Journal articles on the topic 'Phenology'
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Macphie, Kirsty H., and Albert B. Phillimore. "Phenology." Current Biology 34, no. 5 (March 2024): R183—R188. http://dx.doi.org/10.1016/j.cub.2024.01.007.
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Colin Irvine. "Cognitive Phenology:." Interdisciplinary Literary Studies 16, no. 1 (2014): 160. http://dx.doi.org/10.5325/intelitestud.16.1.0160.
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Ma, Xin Ping, Hong Ying Bai, Ying Na He, and Shu Heng Li. "The Vegetation Remote Sensing Phenology of Qinling Mountains Based on the NDVI and the Response of Temperature to it." Applied Mechanics and Materials 700 (December 2014): 394–99. http://dx.doi.org/10.4028/www.scientific.net/amm.700.394.
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The acquisition vegetation phenology information by using time series of satellite data is an important aspect of the application of remote sensing and climate change research . Based on the MODOS NDVI time series of images in 2000-2010, Dynamic threshold method and GIS tools were used to extract the vegetation phenology parameters of Qinling Mountains in 2000-2010 , the accuracy of remote sensing phenology results was verified combined with the measured phenological data, And analyzed the characteristis of phenological variation and the relationship between temperature changes and the phenology of Qinling region,and quantified the extent of temperature change on vegetation phenology in a macro scale. Calculated :the trend of vegetation phenology variation based on the NDVI and the results of phenological data are consistent. Results show that NDVI has good revealed effect on vegetation phenology; From 2000 to 2010,it ahead of 1.8 days at the beginning period of vegetation phenology and late back 1.2 days at the end period ; The start phenology NDVI was generally greater than the late phenology on spatial distribution; The effective temperatures and the temperature in spring, growing period had a maximum influence on NDVI at beginning phenology period,the temperatures in summer and autumn had greater impact on the final NDVI .
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Xu, Lingling, Ben Niu, Xianzhou Zhang, and Yongtao He. "Dynamic Threshold of Carbon Phenology in Two Cold Temperate Grasslands in China." Remote Sensing 13, no. 4 (February 5, 2021): 574. http://dx.doi.org/10.3390/rs13040574.
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Plant phenology, especially the timing of the start and the end of the vegetation growing season (SOS and EOS), plays a major role in grassland ecosystem carbon cycles. As the second-largest grassland country in the world, China’s grasslands are mainly distributed in the northern cold temperate climate zone. The accuracies and relations of plant phenology estimations from multialgorithms and data resources are poorly understood. Here, we investigated vegetation phenology in two typical cold temperate grasslands, Haibei (HB) and Inner Mongolia (NM) grasslands, in China from 2001 to 2017. Compared to ground vegetation phenology observations, we analyzed the performance of the moderate resolution imaging spectroradiometer MODIS phenology products (MCD12Q2) and two remote sensing-based vegetation phenology algorithms from the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) time series (five satellite-based phenology algorithms). The optimal algorithm was used to compare with eddy covariance (EC)-based carbon phenology, and to calculate the thresholds of carbon phenology periods (SOSt and EOSt) in each site. Results showed that satellite-based phenology estimations (all five algorithms in this study) were strongly coupled with the temporal variation of the observed phenological period but significantly overestimated the SOS, predicting it to be over 21 days later than the field data. The carbon phenology thresholds of HB grassland (HB_SOSt and HB_EOSt) had a significant upward trend, with the multiyear average values being 0.14 and 0.29, respectively. In contrast, the thresholds of NM grasslands (NM_SOSt and NM_EOSt) also showed a certain upward trend, but it was not significant (p > 0.05), with the multiyear average values being 0.17 and 0.2, respectively. Our study suggested the thresholds of carbon phenology periods (SOSt and EOSt, %) could be simply and effectively estimated based on their significant relationship with the EC-based maximum of gross primary productivity observations (GPPmax) at a specific site and time. Therefore, this study suggested the thresholds of carbon phenology were not fixed even in a specific ecosystem, which also provided simple bridges between satellite-based vegetation phenology and EC-based carbon phenology in similar grasslands.
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Wang, Cong, Yijin Wu, Qiong Hu, Jie Hu, Yunping Chen, Shangrong Lin, and Qiaoyun Xie. "Comparison of Vegetation Phenology Derived from Solar-Induced Chlorophyll Fluorescence and Enhanced Vegetation Index, and Their Relationship with Climatic Limitations." Remote Sensing 14, no. 13 (June 23, 2022): 3018. http://dx.doi.org/10.3390/rs14133018.
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Satellite-based vegetation datasets enable vegetation phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting phenology from photosynthesis and greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF and EVI for estimating different phenology metrics, i.e., the start of season (SOS) and the end of season (EOS); however, the underlying mechanisms are unclear. In this study, we compared the SOS and EOS of natural ecosystems derived from SIF and EVI in China and explored the underlying mechanisms by investigating the relationships between the differences of phenology derived from SIF and EVI and climatic limiting factors (i.e., temperature, water and radiation). The results showed that the differences between phenology generated using SIF and EVI were diverse in space, which had a close relationship with climatic limitations. The increasing climatic limitation index could result in larger differences in phenology from SIF and EVI for each dominant climate-limited area. The phenology extracted using SIF was more correlated with climatic limiting factors than that using EVI, especially in water-limited areas, making it the main cause of the difference in phenology from SIF and EVI. These findings highlight the impact of climatic limitation on the differences of phenology from SIF and EVI and improve our understanding of land surface phenology from greenness and photosynthesis perspectives.
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Ding, Haiyong, Luming Xu, Andrew J. Elmore, and Yuli Shi. "Vegetation Phenology Influenced by Rapid Urbanization of The Yangtze Delta Region." Remote Sensing 12, no. 11 (June 1, 2020): 1783. http://dx.doi.org/10.3390/rs12111783.
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Impacts of urbanization and climate change on ecosystems are widely studied, but these drivers of change are often difficult to isolate from each other and interactions are complicated. Ecosystem responses to each of these drivers are perhaps most clearly seen in phenology changes due to global climate change (warming climate) and urbanization (heat island effect). The phenology of vegetation can influence many important ecological processes, including primary production, evapotranspiration, and plant fitness. Therefore, evaluating the interacting effects of urbanization and climate change on vegetation phenology has the potential to provide information about the long-term impact of global change. Using remotely sensed time series of vegetation on the Yangtze River Delta in China, this study evaluated the impacts of rapid urbanization and climate change on vegetation phenology along an urban to rural gradient over time. Phenology markers were extracted annually from an 18-year time series by fitting the asymmetric Gaussian function model. Thermal remote sensing acquired at daytime and nighttime was used to explore the relationship between land surface temperature and vegetation phenology. On average, the spring phenology marker was 9.6 days earlier and the autumn marker was 6.63 days later in urban areas compared with rural areas. The spring phenology of urban areas advanced and the autumn phenology delayed over time. Across space and time, warmer spring daytime and nighttime land surface temperatures were related to earlier spring, while autumn daytime and nighttime land surface temperatures were related to later autumn phenology. These results suggest that urbanization, through surface warming, compounds the effect of climate change on vegetation phenology.
