Relevant bibliographies by topics / Phenology Mathematics

Academic literature on the topic 'Phenology Mathematics'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Phenology Mathematics"

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Liu, Jun, Xi Yang, Hao Long Liu, and Zhi Qiao. "Algorithms and Applications in Grass Growth Monitoring." Abstract and Applied Analysis 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/508315.

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Monitoring vegetation phonology using satellite data has been an area of growing research interest in recent decades. Validation is an essential issue in land surface phenology study at large scale. In this paper, double logistic function-fitting algorithm was used to retrieve phenophases for grassland in North China from a consistently processed Moderate Resolution Spectrodiometer (MODIS) dataset. Then, the accuracy of the satellite-based estimates was assessed using field phenology observations. Results show that the method is valid to identify vegetation phenology with good success. The phenophases derived from satellite and observed on ground are generally similar. Greenup onset dates identified by Normalized Difference Vegetation Index (NDVI) and in situ observed dates showed general agreement. There is an excellent agreement between the dates of maturity onset determined by MODIS and the field observations. The satellite-derived length of vegetation growing season is generally consistent with the surface observation.
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Yurk, Brian P., and James A. Powell. "Modeling the Evolution of Insect Phenology." Bulletin of Mathematical Biology 71, no. 4 (December 20, 2008): 952–79. http://dx.doi.org/10.1007/s11538-008-9389-z.

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3

Alles, Guilherme Rezende, João L. D. Comba, Jean-Marc Vincent, Shin Nagai, and Lucas Mello Schnorr. "Measuring phenology uncertainty with large scale image processing." Ecological Informatics 59 (September 2020): 101109. http://dx.doi.org/10.1016/j.ecoinf.2020.101109.

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Yurk, Brian P., and James A. Powell. "Modeling the Effects of Developmental Variation on Insect Phenology." Bulletin of Mathematical Biology 72, no. 6 (January 28, 2010): 1334–60. http://dx.doi.org/10.1007/s11538-009-9494-7.

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Senay, Gabriel. "Modeling Landscape Evapotranspiration by Integrating Land Surface Phenology and a Water Balance Algorithm." Algorithms 1, no. 2 (October 30, 2008): 52–68. http://dx.doi.org/10.3390/a1020052.

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Yamamura, Norio, Noboru Fujita, Motoyuki Hayashi, Yusuke Nakamura, and Atsushi Yamauchi. "Optimal phenology of annual plants under grazing pressure." Journal of Theoretical Biology 246, no. 3 (June 2007): 530–37. http://dx.doi.org/10.1016/j.jtbi.2007.01.010.

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Scharf, Henry R., Mevin B. Hooten, Ryan R. Wilson, George M. Durner, and Todd C. Atwood. "Accounting for phenology in the analysis of animal movement." Biometrics 75, no. 3 (June 24, 2019): 810–20. http://dx.doi.org/10.1111/biom.13052.

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Wei, Wei, Wenbin Wu, Zhengguo Li, Peng Yang, and Qingbo Zhou. "Selecting the Optimal NDVI Time-Series Reconstruction Technique for Crop Phenology Detection." Intelligent Automation & Soft Computing 22, no. 2 (November 16, 2015): 237–47. http://dx.doi.org/10.1080/10798587.2015.1095482.

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9

Matechou, Eleni, Stephen N. Freeman, and Richard Comont. "Caste-Specific Demography and Phenology in Bumblebees: Modelling BeeWalk Data." Journal of Agricultural, Biological and Environmental Statistics 23, no. 4 (August 9, 2018): 427–45. http://dx.doi.org/10.1007/s13253-018-0332-y.

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10

Mariano, Greice C., Vanessa G. Staggemeier, Leonor Patricia Cerdeira Morellato, and Ricardo da S. Torres. "Multivariate cyclical data visualization using radial visual rhythms: A case study in phenology analysis." Ecological Informatics 46 (July 2018): 19–35. http://dx.doi.org/10.1016/j.ecoinf.2018.05.003.

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Dissertations / Theses on the topic "Phenology Mathematics"

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Yurk, Brian P. "Modeling the Evolution of Insect Phenology with Particular Reference to Mountain Pine Beetle." DigitalCommons@USU, 2009. https://digitalcommons.usu.edu/etd/385.

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Climate change is likely to disrupt the timing of developmental events (phenology) in insect populations in which development time is largely determined by temperature. Shifting phenology puts insects at risk of being exposed to seasonal weather extremes during sensitive life stages and losing synchrony with biotic resources. Additionally, warming may result in loss of developmental synchronization within a population, making it difficult to find mates or mount mass attacks against well-defended resources at low population densities. It is unknown whether genetic evolution of development time can occur rapidly enough to moderate these effects. The work presented here is largely motivated by the need to understand how mountain pine beetle (MPB) populations will respond to climate change. MPB is an important forest pest from both an economic and ecological perspective, because MPB outbreaks often result in massive timber loss. Recent MPB range expansion and increased outbreak frequency have been linked to warming temperatures. We present a novel approach to modeling the evolution of phenology by allowing the parameters of a phenology model to evolve in response to selection on emergence time and density. We also develop a temperature-dependent phenology model for MPB that accounts for multiple types of developmental variation: variation that persists throughout a life stage, random variation, and variation due to the MPB oviposition mechanism. This model is parameterized using MPB development time data from constant temperature laboratory experiments. We use Laplace's method to approximate steady distributions of the evolution model under stable temperatures. Here the mean phenotype allows for parents and offspring to be oviposited at exactly the same time of year in consecutive generations. These results are verified numerically for both MPB and a two-stage model insect. The evolution model is also applied to investigate the evolution of phenology for MPB and the two-stage model insect under warming temperatures. The model predicts that local populations can only adapt to climate change if development time can adapt so that individuals can complete exactly one generation per year and if the rate of temperature change is moderate.
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Ben-Younes, Mongi 1953. "Modeling the temperature-mediated phenological development of alfalfa (Medicago sativa L.)." Thesis, 1992. http://hdl.handle.net/1957/36534.

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Books on the topic "Phenology Mathematics"

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Ecosystem function in Savannas: Measurement and modeling at landscape to global scales. Boca Raton: CRC Press, 2011.

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Ben-Younes, Mongi. Modeling the temperature-mediated phenological development of alfalfa (Medicago sativa L.). 1992.

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3

Ben-Younes, Mongi. Modeling the temperature-mediated phenological development of alfalfa (Medicago sativa L.). 1992.

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Conference papers on the topic "Phenology Mathematics"

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sadat Tabatabaei, Neda, Masomeh Mazandaranee, Kamel Ariffin Mohd Atan, and Isthrinayagy S. Krishnarajah. "Investigation of ecological factors, phenology and ethnopharmacology of Proveskia abrotanoides l. in Golestan provinc." In INTERNATIONAL CONFERENCE ON MATHEMATICAL BIOLOGY 2007: ICMB07. AIP, 2008. http://dx.doi.org/10.1063/1.2883855.

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Journal articles
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