Predicting autumn phenology: How deciduous tree species respond to weather stressors

Xie, Yingying; Wang, Xiaojing; Wilson, Adam M.; Silander, John A.

doi:10.1016/j.agrformet.2017.12.259

Cited by 114 publications

(85 citation statements)

References 78 publications

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“…; and Xie et al. ). Proportion of canopy in leaf unfolding, leaf coloration, and leaf drop for each tree canopy were observed and scored visually.…”

Section: Methodsmentioning

confidence: 96%

“…We had parallel, ground-based visual observations on leaf phenology for each of these eight tree species measured twice weekly in spring and autumn from 2012 to 2014 replicated at four sites following the modified protocols from the USA National Phenology Network (Denny et al 2014; see Appendix S1: Table S1; Fig. 1; and Xie et al 2017). Proportion of canopy in leaf unfolding, leaf coloration, and leaf drop for each tree canopy were observed and scored visually.…”

Section: Phenological Date Determination and Comparison From Images Amentioning

confidence: 99%

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Species‐specific spring and autumn leaf phenology captured by time‐lapse digital cameras

2018

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Plant leaf phenology is typically observed either via ground‐based visual observations on individuals or via remote sensing of land surface vegetation. To integrate phenological information from both data sources, collected at different spatial scales using different observational protocols, digital cameras were deployed spanning canopy areas with enough spatial resolution to identify temporal changes in individual deciduous tree species with continuous observations. Comparisons of phenology between camera photography and in situ observations have been reported in prior studies; however, it is still unclear that how these camera images relate to field observations at individual and species levels, and how the metrics from those images provide comparable species‐specific phenological responses to environmental variation. We set a suite of digital time‐lapse cameras to acquire continuous photographs of deciduous tree canopies and conducted ground‐based visual observations in Connecticut, USA, from 2012 to 2014. Comparisons between image‐derived dates and observed phenological dates showed that both green and red color indices could be matched to ground observations, and red color indices showed good performance in matching autumn phenology across our group of eight tree species that dominate the southern New England forests. Linear mixed‐effects models were applied to investigate the relationships between climatic/weather conditions and the timing of peak and of intensity of red color in fall foliage for each species. Model results suggested that temperature, precipitation, drought stress in autumn, and heat stress in summer are all important factors to the timing of peak fall foliage color and that higher minimum temperatures (or lower cold degree‐day accumulation) in the autumn are linked to higher intensity of red coloration at least in sugar maples. This study improves our understanding of temporal and spatial variation in the phenology of deciduous trees captured by digital cameras. As well, this provides insights into relating species‐specific information on phenology from visual observations in the field to near‐surface remote sensing and points to the need for further research on autumn phenology using the change in redness of tree canopies.

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“…; and Xie et al. ). Proportion of canopy in leaf unfolding, leaf coloration, and leaf drop for each tree canopy were observed and scored visually.…”

Section: Methodsmentioning

confidence: 96%

Section: Phenological Date Determination and Comparison From Images Amentioning

confidence: 99%

Species‐specific spring and autumn leaf phenology captured by time‐lapse digital cameras

2018

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“…In temperate forests, delays in EOS could be linked to increased temperatures in autumn (Delpierre et al, ; Estrella & Menzel, ; Ge, Wang, Rutishauser, & Dai, ; Jeong, Ho, Gim, & Brown, ; Liu, Fu, Zeng, et al, ; Yang, Guan, Shen, Liang, & Jiang, ), while earlier leaf senescence may also relate to the warming‐induced drought (Estrella & Menzel, ; Li et al, ; Liu, Fu, Zhu, et al, ; Zhu, Zheng, Jiang, & Zhang, ). An increase in precipitation could delay EOS (Liu, Fu, Zeng, et al, ; Richardson et al, ), but may also advance autumn foliar events (Cong, Shen, & Piao, ; Xie, Wang, Wilson, & Silander, ; Zhu et al, ), suggesting complex responses of autumn phenological events to environmental cues.…”