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Harper, Geoffrey. "Lessons from Phenology." Sibbaldia: the International Journal of Botanic Garden Horticulture, no. 8 (October 31, 2010): 149–64. http://dx.doi.org/10.24823/sibbaldia.2010.143.
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Twenty provisional multiple-regression models based on a small data set are presented to account for the timing of first-flower date and other phenological events. Biological mechanisms are suggested to explain the pattern of temperature-dependent developmental stages. The implications for how plants and vegetation are likely to react to climate change are discussed, and attention is drawn to the importance of within-taxon variation in phenological behaviour.
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Meng, Lin. "Green with phenology." Science 374, no. 6571 (November 26, 2021): 1065–66. http://dx.doi.org/10.1126/science.abm8136.
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Tooke, F., and N. H. Battey. "Temperate flowering phenology." Journal of Experimental Botany 61, no. 11 (June 1, 2010): 2853–62. http://dx.doi.org/10.1093/jxb/erq165.
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White, J. W. "Predicting crop phenology." Agricultural Systems 39, no. 2 (January 1992): 229–30. http://dx.doi.org/10.1016/0308-521x(92)90110-a.
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Schwartz, Mark D. "Green-wave phenology." Nature 394, no. 6696 (August 1998): 839–40. http://dx.doi.org/10.1038/29670.
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Kalita, Himangshu, and Narayan Sharma. "Phenology: Nature’s Calendar." Resonance 28, no. 7 (August 17, 2023): 1117–33. http://dx.doi.org/10.1007/s12045-023-1641-1.
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Zhang, Jing, Shouzhi Chen, Zhaofei Wu, and Yongshuo H. Fu. "Review of vegetation phenology trends in China in a changing climate." Progress in Physical Geography: Earth and Environment 46, no. 6 (November 27, 2022): 829–45. http://dx.doi.org/10.1177/03091333221114737.
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Vegetation phenology is sensitive to climate change and has been defined as the footprint of ongoing climate change. Previous studies have shown that the spatial difference in China’s vegetation phenology varies substantially in both spring and autumn. Here, we reviewed phenological dynamics at the national and the regional scale of China over the period 1982−2020 using a remote sensing-based dataset and meta-analysis from phenological studies in China. We also explored the underlying mechanisms of both spring and autumn phenology and discussed potential phenological studies under future climate conditions. We found that, over the past four decades, the spring phenology advanced at a rate of 0.23 ± 0.47 days/year, while the autumn phenology was delayed at a rate of 0.17 ± 0.46 days/year. This led to an extended vegetation growth season of approximately 5 days per decade. The trends in the spring and autumn phenology were spatially specific in the Northern region, Northwest region, Qinghai–Tibet region, and Southern region: the change in spring phenology was −0.16, −0.46, −0.18, and −0.13 days/year, respectively, while the change in autumn phenology was 0.02, 0.32, 0.09, and 0.28 days/year, respectively. We also explored the dominant climatic drivers of regional phenological changes. We found that temperature was the dominant factor for spring phenology in cold regions, while precipitation, radiation, and temperature co-determined spring phenology in warm regions. The autumn phenology was affected by all three environmental cues but the effect of temperature was larger than that of radiation and precipitation across all regions. In future climate warming conditions, we recommend that studies focus on the phenological feedback mechanisms, such as the climatic and hydrological effects of vegetation changes, and agricultural phenology to investigate its fundamental role in crop productivity, especially under extreme climate events, to ensure national food security and ecological security.
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Meier, Michael, and Christof Bigler. "Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections." Geoscientific Model Development 16, no. 23 (December 11, 2023): 7171–201. http://dx.doi.org/10.5194/gmd-16-7171-2023.
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Abstract. Autumn leaf phenology marks the end of the growing season, during which trees assimilate atmospheric CO2. The length of the growing season is affected by climate change because autumn phenology responds to climatic conditions. Thus, the timing of autumn phenology is often modeled to assess possible climate change effects on future CO2-mitigating capacities and species compositions of forests. Projected trends have been mainly discussed with regards to model performance and climate change scenarios. However, there has been no systematic and thorough evaluation of how performance and projections are affected by the calibration approach. Here, we analyzed >2.3 million performances and 39 million projections across 21 process-oriented models of autumn leaf phenology, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for beech, oak, and larch from 500 central European sites each. Phenology models had the largest influence on model performance. The best-performing models were (1) driven by daily temperature, day length, and partly by seasonal temperature or spring leaf phenology; (2) calibrated with the generalized simulated annealing algorithm; and (3) based on systematically balanced or stratified samples. Autumn phenology was projected to shift between −13 and +20 d by 2080–2099 compared to 1980–1999. Climate scenarios and sites explained more than 80 % of the variance in these shifts and thus had an influence 8 to 22 times greater than the phenology models. Warmer climate scenarios and better-performing models predominantly projected larger backward shifts than cooler scenarios and poorer models. Our results justify inferences from comparisons of process-oriented phenology models to phenology-driving processes, and we advocate for species-specific models for such analyses and subsequent projections. For sound calibration, we recommend a combination of cross-validations and independent tests, using randomly selected sites from stratified bins based on mean annual temperature and average autumn phenology, respectively. Poor performance and little influence of phenology models on autumn phenology projections suggest that current models are overlooking relevant drivers. While the uncertain projections indicate an extension of the growing season, further studies are needed to develop models that adequately consider the relevant processes for autumn phenology.
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Medeiros, Rodolpho, João Andrade, Desirée Ramos, Magna Moura, Aldrin Martin Pérez-Marin, Carlos A. C. dos Santos, Bernardo Barbosa da Silva, and John Cunha. "Remote Sensing Phenology of the Brazilian Caatinga and Its Environmental Drivers." Remote Sensing 14, no. 11 (May 31, 2022): 2637. http://dx.doi.org/10.3390/rs14112637.
