Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greennes...Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greenness index(e.g.,Gcc,green chromatic coordinate)with that derived from satellites is critical for land surface process research.Moreover,our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited.This paper investigated the response of grass Gcc to daily environmental factors in 2018,such as soil moisture(temperature),air temperature,and solar radiation.Thereafter,using a derivative-based phenology extraction method,we evaluated the correspondence between key phenological events(mainly including start,end and length of growing season,and date with maximum greenness value)derived from Gcc,MODIS and VIIRS NDVI(EVI)for the period 2015–2018.The results showed that daily Gcc was in good agreement with ground-level environmental variables.Additionally,multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation,but not by air temperature.High frequency Gcc time series can respond immediately to precipitation events.In the same year,the phenological metrics retrieved from digital cameras and multiple satellites are similar,with spring phenology having a larger relative difference.There are distinct divergences between changing rates in the greenup and senescence stages.Gcc also shows a close relationship with growing degree days(GDD)derived from air temperature.This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors.This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.展开更多
Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms d...Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.展开更多
基金National Natural Science Foundation of China(41601478)National Key Research and Development Program of China(2018YFB0505301,2016YFC0500103)
文摘Near-surface remote sensing(e.g.,digital cameras)has played an important role in capturing plant phenological metrics at either a focal or landscape scale.Exploring the relationship of the digital image-based greenness index(e.g.,Gcc,green chromatic coordinate)with that derived from satellites is critical for land surface process research.Moreover,our understanding of how well Gcc time series associate with environmental variables at field stations in North American prairies remains limited.This paper investigated the response of grass Gcc to daily environmental factors in 2018,such as soil moisture(temperature),air temperature,and solar radiation.Thereafter,using a derivative-based phenology extraction method,we evaluated the correspondence between key phenological events(mainly including start,end and length of growing season,and date with maximum greenness value)derived from Gcc,MODIS and VIIRS NDVI(EVI)for the period 2015–2018.The results showed that daily Gcc was in good agreement with ground-level environmental variables.Additionally,multivariate regression analysis identified that the grass growth in the study area was mainly affected by soil temperature and solar radiation,but not by air temperature.High frequency Gcc time series can respond immediately to precipitation events.In the same year,the phenological metrics retrieved from digital cameras and multiple satellites are similar,with spring phenology having a larger relative difference.There are distinct divergences between changing rates in the greenup and senescence stages.Gcc also shows a close relationship with growing degree days(GDD)derived from air temperature.This study evaluated the performance of a digital camera for monitoring vegetation phenological metrics and related climatic factors.This research will enable multiscale modeling of plant phenology and grassland resource management of temperate prairie ecosystems.
基金supported by the National Natural Science Foundation of China(32171777)the Natural Science Foundation of Heilongjiang for Distinguished Young Scientists(JQ2023F002)the Fundamental Research Funds for Central Universities(2572023CT16).
文摘Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.
