmental economics, policy, and law; sustainable consumption and education.
×
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