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The Caatinga is the largest nucleus of Seasonally Dry Tropical Forests (SDTF) in the Neotropics. The leafing patterns of SDTF vegetation are adapted to the current environmental and climate variability, but the impacts of climate change tend to alter plants’ phenology. Thus, it is necessary to characterise phenological parameters and evaluate the relationship between vegetation and environmental drivers. From this information, it is possible to identify the dominant forces in the environment that trigger the phenological dynamics of the Caatinga. In this way, remote sensing represents an essential tool to investigate the phenology of vegetation, particularly as it has a long series of vegetation monitoring and allows relationships with different environmental drivers. This study has two objectives: (i) estimate phenological parameters using an Enhanced Vegetation Index (EVI) time-series over 20 years, and (ii) characterise the relationship between phenologic dynamics and environmental drivers. TIMESAT software was used to determine four phenological parameters: Start Of Season (SOS), End Of Season (EOS), Length Of Season (LOS), and Amplitude (AMPL). Boxplots, Pearson’s, and partial correlation coefficients defined relationships between phenologic dynamics and environmental drivers. The non-parametric test of Fligner–Killeen was used to test the interannual variability in SOS and EOS. Our results show that the seasonality of vegetation growth in the Caatinga was different in the three experimental sites. The SOS was the parameter that presented the greatest variability in the days of the year (DOY), reaching a variation of 117 days. The sites with the highest SOS variability are the same ones that showed the lowest EOS variation. In addition, the values of LOS and AMPL are directly linked to the annual distribution of rainfall, and the longer the rainy season, the greater their values are. The variability of the natural cycles of the environmental drivers that regulate the ecosystem’s phenology and the influence on the Caatinga’s natural dynamics indicated a greater sensitivity of the phenologic dynamics to water availability, with precipitation being the limiting factor of the phenologic dynamics. Highlights: The EVI time series was efficient in estimating phenological parameters. The high variability of the start of season (SOS) occurred in sites with low variability of end of the season (EOS) and vice versa. The precipitation and water deficit presented a higher correlation coefficient with phenological dynamics. Length of Season (LOS) and amplitude (AMPL) are directly linked to the annual distribution of rainfall.
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Wang, Bo, Yu Liu, Qinghong Sheng, Jun Li, Jiahui Tao, and Zhijun Yan. "Rice Phenology Retrieval Based on Growth Curve Simulation and Multi-Temporal Sentinel-1 Data." Sustainability 14, no. 13 (June 30, 2022): 8009. http://dx.doi.org/10.3390/su14138009.
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The accurate estimation and monitoring of phenology is necessary for modern agricultural industries. For crops with short phenology occurrence times, such as rice, Sentinel-1 can be used to effectively monitor the growth status in different phenology periods within a short time interval. Therefore, this study proposes a method to monitor rice phenology based on growth curve simulation by constructing a polarized growth index (PGI) and obtaining a polarized growth curve. A recursive neural network is used to realize the classification of phenology and use it as prior knowledge of rice phenology to divide and extract the phenological interval and date of rice in 2021. The experimental results show that the average accuracy of neural network phenological interval division reaches 93.5%, and the average error between the extracted and measured phenological date is 3.08 days, which proves the application potential of the method. This study will contribute to the technical development of planning, management and maintenance of renewable energy infrastructure related to phenology.
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Li, Jiyuan, Xiao Feng, Jiangbin Yin, and Fang Chen. "Change Analysis of Spring Vegetation Green-Up Date in Qinba Mountains under the Support of Spatiotemporal Data Cube." Journal of Sensors 2020 (February 27, 2020): 1–12. http://dx.doi.org/10.1155/2020/6413654.
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In recent decades, global and local vegetation phenology has undergone significant changes due to the combination of climate change and human activities. Current researches have revealed the temporal and spatial distribution of vegetation phenology in large scale by using remote sensing data. However, researches on spatiotemporal differentiation of remote sensing phenology and its changes are limited which involves high-dimensional data processing and analysing. A new data model based on data cube technologies was proposed in the paper to efficiently organize remote sensing phenology and related reanalysis data in different scales. The multidimensional aggregation functions in the data cube promote the rapid discovery of the spatiotemporal differentiation of phenology. The exploratory analysis methods were extended to the data cube to mine the change characteristics of the long-term phenology and its influencing factors. Based on this method, the case study explored that the spring phenology of Qinba Mountains has a strong dependence on the topography, and the temperature plays a leading role in the vegetation green-up date distribution of the high-altitude areas while human activities dominate the low-altitude areas. The response of green-up trend slope seems to be the most sensitive at an altitude of about 2000 meters. This research provided a new approach for analysing phenology phenomena and its changes in Qinba Mountains that had the same reference value for other regional phenology studies.
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ZHU, Mengyao, Junhu DAI, Huanjiong WANG, Yulong HAO, Wei LIU, and Lijuan CAO. "A dataset of gridded phenology of woody plants in Europe from 1951 to 2021." China Scientific Data 9, no. 2 (June 30, 2024): 1–5. http://dx.doi.org/10.11922/11-6035.csd.2023.0068.zh.
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Plant phenology records the timing of plant cyclic growth events, and stand as one of the most important indicators of climate change. The monitoring and research of plant phenology is of great significance for understanding the response of ecosystems to global changes and simulating the material and energy balance of terrestrial ecosystems. Based on the ground observation phenological data of six representative woody plants compiled by the Pan European Phenology Project (PEP725) in the past 70 years, this paper employed three phenology models (Unichill, Unified and TSC) to predict and upscale the phenology data on the continental scale, and prepared a dataset of gridded phenology of woody plants in Europe. This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1 ° and a temporal resolution of one day. The quality assessment of the grid phenology data shows that the average error for FLD and FFD is 7.9 and 7.6 days respectively, which are close to simulation errors of spring phenology observed in other regional-scale studies, revealing the high accuracy of the data. This dataset can better characterize the temporal and spatial patterns of plant phenology at a continental scale in Europe, and provide an effective verification approach for plant phenology products from other sources. Furthermore, it can offer valuable data support for research on global change and terrestrial ecosystem simulation.
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Fu, Xuecheng, and Bao-Jie He. "Synergistic Impacts of Built-Up Characteristics and Background Climate on Urban Vegetation Phenology: Evidence from Beijing, China." Forests 15, no. 4 (April 21, 2024): 728. http://dx.doi.org/10.3390/f15040728.
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Vegetation is an important strategy for mitigating heat island effects, owed to its shading and evaporative cooling functions. However, urbanization has significantly affected regional vegetation phenology and can potentially weaken the cooling potential of vegetation. Previous studies have mainly focused on national and regional vegetation phenology, but local-scale vegetation phenology and dynamic variations in built-up areas remain unclear. Therefore, this study characterized the vegetation phenology in the densely built-up area of Beijing, China over the period of 2000–2020 based on high-resolution NDVI data using Savitzky–Golay filtering and explored its spatiotemporal characteristics and drivers. The results indicate that the vegetation phenology exhibits significant spatial heterogeneity and clustering characteristics. Compared with vegetation in peripheral blocks, vegetation in central urban blocks generally has an earlier start in the growing season (SOS), later end in the growing season (EOS), and a longer growing season length (GSL). However, the overall distribution of these parameters has experienced a process of decentralization along with urbanization. In terms of drivers, vegetation phenology indicators are mainly influenced by background climate. Specifically, SOS and GSL are mainly affected by temperature (TEP), whereas EOS is mainly influenced by annual precipitation (PRE). Additionally, local environmental factors, particularly the percentage of water body (WAP), also have an impact. Notably, the local environment and background climate have a synergistic effect on vegetation phenology, which is greater than their individual effects. Overall, this study extends the current knowledge on the response of vegetation phenology to urbanization by investigating long-term vegetation phenology dynamics in dense urban areas and provides new insights into the complex interactions between vegetation phenology and built environments.
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Basinger, Nicholas T., Katherine M. Jennings, Erin L. Hestir, David W. Monks, David L. Jordan, and Wesley J. Everman. "Phenology affects differentiation of crop and weed species using hyperspectral remote sensing." Weed Technology 34, no. 6 (August 18, 2020): 897–908. http://dx.doi.org/10.1017/wet.2020.92.
