Ahmad Abbasnezhad Alchin, Ali Asghar Darvishsefat, Vahid Nasiri, Jarosław Socha
{"title":"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","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":"{\"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}","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}
Trend analysis of greening and browning in Hyrcanian forests and their responses to climate change
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.
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.