Effect of climate change on the seasonal variation in photosynthetic and non-photosynthetic vegetation coverage in desert areas, Northwest China

• Published in: Catena (Giessen) 2024-04, Vol.239, p.107954, Article 107954
• Main Authors: Bai, Xuelian, Zhao, Wenzhi, Luo, Weicheng, An, Ning
• Format: Article
• Language: English
• Subjects: Climate-vegetation interactions; Dead fuel index; Non-photosynthetic vegetation; Photosynthetic vegetation; Vegetation coverage
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2024.107954

•Precipitation drove fPV variation, temperature dominated fNPV variation.•The time-lag of temperature and precipitation effect on fNPV were 1.28 ± 1.2 and 1.19 ± 1.24 months.•Accumulated precipitation had positive effect on fPV and negative effect on fNPV.•Accumulated temperature and accumulated precipitation have opposite effects on fPV, fNPV. Efficient prediction of the response of vegetation to future climate change requires an in depth understanding of the effect of climate change on the land cover by photosynthetic (fPV) and non-photosynthetic vegetation (fNPV) and its driving mechanism. Here, we aimed to estimate fPV and fNPV, and analyze the impacts of climate factors on their seasonal variability in the arid desert of northwestern China. First, optimal vegetation indices (VIs) were selected to estimate fPV and fNPV based on measured spectral reflectance, and the accuracy of estimated fPV and fNPV using Sentinel-2 images was evaluated with data from an unmannedaerialvehicle (UAV). Then, partial correlation, multi-correlation and time‐lag effect analyses were used to obtain the correlation coefficients and lag times between fPV, fNPV and climate change. The results showed that Normalized Difference Vegetation Index (NDVI) and Dead Fuel Index (DFI) were the optimal VIs to estimate fPV and fNPV in the desert, with the accuracy of 67 and 52 % for fPV and fNPV, respectively. The average fPV and fNPV were 8.15 and 9.26 % from March to November, their seasonal variability was consistent with plant phenology, and there was a decrease in fPV and fNPV from east to west with a decrease in precipitation. The variability in fPV was driven by precipitation while that of fNPV was dominated bytemperature. The time-lag of temperature and precipitation effect on fPV was 1.28 ± 1.28 and 1.61 ± 1.16 months, respectively, and on fNPV was 1.28 ± 1.2 and 1.19 ± 1.24 months, respectively. The time-lag of temperature-precipitation interaction on fPV and fNPV were 1.4 ± 1.22 and 1.2 ± 1.28 months, respectively. Accumulated precipitation had a positive effect on fPV and a negative effect on fNPV, while the impact of accumulated temperature was the opposite. We conclude that there were time-lag and cumulative effects of climate change on fPV and fNPV variability in desert areas. This study helps us better understand the climate-vegetation interactions and provides a scientific basis for desert vegetation management under climate change.