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AbstractThe effect of plant phenology and canopy structure of four crops and four weed species on reflectance spectra were evaluated in 2016 and 2017 using in situ spectroscopy. Leaf-level and canopy-level reflectance were collected at multiple phenologic time points in each growing season. Reflectance values at 2 wk after planting (WAP) in both years indicated strong spectral differences between species across the visible (VIS; 350–700 nm), near-infrared (NIR; 701–1,300 nm), shortwave-infrared I (SWIR1; 1,301–1,900 nm), and shortwave-infrared II (SWIR2; 1,901–2,500 nm) regions. Results from this study indicate that plant spectral reflectance changes with plant phenology and is influenced by plant biophysical characteristics. Canopy-level differences were detected in both years across all dates except for 1 WAP in 2017. Species with similar canopy types (e.g., broadleaf prostrate, broadleaf erect, or grass/sedge) were more readily discriminated from species with different canopy types. Asynchronous phenology between species also resulted in spectral differences between species. SWIR1 and SWIR2 wavelengths are often not included in multispectral sensors but should be considered for species differentiation. Results from this research indicate that wavelengths in SWIR1 and SWIR2 in conjunction with VIS and NIR reflectance can provide differentiation across plant phenologies and, therefore should be considered for use in future sensor technologies for species differentiation.
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Zhu, Enyan, Dan Fang, Lisu Chen, Youyou Qu, and Tao Liu. "The Impact of Urbanization on Spatial–Temporal Variation in Vegetation Phenology: A Case Study of the Yangtze River Delta, China." Remote Sensing 16, no. 5 (March 5, 2024): 914. http://dx.doi.org/10.3390/rs16050914.
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The response of vegetation phenology to urbanization has become a growing concern. As impervious surfaces change as urbanization advances, the variation in vegetation phenology at the dynamic urbanization level was analyzed to significantly quantify the impact of urbanization processes on vegetation phenology. Based on the MOD13Q1 vegetation index product from 2001 to 2020, vegetation phenology parameters, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (GSL), were extracted, and the spatial–temporal variation in vegetation phenology, as well as its response to urbanization, was comprehensively analyzed. The results reveal that (1) from 2001 to 2020, the average rates of change for the SOS, EOS, and GSL were 0.41, 0.16, and 0.57 days, respectively. (2) The vegetation phenology changes showed significant spatial–temporal differences at the urbanization level. With each 10% increase in the urbanization level, the SOS and EOS were advanced and delayed by 0.38 and 0.34 days, respectively. (3) The urban thermal environment was a major factor in the impact of urbanization on the SOS and EOS. Overall, this study elucidated the dynamic reflection of urbanization in phenology and revealed the complex effects of urbanization on vegetation phenology, thus helping policymakers to develop effective strategies to improve urban ecological management.
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Li, Xuecao, Yuyu Zhou, Lin Meng, Ghassem R. Asrar, Chaoqun Lu, and Qiusheng Wu. "A dataset of 30 m annual vegetation phenology indicators (1985–2015) in urban areas of the conterminous United States." Earth System Science Data 11, no. 2 (June 21, 2019): 881–94. http://dx.doi.org/10.5194/essd-11-881-2019.
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Abstract. Medium-resolution satellite observations show great potential for characterizing seasonal and annual dynamics of vegetation phenology in urban domains from local to regional and global scales. However, most previous studies were conducted using coarse-resolution data, which are inadequate for characterizing the spatiotemporal dynamics of vegetation phenology in urban domains. In this study, we produced an annual vegetation phenology dataset in urban ecosystems for the conterminous United States (US), using all available Landsat images on the Google Earth Engine (GEE) platform. First, we characterized the long-term mean seasonal pattern of phenology indicators of the start of season (SOS) and the end of season (EOS), using a double logistic model. Then, we identified the annual variability of these two phenology indicators by measuring the difference of dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived phenology indicators agree well with in situ observations from the PhenoCam network and Harvard Forest. Comparing with results derived from the moderate-resolution imaging spectroradiometer (MODIS) data, our Landsat-derived phenology indicators can provide more spatial details. Also, we found the temporal trends of phenology indicators (e.g., SOS) derived from Landsat and MODIS are consistent overall, but the Landsat-derived results from 1985 offer a longer temporal span compared to MODIS from 2001 to present. In general, there is a spatially explicit pattern of phenology indicators from the north to the south in cities in the conterminous US, with an overall advanced SOS in the past 3 decades. The derived phenology product in the US urban domains at the national level is of great use for urban ecology studies for its medium spatial resolution (30 m) and long temporal span (30 years). The data are available at https://doi.org/10.6084/m9.figshare.7685645.v5.
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Xie, Zhiying, Wenquan Zhu, Bangke He, Kun Qiao, Pei Zhan, and Xin Huang. "A background-free phenology index for improved monitoring of vegetation phenology." Agricultural and Forest Meteorology 315 (March 2022): 108826. http://dx.doi.org/10.1016/j.agrformet.2022.108826.
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Cao, Heqin, Yan Hua, Xin Liang, Zexu Long, Jinzhe Qi, Dusu Wen, Nathan James Roberts, Haijun Su, and Guangshun Jiang. "Wavelet Analysis Reveals Phenology Mismatch between Leaf Phenology of Temperate Forest Plants and the Siberian Roe Deer Molting under Global Warming." Remote Sensing 14, no. 16 (August 11, 2022): 3901. http://dx.doi.org/10.3390/rs14163901.
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Global warming is deeply influencing various ecological processes, especially regarding the phenological synchronization pattern between species, but more cases around the world are needed to reveal it. We report how the forest leaf phenology and ungulate molting respond differently to climate change, and investigate whether it will result in a potential phenology mismatch. Here, we explored how climate change might alter phenological synchronization between forest leaf phenology and Siberian roe deer (Capreolus pygargus) molting in northeast China based on a camera-trapping dataset of seven consecutive years, analyzing forest leaf phenology in combination with records of Siberian roe deer molting over the same period by means of wavelet analysis. We found that the start of the growing season of forest leaf phenology was advanced, while the end of the growing season was delayed, so that the length of the growing season was prolonged. Meanwhile, the start and the end of the molting of Siberian roe deer were both advanced in spring, but in autumn, the start of molting was delayed while the end of molting was advanced. The results of wavelet analysis also suggested the time lag of synchronization fluctuated slightly from year to year between forest leaf phenology and Siberian roe deer molting, with a potential phenology mismatch in spring, indicating the effect of global warming on SRD to forest leaf phenology. Overall, our study provides new insight into the synchronization between forest leaf phenology and ungulate molting, and demonstrates feasible approaches to data collection and analysis using camera-trapping data to explore global warming issues.
