Spatio-Temporal Analysis of Vegetation Response to Climate Change, Case Study: Republic of Serbia

被引:1
|
作者
Baumgertel, Aleksandar [1 ]
Lukic, Sara [1 ]
Cakovic, Milica [1 ]
Lazic, Irida [2 ]
Tosic, Milica [2 ]
Momirovic, Natalija [3 ]
Pandey, Shachi [4 ]
Bezdan, Atila [5 ]
Blagojevic, Bosko [5 ]
Djurdjevic, Vladimir [2 ]
机构
[1] Univ Belgrade, Fac Forestry, Belgrade, Serbia
[2] Univ Belgrade, Inst Meteorol, Fac Phys, Belgrade, Serbia
[3] Inst Forestry, Belgrade, Serbia
[4] Forest Res Inst Univ, Dehra Dun, India
[5] Univ Novi Sad, Fac Agr, Novi Sad, Serbia
关键词
Climate change; NDVI; Forest; Agriculture; GLOBAL YIELDS; IMPACTS; NDVI;
D O I
10.1007/s41742-024-00571-z
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Climate change has a potentially negative impact on the overall vitality of vegetation in both forested and agricultural areas. A comprehensive understanding of the interaction between climate and vegetation across various land cover types holds significant importance from multiple perspectives. This research examined the current state of vegetation trends and their interplay with climate parameters, specifically temperature and precipitation. Additionally, it aimed to provide insights into the anticipated changes in these climate parameters in the future, across the entire area of the Republic of Serbia. The vegetation was observed using the Normalized Difference Vegetation Index (NDVI) obtained from AVHRR/NOAA 11 satellite for the vegetation season (May-October) from 1981 to 2021, while the climate data records used the examination of the relationship between climate indicators and vegetation were monthly mean 2m temperature and precipitation obtained from the ERA5-Land (from April to October). The nonparametric Mann-Kendall test implemented with the Sen's slope estimator and the Pearson correlation coefficient (r) was utilized to identify trends (for the NDVI and climate variables) and the strength of the correlation, respectively. To obtain the information of temperature and precipitation change in future (from 2071 to 2100), the ensemble mean of the eight climate models, for vegetation period and summer season (June-July-August) from the EURO-CORDEX database was used. Results show relatively high NDVI values (> 0.5) over the entire area and the statistically significant (p < 0.005) positive NDVI trend increasing (up to 0.0006 year(-1) from the north (mainly agriculture cover) to the south (forest cover). In agricultural areas, a positive statistically significant correlation (r = 0.4-0.6, p < 0.005) indicates that the quality of vegetation cover in rainfed agriculture is directly dependent on the amount of precipitation, which serves as the sole source of moisture input. In contrast, the situation differs in forested areas where the correlation between NDVI and precipitation is often statistically not significant (p > 0.005) indicating that forests, because of their characteristics, are less dependent on the amount of precipitation. Regarding temperature, in agricultural areas, there is a positive correlation with NDVI, although it does not reach statistical significance. Conversely, in forested areas, a significant positive correlation is observed between NDVI and temperature which even positively contributes to the development of forest vegetation. In future, the recorded decline in precipitation (a substantial 22.72% drop) and the concurrent rise in temperature (up to 4.39(degrees)C) in vegetation period, until 2100 might impact the reduction of NDVI.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] Estimation and Spatio-Temporal Change Analysis of NPP in Subtropical Forests: A Case Study of Shaoguan, Guangdong, China
    Li, Tao
    Li, Mingyang
    Ren, Fang
    Tian, Lei
    REMOTE SENSING, 2022, 14 (11)
  • [42] Spatio-temporal responses of cropland phenophases to climate change in Northeast China
    Zhengguo Li
    Huajun Tang
    Peng Yang
    Wenbin Wu
    Zhongxin Chen
    Qingbo Zhou
    Li Zhang
    Jinqiu Zou
    Journal of Geographical Sciences, 2012, 22 : 29 - 45
  • [43] Spatio-temporal responses of cropland phenophases to climate change in Northeast China
    Li Zhengguo
    Tang Huajun
    Yang Peng
    Wu Wenbin
    Chen Zhongxin
    Zhou Qingbo
    Zhang Li
    Zou Jinqiu
    JOURNAL OF GEOGRAPHICAL SCIENCES, 2012, 22 (01) : 29 - 45
  • [44] Spatio-temporal modeling of climate change impacts on drought forecast using Generative Adversarial Network: A case study in Africa
    Ferchichi, Ahlem
    Chihaoui, Mejda
    Ferchichi, Aya
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 238
  • [45] Bridging climate refuges for climate change adaptation: A spatio-temporal connectivity network approach
    Xu, Dongmei
    Peng, Jian
    Liu, Menglin
    Jiang, Hong
    Tang, Hui
    Dong, Jianquan
    Meersmans, Jeroen
    GEOGRAPHY AND SUSTAINABILITY, 2025, 6 (02)
  • [46] Spatio-temporal analysis of land use/land cover change and its effects on soil erosion (Case study in the Oplenac wine-producing area, Serbia)
    Veljko Perović
    Darko Jakšić
    Darko Jaramaz
    Nikola Koković
    Dragan Čakmak
    Miroslava Mitrović
    Pavle Pavlović
    Environmental Monitoring and Assessment, 2018, 190
  • [47] Spatio-temporal analysis of land use/land cover change and its effects on soil erosion (Case study in the Oplenac wine-producing area, Serbia)
    Perovic, Veljko
    Jaksic, Darko
    Jaramaz, Darko
    Kokovic, Nikola
    Cakmak, Dragan
    Mitrovic, Miroslava
    Pavlovic, Pavle
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2018, 190 (11)
  • [48] Spatio-temporal big data analysis of tourists: A Case Study of Hangzhou
    Xue, Jie
    Shi, Jinhao
    INTERNATIONAL CONFERENCE ON ENVIRONMENTAL REMOTE SENSING AND BIG DATA (ERSBD 2021), 2021, 12129
  • [49] Modeling of vegetation cover and spatio-temporal variations
    Isler, Buket
    Aslan, Zafer
    JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, 2021, 36 (04): : 1863 - 1874
  • [50] Study on spatio-temporal dynamics of vegetation carbon use efficiency and its response to climate factors in Three-River Headwaters Region
    Zheng, Fei-Ge
    Yi, Gui-Hua
    Zhang, Ting-Bin
    Li, Jing-Ji
    Bie, Xiao-Juan
    Liu, Dong
    Qin, Yi
    Zhang, Tian-Tian
    Zhongguo Huanjing Kexue/China Environmental Science, 2020, 40 (01): : 401 - 413