Shaanxi province in China is rich in vegetation resources,making the study of vegetation phenology crucial for understanding climate change and ecological dynamics.Previous studies on Shaanxi have primarily focused on...Shaanxi province in China is rich in vegetation resources,making the study of vegetation phenology crucial for understanding climate change and ecological dynamics.Previous studies on Shaanxi have primarily focused on coarse-scale phenology,which results in the loss of essential spatial details due to the lack of finer-scale analyses.Moreover,the impact of rapid urbanisation on the phenology of various vegetation types in this region remains unclear.To address these gaps,we integrated Landsat and MODIS data to generate a daily 30 m NDVI sequence.Vegetation phenology across Shaanxi from 2000 to 2020 was then extracted and analysed,and the responses of different vegetation types to urbanisation were explored.Results indicated that accurate vegetation phenology can be obtained at a 30 m resolution,which is more precise than conventional coarse-scale investigations.The Start of Season(SOS)and End of Season(EOS)in the province advanced by-0.56 and-0.08 day/year,respectively,from 2000 to 2020.In general,the SOS gradually delays,and the EOS progressively advances with increasing latitude.However,anomalies were identified in regions south of 32.64°N and across the zone from 34.04°N to 37.56°N,which are primarily due to the effects of high elevation and rapid urbanisation,respectively.Further investigation into phenological differences among various vegetation types revealed that forests exhibit the earliest SOS,while grasslands present the latest.In addition,a significant negative correlation was found between the SOS difference and air temperature difference along the urban-rural gradient,with urbanisation having the most significant impact on cropland phenology.This study provides valuable insights into vegetation phenology and its response to urbanisation.These insights offer crucial information for ecological conservation and management strategies.展开更多
基金The National Key R&D Program of China,No.2023YFD2000105The Shaanxi Province Science and Technology Innovation Team,No.2021TD-51The Shaanxi Province Geoscience Big Data and Geohazard Prevention Innovation Team(2022)。
文摘Shaanxi province in China is rich in vegetation resources,making the study of vegetation phenology crucial for understanding climate change and ecological dynamics.Previous studies on Shaanxi have primarily focused on coarse-scale phenology,which results in the loss of essential spatial details due to the lack of finer-scale analyses.Moreover,the impact of rapid urbanisation on the phenology of various vegetation types in this region remains unclear.To address these gaps,we integrated Landsat and MODIS data to generate a daily 30 m NDVI sequence.Vegetation phenology across Shaanxi from 2000 to 2020 was then extracted and analysed,and the responses of different vegetation types to urbanisation were explored.Results indicated that accurate vegetation phenology can be obtained at a 30 m resolution,which is more precise than conventional coarse-scale investigations.The Start of Season(SOS)and End of Season(EOS)in the province advanced by-0.56 and-0.08 day/year,respectively,from 2000 to 2020.In general,the SOS gradually delays,and the EOS progressively advances with increasing latitude.However,anomalies were identified in regions south of 32.64°N and across the zone from 34.04°N to 37.56°N,which are primarily due to the effects of high elevation and rapid urbanisation,respectively.Further investigation into phenological differences among various vegetation types revealed that forests exhibit the earliest SOS,while grasslands present the latest.In addition,a significant negative correlation was found between the SOS difference and air temperature difference along the urban-rural gradient,with urbanisation having the most significant impact on cropland phenology.This study provides valuable insights into vegetation phenology and its response to urbanisation.These insights offer crucial information for ecological conservation and management strategies.