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Hartmann, Eva, Jan-Peter Schulz, Ruben Seibert, Marius Schmidt, Mingyue Zhang, Jürg Luterbacher, and Merja H. Tölle. "Impact of Environmental Conditions on Grass Phenology in the Regional Climate Model COSMO-CLM." Atmosphere 11, no. 12 (December 16, 2020): 1364. http://dx.doi.org/10.3390/atmos11121364.
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Feedbacks of plant phenology to the regional climate system affect fluxes of energy, water, CO2, biogenic volatile organic compounds as well as canopy conductance, surface roughness length, and are influencing the seasonality of albedo. We performed simulations with the regional climate model COSMO-CLM (CCLM) at three locations in Germany covering the period 1999 to 2015 in order to study the sensitivity of grass phenology to different environmental conditions by implementing a new phenology module. We provide new evidence that the annually-recurring standard phenology of CCLM is improved by the new calculation of leaf area index (LAI) dependent upon surface temperature, day length, and water availability. Results with the new phenology implemented in the model show a significantly higher correlation with observations than simulations with the standard phenology. The interannual variability of LAI improves the representation of vegetation in years with extremely warm winter/spring (e.g., 2007) or extremely dry summer (e.g., 2003) and shows a more realistic growth period. The effect of the newly implemented phenology on atmospheric variables is small but tends to be positive. It should be used in future applications with an extension on more plant functional types.
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Forrest, Jessica, and Abraham J. Miller-Rushing. "Toward a synthetic understanding of the role of phenology in ecology and evolution." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1555 (October 12, 2010): 3101–12. http://dx.doi.org/10.1098/rstb.2010.0145.
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Phenology affects nearly all aspects of ecology and evolution. Virtually all biological phenomena—from individual physiology to interspecific relationships to global nutrient fluxes—have annual cycles and are influenced by the timing of abiotic events. Recent years have seen a surge of interest in this topic, as an increasing number of studies document phenological responses to climate change. Much recent research has addressed the genetic controls on phenology, modelling techniques and ecosystem-level and evolutionary consequences of phenological change. To date, however, these efforts have tended to proceed independently. Here, we bring together some of these disparate lines of inquiry to clarify vocabulary, facilitate comparisons among habitat types and promote the integration of ideas and methodologies across different disciplines and scales. We discuss the relationship between phenology and life history, the distinction between organismal- and population-level perspectives on phenology and the influence of phenology on evolutionary processes, communities and ecosystems. Future work should focus on linking ecological and physiological aspects of phenology, understanding the demographic effects of phenological change and explicitly accounting for seasonality and phenology in forecasts of ecological and evolutionary responses to climate change.
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Gao, Yu, Zhaoling Hu, Zhen Wang, Qiang Shi, Dan Chen, Shuai Wu, Yajun Gao, and Yuanzhi Zhang. "Phenology Metrics for Vegetation Type Classification in Estuarine Wetlands Using Satellite Imagery." Sustainability 15, no. 2 (January 11, 2023): 1373. http://dx.doi.org/10.3390/su15021373.
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While the efficiency of incorporating phenology features into vegetation type classification, in general, and coastal wetland vegetation classification, in particular, has been verified, it is difficult to acquire high-spatial-resolution (HSR) images taken at appropriate times for vegetation identification using phenology features because of the coastal climate and the HSR satellite imaging cycle. To strengthen phenology feature differences, in this study, we constructed vegetation phenology metrics according to vegetation NDVI time series curves fitted by samples collected from the Linhong Estuary Wetland and Liezi Estuary Wetland based on Gao Fen (GF) series satellite images taken between 2018 and 2022. Next, we calculated the phenology metrics using GF series satellite imagery taken over the most recent complete phenology cycle: 21 October 2020, 9 January 2021, 19 February 2021, and 8 May 2021. Five vegetation type classifications in the Linhong Estuary Wetland were carried out using single images of 21 October 2020 and 8 May 2021, along with their combination and the further addition of phenology metrics. From our comparison and analysis, the following findings emerged: Combining the images taken in 21 October 2020 and 8 May 2021 provided better vegetation classification accuracy than any single image, and the overall accuracy was, respectively, increased from 47% and 48% to 67%, while the corresponding kappa was increased from 33% and 34% to 58%; however, adding phenology metrics further improved the accuracy by decreasing the effect of some confusion among different vegetation types, and the overall accuracy and kappa were further improved to 75% and 69%, respectively. Though some problems remain to be further dealt with, this exploration offers helpful insights into coastal wetland vegetation classification using phenology based on HSR imagery.
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Čehulić, Ivica, Krunoslav Sever, Ida Katičić Bogdan, Anamarija Jazbec, Željko Škvorc, and Saša Bogdan. "Drought Impact on Leaf Phenology and Spring Frost Susceptibility in a Quercus robur L. Provenance Trial." Forests 10, no. 1 (January 11, 2019): 50. http://dx.doi.org/10.3390/f10010050.
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Research highlights: The susceptibility of oaks to late spring and early autumn frosts is directly related to their leaf phenology. Drought may alter the leaf phenology and therefore frost tolerance of oaks. However, the effects of drought on oak leaf phenology and frost resistance have not been thoroughly studied. Background and objectives: One of the consequences of climate change is an increase in the frequency of dry episodes during the vegetation period. Pedunculate oak (Quercus robur L.) is an economically and ecologically important forest tree species that prefers humid habitats. Therefore, knowledge of the impact of drought on this species is of great importance for the adaptation of forestry strategies and practices to altered environmental conditions. The aim of this study was to determine the impact of drought on leaf phenology and spring frost susceptibility in nine provenances. Materials and methods: One-year-old saplings originating from nine European provenances were used in the trial. The saplings were exposed to experimental drought and then re-watered in two subsequent years. Spring and autumn leaf phenology were scored. The trial was impacted by a late spring frost in the third year, and the resulting leaf frost injury was scored. The effects of drought treatment on the phenology and frost susceptibility of plants from the provenances were analysed. Results: Leaf phenology of plants from most of the studied provenances was significantly influenced by the drought treatment (p < 0.001). Drought induced a carry-over effect on flushing phenology, which was observed as delayed bud burst (from 0.6 to 2.4 days) in the second year and as advanced bud burst (from 0.1 to 6.3 days) in the third year. Therefore, opposite shifts in flushing phenology may be induced as a result of differences in the time span when plants sense water deficits. In contrast to flushing, autumn leaf phenology was unambiguously delayed following the drought treatments for all studied provenances (from 2.1 to 25.8 days). Differences in late frost susceptibility were predominantly caused by among-provenance differences in flushing phenology. However, the drought treatment significantly increased frost susceptibility in the plants (the rate of frost-injured plants per provenance increased from 3% to 78%). This higher susceptibility to spring frost was most likely caused by the advanced flushing phenology that resulted from the drought treatment in the previous year.
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Hou, Guanyu, Xiuliang Yuan, Shixin Wu, Xiaofei Ma, Zihui Zhang, Xingwen Cao, Conghui Xie, Qing Ling, Weiyi Long, and Geping Luo. "Phenological Changes and Driving Forces of Lake Ice in Central Asia from 2002 to 2020." Remote Sensing 14, no. 19 (October 7, 2022): 4992. http://dx.doi.org/10.3390/rs14194992.
