Trend analysis of greening and browning in Hyrcanian forests and their responses to climate change

IF 3.9 3区环境科学与生态学 Q1 ENGINEERING, CIVIL

Stochastic Environmental Research and Risk Assessment Pub Date : 2024-08-14 DOI:10.1007/s00477-024-02794-0

Ahmad Abbasnezhad Alchin, Ali Asghar Darvishsefat, Vahid Nasiri, Jarosław Socha

{"title":"Trend analysis of greening and browning in Hyrcanian forests and their responses to climate change","authors":"Ahmad Abbasnezhad Alchin, Ali Asghar Darvishsefat, Vahid Nasiri, Jarosław Socha","doi":"10.1007/s00477-024-02794-0","DOIUrl":null,"url":null,"abstract":"<p>Recognizing the impact of climate change on the temporal and spatial variations in forests is crucial for sustaining them in the face of climate change. Here, we aimed to: (1) analyses the greening and browning trends in HFs based on time-series VIs, focusing on foliage trends observable through remote sensing; (2) explore the temporal and spatial trends of climatic factors; and (3) identify the relationship between the greening and browning of the forests and climate change. In this regard, we generated an 18-year (2003–2020) time series with an 8-day temporal resolution, encompassing MODIS vegetation indices (EVI and NDVI) and four climatic and hydrological factors: day and night temperature (LSTd, LSTn), precipitation (PRE), and actual evapotranspiration (ET). Subsequently, we used spatial statistical methods for analysis. EVI and NDVI trend analyses over the study period revealed greening in 77.02% and 92.32% of the study area, respectively. The statistical test confirmed significance (<i>p</i> < 0.05) for this greening in around 41.59% (EVI trend) and 75.11% (NDVI trend). Regarding the climatic and hydrological factors, PRE exhibited a declining trend, whereas LSTd, LSTn, and ET showed an increasing trend. Conclusively, the results reveal a positive correlation, ranging between 0.7 and 0.9, between temperature (LSTd and LSTn) and vegetation indices, indicating a close association between the greening process in HFs and rising temperatures (LSTd and LSTn). These results contribute to the understanding of the ecological resilience of HFs, aiding in the development of strategies to enhance ecosystems’ resilience in the face of climate change.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"60 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastic Environmental Research and Risk Assessment","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s00477-024-02794-0","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}

引用次数: 0

Abstract

Recognizing the impact of climate change on the temporal and spatial variations in forests is crucial for sustaining them in the face of climate change. Here, we aimed to: (1) analyses the greening and browning trends in HFs based on time-series VIs, focusing on foliage trends observable through remote sensing; (2) explore the temporal and spatial trends of climatic factors; and (3) identify the relationship between the greening and browning of the forests and climate change. In this regard, we generated an 18-year (2003–2020) time series with an 8-day temporal resolution, encompassing MODIS vegetation indices (EVI and NDVI) and four climatic and hydrological factors: day and night temperature (LSTd, LSTn), precipitation (PRE), and actual evapotranspiration (ET). Subsequently, we used spatial statistical methods for analysis. EVI and NDVI trend analyses over the study period revealed greening in 77.02% and 92.32% of the study area, respectively. The statistical test confirmed significance (p < 0.05) for this greening in around 41.59% (EVI trend) and 75.11% (NDVI trend). Regarding the climatic and hydrological factors, PRE exhibited a declining trend, whereas LSTd, LSTn, and ET showed an increasing trend. Conclusively, the results reveal a positive correlation, ranging between 0.7 and 0.9, between temperature (LSTd and LSTn) and vegetation indices, indicating a close association between the greening process in HFs and rising temperatures (LSTd and LSTn). These results contribute to the understanding of the ecological resilience of HFs, aiding in the development of strategies to enhance ecosystems’ resilience in the face of climate change.

Abstract Image

查看原文本刊更多论文

海尔卡尼亚森林绿化和褐变趋势分析及其对气候变化的响应

认识到气候变化对森林时空变化的影响对于在气候变化中维持森林至关重要。在此，我们旨在(1) 基于时间序列VI分析高频森林的绿化和褐变趋势，重点关注遥感观测到的叶片趋势；(2) 探讨气候因子的时空变化趋势；(3) 识别森林绿化和褐变与气候变化之间的关系。为此，我们生成了一个时间分辨率为 8 天的 18 年（2003-2020 年）时间序列，其中包括 MODIS 植被指数（EVI 和 NDVI）以及四个气候和水文因子：昼夜温度（LSTd、LSTn）、降水量（PRE）和实际蒸散量（ET）。随后，我们使用空间统计方法进行分析。研究期间的 EVI 和 NDVI 趋势分析显示，分别有 77.02% 和 92.32% 的研究区域出现绿化。统计检验证实，约 41.59%（EVI 趋势）和 75.11%（NDVI 趋势）的绿化率显著（p < 0.05）。在气候和水文因子方面，PRE 呈下降趋势，而 LSTd、LSTn 和 ET 则呈上升趋势。总之，研究结果表明，温度（LSTd 和 LSTn）与植被指数之间存在 0.7 至 0.9 的正相关关系，这表明高寒地区的绿化过程与温度（LSTd 和 LSTn）的升高密切相关。这些结果有助于了解高频地区的生态恢复能力，有助于制定战略，提高生态系统在气候变化面前的恢复能力。

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

Stochastic Environmental Research and Risk Assessment 环境科学-工程：环境

CiteScore

7.10

自引率

9.50%

发文量

189

审稿时长

3.8 months

期刊介绍： Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.