Phenology and Climate Change

Zhang, Xiaoyang

doi:10.5772/2146

Cited by 14 publications

(4 citation statements)

References 234 publications

(358 reference statements)

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“…Por el contrario, los valores máximos registrados se relacionan con la reducción y desaparición de la cobertura nival, y con un aumento de la temperatura en el contexto de un ambiente subhúmedo y húmedo en la alta montaña mediterránea, que origina un aumento de la actividad vegetal. En efecto, se ha demostrado que la temperatura es el principal impulsor de muchos procesos de desarrollo de la planta, y en muchos casos se ha demostrado que temperaturas más altas aceleran el desarrollo de las comunidades vegetales (Vandvik et al, 2018;Zhang, 2012). Esto es coherente con el hecho de que en nuestro estudio se encontraron gradientes fenológicos en función de la altitud promedio de cada sector, con diferencias que podrían estar explicadas por la existencia de diversas formaciones vegetales y distintas condiciones de humedad.…”

Section: Discussionunclassified

Fenología de los ecosistemas de alta montaña en Andalucía: Análisis de la tendencia estacional del SAVI (2000-2019)

Gutiérrez-Hernández

2020

Pirineos

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Los paisajes vegetales de alta montaña constituyen un singular ecosistema en la región más meridional de la península ibérica, donde la estación favorable coincide con un periodo de sequía. En este trabajo, se usaron las imágenes de satélite del sensor Terra-MODIS para desarrollar un análisis de tendencia estacional basado en el cálculo del Índice de Vegetación Ajustado al Suelo (SAVI) de 460 imágenes de satélite con frecuencia de 16 días y aplicado sobre las superficies cubiertas por las comunidades vegetales de los pisos oromediterráneo y crioromediterráneo en Andalucía, con el fin de obtener las curvas de tenencia estacional modeladas (o ajustadas) para el primer (2000) y último (2019) año de la serie. Los resultados obtenidos permitieron caracterizar la curva fenológica típica de estos ecosistemas, con mínimos en invierno (febrero) y máximos en primavera-verano (mayo-junio y julio-agosto, respectivamente), más tardíos a mayor altitud, por el efecto de la temperatura. En este sentido, detectamos un incremento generalizado del SAVI, un adelantamiento de la fase de reverdecimiento, culminado por un aumento del máximo reverdecimiento y seguido de una extensión de la estación de crecimiento. Se concluye que los ecosistemas de alta montaña en Andalucía muestran una tendencia estacional generalizada.

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Section: Discussionunclassified

Fenología de los ecosistemas de alta montaña en Andalucía: Análisis de la tendencia estacional del SAVI (2000-2019)

Gutiérrez-Hernández

2020

Pirineos

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“…The concept of gathering data on the timing of leaf opening, flowering, fruiting and leaf fall alongside climatological observations "so as to show how areas differ" was first proposed by the Swedish botanist in his work Philosphia Botinica in 1751 (Linnaeus, 1751) and is still relevant today (Lieth, 1974). Phenological observations have historically assisted agriculture by means of predicting the timing for cultivation practices (Zhang, 2012) and in the last 100 years emerged as a scientific discipline (Schwartz, 2013). In recent times, phenological events are recognized as a bio-indicator for climate change (Menzel, 2002).…”

Section: Phenologicalmentioning

confidence: 99%

European Beech Spring Phenological Phase Prediction with UAV-derived Multispectral Indices and Machine Learning Regression

Krause

Sanders

2022

Preprint

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The acquisition of phenological events play an integral part in investigating the effects of climate change on forest dynamics and assessing the potential risk involved with the early onset of young leaves. Large scale mapping of forest phenological timing using earth observation data, could facilitate a better understanding of phenological processes due to an added spatial component. The translation of traditional phenological ground observation data into reliable ground truthing for the purpose of the training and validation of Earth Observation (EO) mapping applications is a challenge. In this study, we explored the possibility of predicting high resolution phenological phase data for European beech (Fagus sylvatica) with the use of Unmanned Aerial Vehicle (UAV)-based multispectral indices and machine learning. Using a comprehensive feature selection process, we were able to identify the most effective sensors, vegetations indices, training data partitions, and machine learning models for phenological phase prediction. The best performing model that generalised well over various sites was the model utilising the Green Chromatic Coordinate (GCC) and Generalized Addictive Model (GAM) boosting. The GCC training data was derived from the radiometrically calibrated visual bands from a multispectral sensor and predicted using uncalibrated RGB sensor data. The final GCC/GAM boosting model was capable in predicting phenological phases on unseen datasets within a RMSE threshold of 0.5. This research shows the potential of the interoperability among common UAV-mounted sensors in particular the utility of readily available low cost RGB sensors. Considerable limitations were however discovered with indices implementing the near-infrared (NIR) band due to oversaturation. Future work involves adapting models to facilitate the ICP Forests phenological flushing stages.

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“…Leaves were classified as leaf buds, new leaves or old leaves; and fruit/pods were classified as new or mature. For this study, we focused on numbers of flowers, new fruit/pods and new leaves because the relationship between temperature, rainfall and photoperiod affects these resource items directly [28].…”

Section: Plos Onementioning

confidence: 99%

Effects of rainfall, temperature and photoperiod on the phenology of ephemeral resources for selected bushveld woody plant species in southern Africa

Barrett

Brown

2021

PLoS ONE

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Variability of ephemeral resources provided by woody plants is related to fluctuating environmental conditions, specifically the predominant climate variables temperature and rainfall. Photoperiod has less impact but also plays a role in the onset of resource pulses. In the seasonally affected bushveld of southern Africa, declining resources could have dire consequences to various animals that depend on these resources. Understanding the impact that rainfall, temperature and photoperiod has on woody plant resources allows managers of natural areas to plan for times when resources are scarce. Using a series of General Linear Models, this baseline study investigates the effects that these variables have on flower production, numbers of new fruit/pods and numbers of new leaves for 113 tagged trees from 26 woody plant species. Leads, lags and coincidental relationships observed between environmental predictor and phenological response variables were explored using time-series cross-correlations and concomitant correlograms. Model results indicated that temperature was the predominant indicator for flowering, with initial flowering starting when temperatures increase in September. A significant lead was observed between flowering and rainfall, suggesting that flower numbers increase approximately one month before rainfall increases. Temperature had the biggest effect on the number of species with new fruits and pods. Significant lags were observed between new fruits and pods and all environmental variables investigated, indicating that these resources depend on rainfall, temperature and photoperiod to reach their full potential. Photoperiod, temperature and the interaction between these variables had a noticeable effect on the number of species with new leaves. Peaks in species with new leaves coincide with peaks in rainfall, temperature and photoperiod. No leading or lagging indicators were observed between new leaves and the environmental variables investigated. In areas containing wildlife populations, recommendations are to undertake regular monitoring of climatic variables investigated, and the ephemeral resources on woody plant species.

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Phenology and Climate Change

Abstract: he has been working at NOAA/NESDIS through a contract from April 2005. His research interests include satellite remote sensing of vegetation seasonality and biomass burning emissions, climate-vegetation interaction, and land surface modeling.

Cited by 14 publications

References 234 publications

Fenología de los ecosistemas de alta montaña en Andalucía: Análisis de la tendencia estacional del SAVI (2000-2019)

Fenología de los ecosistemas de alta montaña en Andalucía: Análisis de la tendencia estacional del SAVI (2000-2019)

European Beech Spring Phenological Phase Prediction with UAV-derived Multispectral Indices and Machine Learning Regression

Effects of rainfall, temperature and photoperiod on the phenology of ephemeral resources for selected bushveld woody plant species in southern Africa

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