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Lake ice phenology is an indicator of past and present climate, it is sensitive to regional and global climate change. In the past few decades, the climate of Central Asia has changed significantly due to global warming and anthropogenic activities. However, there are few studies on the lake ice phenology in Central Asia. In this study, the lake ice phenology of 53 lakes in Central Asia were extracted using MODIS daily LST products from 2002 to 2020. The results show that MODIS-extracted lake ice phenology is generally consistent with Landsat-extracted and AVHRR-extracted lake ice phenology. Generally, lakes in Central Asia start to freeze from October to December. The trends in the lake ice phenology show strong regional differences. Lakes distributed along the Kunlun Mountains show overall delayed trends in all lake ice phenology variables, while lakes located in southwestern Central Asia show clear advancing trends in the freeze-up start dates (7.06 days) and breakup end dates (6.81 days). Correlations between the phenology of lake ice and local and climatic factors suggest that the ice breakup process and the duration of its complete coverage depend more on heat, while precipitation mainly affects the freezing time of the ice. Wind speed mainly affects the time of completely frozen of ice. In general, the breakup process is more susceptible to climatic factors, while local factors have strong influences on the freeze-up process.
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Tian, Jiaqi, Xiaolin Zhu, Jin Wu, Miaogen Shen, and Jin Chen. "Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology." Remote Sensing 12, no. 1 (January 1, 2020): 117. http://dx.doi.org/10.3390/rs12010117.
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Numerous investigations of urbanization effects on vegetation spring phenology using satellite images have reached a consensus that vegetation spring phenology in urban areas occurs earlier than in surrounding rural areas. Nevertheless, the magnitude of this rural–urban difference is quite different among these studies, especially for studies over the same areas, which implies large uncertainties. One possible reason is that the satellite images used in these studies have different spatial resolutions from 30 m to 1 km. In this study, we investigated the impact of spatial resolution on the rural–urban difference of vegetation spring phenology using satellite images at different spatial resolutions. To be exact, we first generated a dense 10 m NDVI time series through harmonizing Sentinel-2 and Landsat-8 images by data fusion method, and then resampled the 10 m time series to coarser resolutions from 30 m to 8 km to simulate images at different resolutions. Afterwards, to quantify urbanization effects, vegetation spring phenology at each resolution was extracted by a widely used tool, TIMESAT. Last, we calculated the difference between rural and urban areas using an urban extent map derived from NPP VIIRS nighttime light data. Our results reveal: (1) vegetation spring phenology in urban areas happen earlier than rural areas no matter which spatial resolution from 10 m to 8 km is used, (2) the rural–urban difference in vegetation spring phenology is amplified with spatial resolution, i.e., coarse satellite images overestimate the urbanization effects on vegetation spring phenology, and (3) the underlying reason of this overestimation is that the majority of urban pixels in coarser images have higher diversity in terms of spring phenology dates, which leads to spring phenology detected from coarser NDVI time series earlier than the actual dates. This study indicates that spatial resolution is an important factor that affects the accuracy of the assessment of urbanization effects on vegetation spring phenology. For future studies, we suggest that satellite images with a fine spatial resolution are more appropriate to explore urbanization effects on vegetation spring phenology if vegetation species in urban areas is very diverse.
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RAMÍREZ MARTÍNEZ, ADRIANA, DEMETRIA MARTHA MONDRAGÓN CHAPARRO, and RAÚL RIVERA GARCÍA. "VASCULAR EPIPHYTES: THE UGLY DUCKLING OF PHENOLOGICAL STUDIES." Acta Biológica Colombiana 26, no. 2 (January 14, 2021): 247–61. http://dx.doi.org/10.15446/abc.v26n2.83473.
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The phenology of vascular epiphytes, which represent account for about 10 % of the world’s flowering plants and perform important ecological functions, has been just partially explored. Since phenology is a key tool for the management and conservation of species, the objective of this review was to synthesize the information published so far about the phenology of vascular epiphytes, detect gaps of knowledge, and suggest future lines of investigation to understand the underlying mechanisms. We conducted an online search for articles in Google Scholar and in the ISI Web of Science database from 1800 to 2020, with different combinations of keywords. 107 studies addressing the phenology of different holo-epiphyte species were found; 88 % of the studies were performed in the Neotropic, especially in tropical and subtropical wet forests. The phenology of only ca.2% (418 spp.) of all reported epiphyte species has been explored. There is a bias toward the study of the flowering and fruiting phenology in members of the Orchidaceae (192 spp.) and Bromeliaceae (124 spp.) families. In general, the vegetative and reproductive phenology of epiphytes tends to be seasonal; however, there is a huge gap in our understanding of the proximate and ultimate factors involved. Future research should explicitly focus on studying those factors.
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Garroutte, Erica, and Andrew Hansen. "Using Field Data to Validate Satellite Models of Elk Forage in the Upper Yellowstone River Basin." UW National Parks Service Research Station Annual Reports 36 (January 1, 2013): 134–37. http://dx.doi.org/10.13001/uwnpsrc.2013.4003.
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Spatial and temporal variations in grassland phenology are thought to play a critical role in migration patterns of large herbivores in the Greater Yellowstone Ecosystem. Phenology, referring to the timing of green-up in this study, is directly related to biomass and forage quality. Migratory elk (Cervus elaphus), therefore, are believed to follow phenology across an elevation gradient during the growing season to maximize their access to high quality and quantity of forage. Concern that climate change and human land use alterations of phenology may impact the benefits of elk migration highlights the need for landscape-scale vegetation phenology monitoring. Satellite-derived Normalized Difference Vegetation Index (NDVI) shows potential as a remote sensing tool to predict landscape-level shifts in grassland phenology, but is limited by a lack of validation at varying scales, seasons, and in human land use areas. This study is focused on validating the accuracy of satellite-derived NDVI in estimating grassland phenology, biomass, and forage quality throughout the summer growing season within elk migratory ranges in the Upper Yellowstone River Basin. Results from this study will provide managers and researchers with information on the accuracy of NDVI as a tool for monitoring the effects of climate change and human land use on grassland dynamics relevant to migratory elk.
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Dodonov, P., C. B. Zanelli, and D. M. Silva-Matos. "Effects of an accidental dry-season fire on the reproductive phenology of two Neotropical savanna shrubs." Brazilian Journal of Biology 78, no. 3 (October 30, 2017): 564–73. http://dx.doi.org/10.1590/1519-6984.174660.
