A multidimensional machine learning framework for LST reconstruction and climate variable analysis in forest fire occurrence

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY
Hatef Dastour, Quazi K. Hassan
{"title":"A multidimensional machine learning framework for LST reconstruction and climate variable analysis in forest fire occurrence","authors":"Hatef Dastour,&nbsp;Quazi K. Hassan","doi":"10.1016/j.ecoinf.2024.102849","DOIUrl":null,"url":null,"abstract":"<div><div>Land Surface Temperature (LST) datasets play a crucial role in understanding the complex interplay between forest fires, climate variables, and vegetation dynamics. This study is divided into two primary parts: the first part investigates the predictive performance of a machine learning framework based on CatBoost and XGBoost models in estimating LST across different land cover classes in Alberta, Canada. On the test set, for LST-Day data, CatBoost and XGBoost achieved Median Absolute Errors (MedAE) of approximately 1.434 °C and 1.425 °C, respectively. For LST-Night data, also on the test set, the MedAE values were approximately 1.186 °C for CatBoost and 1.176 °C for XGBoost. The second part explores the intricate relationships between climatic variables—LST, precipitation, and relative humidity—forest fire occurrences, and vegetation dynamics in various subregions. The findings revealed complex interactions, with high LST, reduced precipitation, and humidity associated with increased forest fire activity and subsequent changes in vegetation patterns, particularly in the Central Mixedwood, Dry Mixedwood, and Montane subregions. A notable potential association was identified between high LST, reduced precipitation and humidity, and increased forest fire activity in these areas. These climate change impacts and fire events were found to influence ecological processes, altering species composition, reducing biodiversity, and potentially disrupting ecosystem services such as carbon sequestration and nutrient cycling. These insights are crucial for informing adaptive forest management strategies aimed at understanding and mitigating the cascading effects of climate change on fire regimes and vegetation dynamics in Alberta's diverse landscapes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102849"},"PeriodicalIF":5.8000,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574954124003911","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Land Surface Temperature (LST) datasets play a crucial role in understanding the complex interplay between forest fires, climate variables, and vegetation dynamics. This study is divided into two primary parts: the first part investigates the predictive performance of a machine learning framework based on CatBoost and XGBoost models in estimating LST across different land cover classes in Alberta, Canada. On the test set, for LST-Day data, CatBoost and XGBoost achieved Median Absolute Errors (MedAE) of approximately 1.434 °C and 1.425 °C, respectively. For LST-Night data, also on the test set, the MedAE values were approximately 1.186 °C for CatBoost and 1.176 °C for XGBoost. The second part explores the intricate relationships between climatic variables—LST, precipitation, and relative humidity—forest fire occurrences, and vegetation dynamics in various subregions. The findings revealed complex interactions, with high LST, reduced precipitation, and humidity associated with increased forest fire activity and subsequent changes in vegetation patterns, particularly in the Central Mixedwood, Dry Mixedwood, and Montane subregions. A notable potential association was identified between high LST, reduced precipitation and humidity, and increased forest fire activity in these areas. These climate change impacts and fire events were found to influence ecological processes, altering species composition, reducing biodiversity, and potentially disrupting ecosystem services such as carbon sequestration and nutrient cycling. These insights are crucial for informing adaptive forest management strategies aimed at understanding and mitigating the cascading effects of climate change on fire regimes and vegetation dynamics in Alberta's diverse landscapes.
用于林火发生过程中 LST 重建和气候变量分析的多维机器学习框架
地表温度(LST)数据集在了解森林火灾、气候变量和植被动态之间复杂的相互作用方面发挥着至关重要的作用。本研究分为两个主要部分:第一部分研究了基于 CatBoost 和 XGBoost 模型的机器学习框架在估算加拿大艾伯塔省不同土地覆被等级的 LST 时的预测性能。在测试集上,对于低温日数据,CatBoost 和 XGBoost 的中位绝对误差(MedAE)分别约为 1.434 ℃ 和 1.425 ℃。对于 LST-Night 数据(也是测试集),CatBoost 和 XGBoost 的中位绝对误差值分别约为 1.186 ℃ 和 1.176 ℃。第二部分探讨了气候变量--LST、降水量和相对湿度--森林火灾发生率和各次区域植被动态之间错综复杂的关系。研究结果表明,低温层高、降水量减少和湿度与森林火灾活动的增加以及植被模式的随之变化有关,尤其是在中部混交林、干旱混交林和山地次区域,这些因素之间存在复杂的相互作用。在这些地区,低温层高、降水量和湿度减少与森林火灾活动增加之间存在明显的潜在联系。研究发现,这些气候变化影响和火灾事件会影响生态过程,改变物种组成,减少生物多样性,并可能破坏生态系统服务,如碳封存和养分循环。这些见解对于制定适应性森林管理战略至关重要,这些战略旨在了解和减轻气候变化对阿尔伯塔省多样化地貌的火灾机制和植被动态的连锁影响。
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
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
×
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