The trend estimate of vegetation change is essential to understand the change rule of the ecosystem.Previous studies were mainly focused on quantifying trends or analyzing their spatial distribution characteristics.Ne...The trend estimate of vegetation change is essential to understand the change rule of the ecosystem.Previous studies were mainly focused on quantifying trends or analyzing their spatial distribution characteristics.Nevertheless,the uncertainties of trend estimates caused by spatiotemporal scale effects have rarely been studied.In response to this challenge,this study aims to investigate spatiotemporal scale effects on trend estimates using Moderate-Resolution Imaging Spectroradiometer(MODIS)Normalized Difference Vegetation Index(NDVI)and Gross Primary Productivity(GPP)products from 2001 to 2019 in the Qinghai-Tibet Plateau(QTP).Moreover,the possible influencing factors on spatiotemporal scale effect,including spatial heterogeneity,topography,and vegetation types,were explored.The results indicate that the spatial scale effect depends more on the dataset with a coarser spatial resolution,and temporal scale effects depend on the time span of datasets.Unexpectedly,the trend estimates on the 8-day and yearly scale are much closer than that on the monthly scale.In addition,in areas with low spatial heterogeneity,low topography variability,and sparse vegetation,the spatiotemporal scale effect can be ignored,and vice versa.The results in this study help deepen the consciousness and understanding of spatiotemporal scale effects on trend detection.展开更多
There are complex landscape pattern and hydrological process(LPHP)interactions,which exhibit different coupling mechanisms across multiple temporal and spatial scales.However,in-depth understanding of the LPHP interac...There are complex landscape pattern and hydrological process(LPHP)interactions,which exhibit different coupling mechanisms across multiple temporal and spatial scales.However,in-depth understanding of the LPHP interactions is currently lacking.This research conducted a systematic review of 198 empirical studies to explore the LPHP interactions.The findings reveal that:1)global LPHP research was concentrated in temperate regions,with tropical and cold regions underrepresented;2)LPHP interactions showed temporal and spatial scales differentiation,with the majority of studies occurring at long-term local and regional scales,and the relationship between agricultural land expansion and surface runoff was a key point.This research proposed a dual-path driving model that captures both landscape pattern-driven hydrological processes and hydrological process-reshaping landscape patterns.In natural areas,high cohesion and aggregation patterns should be protected and enhanced.In urban areas,landscape fragmentation should be controlled and green infrastructure should be promoted to strengthen hydrological resilience.Additionally,soil erosion and floods not only alter the landscape composition but may also trigger dynamic changes in landscape configuration,forming feedback loops,which are particularly pronounced at the local scale.Identifying these key pathways enhances the understanding of the coupled human-nature system,facilitating more robust predictions and responses to future changes and challenges.展开更多
基金The Second Tibetan Plateau Scientific Expedition and Research Program(STEP),No.2019QZKK0605National Natural Science Foundation of China,No.42071296。
文摘The trend estimate of vegetation change is essential to understand the change rule of the ecosystem.Previous studies were mainly focused on quantifying trends or analyzing their spatial distribution characteristics.Nevertheless,the uncertainties of trend estimates caused by spatiotemporal scale effects have rarely been studied.In response to this challenge,this study aims to investigate spatiotemporal scale effects on trend estimates using Moderate-Resolution Imaging Spectroradiometer(MODIS)Normalized Difference Vegetation Index(NDVI)and Gross Primary Productivity(GPP)products from 2001 to 2019 in the Qinghai-Tibet Plateau(QTP).Moreover,the possible influencing factors on spatiotemporal scale effect,including spatial heterogeneity,topography,and vegetation types,were explored.The results indicate that the spatial scale effect depends more on the dataset with a coarser spatial resolution,and temporal scale effects depend on the time span of datasets.Unexpectedly,the trend estimates on the 8-day and yearly scale are much closer than that on the monthly scale.In addition,in areas with low spatial heterogeneity,low topography variability,and sparse vegetation,the spatiotemporal scale effect can be ignored,and vice versa.The results in this study help deepen the consciousness and understanding of spatiotemporal scale effects on trend detection.
文摘There are complex landscape pattern and hydrological process(LPHP)interactions,which exhibit different coupling mechanisms across multiple temporal and spatial scales.However,in-depth understanding of the LPHP interactions is currently lacking.This research conducted a systematic review of 198 empirical studies to explore the LPHP interactions.The findings reveal that:1)global LPHP research was concentrated in temperate regions,with tropical and cold regions underrepresented;2)LPHP interactions showed temporal and spatial scales differentiation,with the majority of studies occurring at long-term local and regional scales,and the relationship between agricultural land expansion and surface runoff was a key point.This research proposed a dual-path driving model that captures both landscape pattern-driven hydrological processes and hydrological process-reshaping landscape patterns.In natural areas,high cohesion and aggregation patterns should be protected and enhanced.In urban areas,landscape fragmentation should be controlled and green infrastructure should be promoted to strengthen hydrological resilience.Additionally,soil erosion and floods not only alter the landscape composition but may also trigger dynamic changes in landscape configuration,forming feedback loops,which are particularly pronounced at the local scale.Identifying these key pathways enhances the understanding of the coupled human-nature system,facilitating more robust predictions and responses to future changes and challenges.