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Abstract Fire is a recurrent disturbance in savanna vegetation and savanna species are adapted to it. Even so, fire may affect various aspects of plant ecology, including phenology. We studied the effects of a spatially heterogeneous fire on the reproductive phenology of two dominant woody plant species, Miconia albicans (Melastomataceae) and Schefflera vinosa (Araliaceae), in a savanna area in South-eastern Brazil. The study site was partially burnt by a dry-season accidental fire in August 2006, and we monitored the phenolology of 30 burnt and 30 unburnt individuals of each species between September 2007 and September 2008. We used restricted randomizations to assess phenological differences between the burnt and unburnt individuals. Fire had negative effects on the phenology of M. albicans, with a smaller production of reproductive structures in general and of floral buds, total fruits, and ripe fruits in burnt plants. All unburnt but only 16% of the burnt M. albicans plants produced ripe fruits during the study. Fire effects on S. vinosa were smaller, but there was a greater production of floral buds and fruits (but not ripe fruits) by burnt plants; approximately 90% of the individuals of S. vinosa produced ripe fruits during the study, regardless of having been burnt or not. The differences between the two species may be related to S. vinosa’s faster growth and absence from the seed bank at the study site, whereas M. albicans grows more slowly and is dominant in the seed bank.
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Niu, Quandi, Xuecao Li, Jianxi Huang, Hai Huang, Xianda Huang, Wei Su, and Wenping Yuan. "A 30 m annual maize phenology dataset from 1985 to 2020 in China." Earth System Science Data 14, no. 6 (June 23, 2022): 2851–64. http://dx.doi.org/10.5194/essd-14-2851-2022.
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Abstract. Crop phenology indicators provide essential information on crop growth phases, which are highly required for agroecosystem management and yield estimation. Previous crop phenology studies were mainly conducted using coarse-resolution (e.g., 500 m) satellite data, such as the moderate resolution imaging spectroradiometer (MODIS) data. However, precision agriculture requires higher resolution phenology information of crops for better agroecosystem management, and this requirement can be met by long-term and fine-resolution Landsat observations. In this study, we generated the first national maize phenology product with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in China, using all available Landsat images on the Google Earth Engine (GEE) platform. First, we extracted long-term mean phenological indicators using the harmonic model, including the v3 (i.e., the date when the third leaf is fully expanded) and the maturity phases (i.e., when the dry weight of maize grains first reaches the maximum). Second, we identified the annual dynamics of phenological indicators by measuring the difference in dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived maize phenology datasets are consistent with in situ observations from the agricultural meteorological stations and the PhenoCam network. Besides, the derived fine-resolution phenology dataset agrees well with the MODIS phenology product regarding the spatial patterns and temporal dynamics. Furthermore, we observed a noticeable difference in maize phenology temporal trends before and after 2000, which is likely attributable to the changes in temperature and precipitation, which further altered the farming activities. The extracted maize phenology dataset can support precise yield estimation and deepen our understanding of the future agroecosystem response to global warming. The data are available at https://doi.org/10.6084/m9.figshare.16437054 (Niu et al., 2021).
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Chen, Zhizhong, Mei Zan, Jingjing Kong, Shunfa Yang, and Cong Xue. "Phenology of Vegetation in Arid Northwest China Based on Sun-Induced Chlorophyll Fluorescence." Forests 14, no. 12 (November 24, 2023): 2310. http://dx.doi.org/10.3390/f14122310.
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The accurate monitoring of vegetation phenology is critical for carbon sequestration and sink enhancement. Vegetation phenology in arid zones is more sensitive to climate responses; therefore, it is important to conduct research on phenology in arid zones in response to global climate change. This study compared the applicability of the enhanced vegetation index (EVI), which is superior in arid zones, and global solar-induced chlorophyll fluorescence (GOSIF), which has a high spatial resolution, in extracting vegetation phenology in arid zones, and explored the mechanism of the differences in the effects of environmental factors on the phenology of different vegetation types. Therefore, this study employed a global solar-induced chlorophyll fluorescence (GOSIF) dataset to determine the start and end of the vegetation growth season (SOSSIF and EOSSIF, respectively) in the arid zone of Northwest China from 2001 to 2019. The results were compared with those from the EVI-based MODIS climate product MCD12Q2 (SOSEVI and EOSEVI). Variations in the sensitivity of these climatic datasets concerning temperature, precipitation, and standardised precipitation evapotranspiration index (SPEI) were assessed through partial correlation analysis. Results: Compared to the MCD12Q2 climatic products, SOSSIF and EOSSIF closely matched the observed climate data in the study area. Spring onset was delayed at higher altitudes and latitudes, and the end of the growing season occurred earlier in these areas. Both SOSSIF and EOSSIF significantly advanced from 2001 to 2019 (trend degrees −0.22 and −0.48, respectively). Spring vegetation phenology was chiefly influenced by precipitation while autumn vegetation phenology was driven by both precipitation and SPEI. GOSIF-based climate data provides a more accurate representation of vegetation phenology compared to traditional vegetation indices. The findings of this study contribute to a deeper understanding of the potential ability of EVI and SIF to reveal the influence of vegetation phenology on the carbon cycle.
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Su, Lei, Tao Che, and Liyun Dai. "Variation in Ice Phenology of Large Lakes over the Northern Hemisphere Based on Passive Microwave Remote Sensing Data." Remote Sensing 13, no. 7 (April 4, 2021): 1389. http://dx.doi.org/10.3390/rs13071389.
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Ice phenology data of 22 large lakes of the Northern Hemisphere for 40 years (1979–2018) have been retrieved from passive microwave remote sensing brightness temperature (Tb). The results were compared with site-observation data and visual interpretation from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectivity products images (MOD09GA). The mean absolute errors of four lake ice phenology parameters, including freeze-up start date (FUS), freeze-up end date (FUE), break-up start date (BUS), and break-up end date (BUE) against MODIS-derived ice phenology were 2.50, 2.33, 1.98, and 3.27 days, respectively. The long-term variation in lake ice phenology indicates that FUS and FUE are delayed; BUS and BUE are earlier; ice duration (ID) and complete ice duration (CID) have a general decreasing trend. The average change rates of FUS, FUE, BUS, BUE, ID, and CID of lakes in this study from 1979 to 2018 were 0.23, 0.23, −0.17, −0.33, −0.67, and −0.48 days/year, respectively. Air temperature and latitude are two dominant driving factors of lake ice phenology. Lake ice phenology for the period 2021–2100 was predicted by the relationship between ice phenology and air temperature for each lake. Compared with lake ice phenology changes from 1990 to 2010, FUS is projected to be delayed by 3.1 days and 11.8 days under Representative Concentration Pathways (RCPs) 2.6 and 8.5 scenarios, respectively; BUS is projected to be earlier by 3.3 days and 10.7 days, respectively; and ice duration from 2080 to 2100 will decrease by 6.5 days and 21.9 days, respectively.
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Yang, Fan, Chao Liu, Qianqian Chen, Jianbin Lai, and Tiegang Liu. "Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems." Remote Sensing 16, no. 9 (April 29, 2024): 1580. http://dx.doi.org/10.3390/rs16091580.