Section: Introductionmentioning

confidence: 99%

Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere

Peng

Zhang

et al. 2019

Global Change Biology

173

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Climate change has substantial influences on autumn leaf senescence, that is, the end of the growing season (EOS). Relative to the impacts of temperature and precipitation on EOS, the influence of drought is not well understood, especially considering that there are apparent cumulative and lagged effects of drought on plant growth. Here, we investigated the cumulative and lagged effects of drought (in terms of the Standardized Precipitation-Evapotranspiration Index, SPEI) on EOS derived from the normalized difference vegetation index (NDVI3g) data over the Northern Hemisphere extra-tropical ecosystems (>30°N) during 1982-2015. The cumulative effect was determined by the number of antecedent months at which SPEI showed the maximum correlation with EOS (i.e., R max-cml ) while the lag effect was determined by a month during which the maximum correlation between 1-month SPEI and EOS occurred (i.e., R max-lag ). We found cumulative effect of drought on EOS for 27.2% and lagged effect for 46.2% of the vegetated land area. For the dominant time scales where the R max-cml and R max-lag occurred, we observed 1-4 accumulated months for the cumulative effect and 2-6 lagged months for the lagged effect. At the biome level, drought had stronger impacts on EOS in grasslands, savannas, and shrubs than in forests, which may be related to the different root functional traits among vegetation types. Considering hydrological conditions, the mean values of both R max-cml and R max-lag decreased along the gradients of annual SPEI and its slope, suggesting stronger cumulative and lagged effects in drier regions as well as in areas with decreasing water availability. Furthermore, the average accumulated and lagged months tended to decline along the annual SPEI gradient but increase with increasing annual SPEI. Our results revealed that drought has strong cumulative and lagged effects on autumn phenology, and considering these effects could provide valuable information on the vegetation response to a changing climate. K E Y W O R D Sautumn phenology, cumulative effect, drought, lagged effect

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“…The seasonal and long-time-scale dynamics in vegetation foliage parameterizes models used to understand the dynamic processes of earth ecosystems [2] and reflect the interactions between the terrestrial biosphere and the climate system [3]. Although a large number of growing season phenological events have been extensively explored [4], fall phenology remains poorly understood [5]; such knowledge is important to gain insights into the climate system and ecosystem management, because it can help in choosing suitable species to be planted according to the climate conditions [6]. Peak foliage coloration, which is one of the fall phenological events, is sensitive to environmental conditions, such as temperatures, water, and protein [7].…”

Section: Introductionmentioning

confidence: 99%

Grouping-Based Time-Series Model for Monitoring of Fall Peak Coloration Dates Using Satellite Remote Sensing Data

et al. 2020

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Accurate monitoring of plant phenology is vital to effective understanding and prediction of the response of vegetation ecosystems to climate change. Satellite remote sensing is extensively employed to monitor vegetation phenology. However, fall phenology, such as peak foliage coloration, is less well understood compared with spring phenological events, and is mainly determined using the vegetation index (VI) time-series. Each VI only emphasizes a single vegetation property. Thus, selecting suitable VIs and taking advantage of multiple spectral signatures to detect phenological events is challenging. In this study, a novel grouping-based time-series approach for satellite remote sensing was proposed, and a wide range of spectral wavelengths was considered to monitor the complex fall foliage coloration process with simultaneous changes in multiple vegetation properties. The spatial and temporal scale effects of satellite data were reduced to form a reliable remote sensing time-series, which was then divided into groups, namely pre-transition, transition and post-transition groups, to represent vegetation dynamics. The transition period of leaf coloration was correspondingly determined to divisions with the smallest intra-group and largest inter-group distances. Preliminary results using a time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2002 to 2013 at the Harvard Forest (spatial scale: ~3500 m; temporal scale: ~8 days) demonstrated that the method can accurately determine the coloration period (correlation coefficient: 0.88; mean absolute difference: 3.38 days), and that the peak coloration periods displayed a shifting trend to earlier dates. The grouping-based approach shows considerable potential in phenological monitoring using satellite time-series.

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Predicting autumn phenology: How deciduous tree species respond to weather stressors

Cited by 114 publications

References 78 publications

Species‐specific spring and autumn leaf phenology captured by time‐lapse digital cameras

Species‐specific spring and autumn leaf phenology captured by time‐lapse digital cameras

Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere

Grouping-Based Time-Series Model for Monitoring of Fall Peak Coloration Dates Using Satellite Remote Sensing Data

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