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Carbon uptake of vegetation is controlled by phenology and photosynthetic carbon uptake capacity. However, our knowledge of the seasonal responses of vegetation productivity to phenological and physiological changes in alpine ecosystems is still weak. In this study, we quantified the spatio-temporal variations of vegetation phenology and gross primary productivity (GPP) across the source region of the Yellow River (SRYR) by analyzing MODIS-derived vegetation phenology and GPP from 2001 to 2019, and explored how vegetation phenology and maximum carbon uptake capacity (GPPmax) affected seasonal GPP over the region. Our results showed that the SRYR experienced significantly advanced trends (p < 0.05) for both start (SOS) and peak (POS) of the growing season from 2001 to 2019. Spring GPP (GPPspr) had a significantly increasing trend (p < 0.01), and the earlier SOS had obvious positive effects on GPPspr. Summer GPP (GPPsum) was significantly and negatively correlated to POS (p < 0.05). In addition, GPPmax had a significant and positive correlation with GPPsum and GPPann (p < 0.01), respectively. It was found that an earlier spring-summer phenology and higher photosynthetic peak enhanced the photosynthetic efficiency of vegetation in spring and summer and altered the seasonal patterns of vegetation productivity in the SRYR under warming and wetting climates. This study indicated that not only spring and autumn phenology but also summer phenology and maximum carbon uptake capacity should be regarded as crucial indicators regulating the carbon uptake process in alpine ecosystems. This research provides important information about how changes in phenology affect vegetation productivity in alpine ecosystems under global climate warming.
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Zhu, Mengyao, Junhu Dai, Huanjiong Wang, Juha M. Alatalo, Wei Liu, Yulong Hao, and Quansheng Ge. "Mapping 24 woody plant species phenology and ground forest phenology over China from 1951 to 2020." Earth System Science Data 16, no. 1 (January 11, 2024): 277–93. http://dx.doi.org/10.5194/essd-16-277-2024.
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Abstract. Plant phenology refers to cyclic plant growth events, and is one of the most important indicators of climate change. Integration of plant phenology information is crucial for understanding the ecosystem response to global change and modeling the material and energy balance of terrestrial ecosystems. Utilizing 24 552 in situ phenological observations of 24 representative woody plant species from the Chinese Phenology Observation Network (CPON), we have developed maps delineating species phenology (SP) and ground phenology (GP) of forests over China from 1951 to 2020. These maps offer a detailed spatial resolution of 0.1∘ and a temporal resolution of 1 d. Our method involves a model-based approach to upscale in situ phenological observations to SP maps, followed by the application of weighted average and quantile methods to derive GP maps from the SP data. The resulting SP maps for the 24 woody plants exhibit a high degree of concordance with in situ observations, manifesting an average deviation of 6.9 d for spring and 10.8 d for autumn phenological events. Moreover, the GP maps demonstrate robust alignment with extant land surface phenology (LSP) products sourced from remote sensing data, particularly within deciduous forests, where the average discrepancy is 8.8 d in spring and 15.1 d in autumn. This dataset provides an independent and reliable phenology data source for China on a long-time scale of 70 years, and contributes to more comprehensive research on plant phenology and climate change at both regional and national scales. The dataset can be accessed at https://doi.org/10.57760/sciencedb.07995 (Zhu and Dai, 2023).
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Bibi, Adeela. "STUDY OF PHENOLOGICAL BEHAVIOR OF PLANTS OF LOWER TANAWAL, ABBOTTABAD, PAKISTAN." International Journal of Research -GRANTHAALAYAH 9, no. 12 (December 30, 2021): 133–45. http://dx.doi.org/10.29121/granthaalayah.v9.i12.2021.4415.
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The purpose of this research study to explore the phonological behavior of plants of Lower Tanawal, Pakistan. The phenology of the 286 plants species belonging to 86 families from 80 stands of the Lower Tanawal Pakistan were documented during the different season of the year. It was observed that maximum flowering were recorded in March-April whereas maximum fruiting was noted in June-July. Plant phenology provides knowledge about the effects of environment on flowering and fruiting behavior. This is the first research work on phenology of plants of Lower Tanawal because no work was done on the phenology in past.
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Chaves, B., M. R. Salazar, T. Schmidt, N. Dasgupta, and G. Hoogenboom. "Modeling apple bloom phenology." Acta Horticulturae, no. 1160 (May 2017): 201–6. http://dx.doi.org/10.17660/actahortic.2017.1160.29.
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Siburian, Rima Herlina. "Phenology of Cinnamomum cullilawan." Median : Jurnal Ilmu Ilmu Eksakta 14, no. 1 (February 28, 2022): 1–6. http://dx.doi.org/10.33506/md.v14i1.1667.
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Abstract Cinnamomum cullilawan is one type of forest plants that is included in the type of aromatic plants. The purpose of this study was to determine the flowering development process as a basic information for the development and the breeding of this plant. The method used in this research was a descriptive method. The results of the observations indicated that the process of flowering C. cullilawan can be grouped into three major parts: the stage of initiation, budding and flowering, where each stage requires different formation times. At the initiation stage, since the emergence of generative shoots on the armpit until the formation of panicles takes two months. Furthermore, the stage of the appearance of buds at the ends of panicles and the formation of reproduction of flowers required two months period. After that the flowers will break slightly and then the petals will turn black and fall in the second week, after the flower process has blossomed.
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REITSCHLÄGER, J. DAVID. "Phenology of Spring Barley." Kvasny Prumysl 53, no. 6 (June 1, 2007): 178–80. http://dx.doi.org/10.18832/kp2007010.
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Norquay, K. J. O., and C. K. R. Willis. "Hibernation phenology ofMyotis lucifugus." Journal of Zoology 294, no. 2 (June 17, 2014): 85–92. http://dx.doi.org/10.1111/jzo.12155.
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Korner, C., and D. Basler. "Phenology Under Global Warming." Science 327, no. 5972 (March 18, 2010): 1461–62. http://dx.doi.org/10.1126/science.1186473.
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Forchhammer, Mads C., Eric Post, and Nils Chr Stenseth. "Breeding phenology and climate⃛." Nature 391, no. 6662 (January 1998): 29–30. http://dx.doi.org/10.1038/34070.
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Mayer, Amy. "Phenology and Citizen Science." BioScience 60, no. 3 (March 2010): 172–75. http://dx.doi.org/10.1525/bio.2010.60.3.3.
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Aide, T. Mitchell, and Todd Dawson. "Data wanted on phenology." Journal of Tropical Ecology 5, no. 2 (May 1989): 238. http://dx.doi.org/10.1017/s0266467400003527.
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van Vliet, Arnold J. H., Rudolf S. de Groot, Yvette Bellens, Peter Braun, Robert Bruegger, Ekko Bruns, Jan Clevers, et al. "The European Phenology Network." International Journal of Biometeorology 47, no. 4 (May 7, 2003): 202–12. http://dx.doi.org/10.1007/s00484-003-0174-2.
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Pendleton, Bonnie B., George L. Teetes, and Gary C. Peterson. "Phenology of Sorghum Flowering." Crop Science 34, no. 5 (September 1994): 1263–66. http://dx.doi.org/10.2135/cropsci1994.0011183x003400050023x.
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Grant, R. F. "Simulation of Maize Phenology." Agronomy Journal 81, no. 3 (May 1989): 451–57. http://dx.doi.org/10.2134/agronj1989.00021962008100030011x.
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