Long-Term Detection of Global Vegetation Phenology from Satellite Instruments

Zhang, Xiaoyang; Friedl, M. A.; Tan, Bin; Goldberg, Mitchell D.; Yu, Yunyue

doi:10.5772/39197

Cited by 24 publications

(17 citation statements)

References 76 publications

(106 reference statements)

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“…Vegetation phenology is a useful indicator in the study of the response of ecosystems to climate variability (Zhang et al, 2012;Richardson et al, 2013), and an important parameter for land surface, climate and biogeochemical models that quantify the exchange of water, energy and gases between vegetation and the atmosphere (Pitman, 2003;Eklundh and Jönsson, 2010). A variety of applications that require the characterization of vegetation phenology include crop yield quantification, wildfire fuel accumulation, vegetation condition, ecosystem response to climate variability and climate change, and ecosystem resilience (Schwartz, 2003;Liang and Schwartz, 2009;Peñue-las et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Phenology on the landscape-to-continental scale is typically derived using time series of remotely sensed vegetation greenness indices such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) (de Beurs and Henebry, 2008). Several studies have used NDVI time series recorded by the Advanced Very High Resolution Radiometer (AVHRR) to investigate long-term phenological trends induced by climate change (Moulin et al, 1997;Zhang et al, 2012). More recent studies used EVI time series recorded by the MODerate-resolution Imaging Spectroradiometer (MODIS) that has better geometric correction and increased resolution compared to AVHRR .…”

Section: Introductionmentioning

confidence: 99%

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Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

et al. 2014

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Abstract. Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative spatial information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually recurring patterns. However, precipitation-driven nonannual phenology of arid and semi-arid systems (i.e., drylands) received much less attention, despite the fact that they cover more than 30 % of the global land surface. Here, we focused on Australia, a continent with one of the most variable rainfall climates in the world and vast areas of dryland systems, where a detailed phenological investigation and a characterization of the relationship between phenology and climate variability are missing.To fill this knowledge gap, we developed an algorithm to characterize phenological cycles, and analyzed geographic and climate-driven variability in phenology from 2000 to 2013, which included extreme drought and wet years. We linked derived phenological metrics to rainfall and the Southern Oscillation Index (SOI). We conducted a continentwide investigation and a more detailed investigation over the Murray-Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia.Results showed high inter-and intra-annual variability in phenological cycles across Australia. The peak of phenological cycles occurred not only during the austral summer, but also at any time of the year, and their timing varied by more than a month in the interior of the continent. The magnitude of the phenological cycle peak and the integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over northeastern Australia and within the MDB, predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of vegetation productivity) showed positive anomalies of more than 2 standard deviations over most of eastern Australia in 2009-2010, which coincided with the transition from the El Niño-induced decadal droughts to flooding caused by La Niña.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

et al. 2014

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“…Los patrones de la Figura 1b permiten def inir los umbrales y su duración en los diferentes estadios del desarrollo del follaje, en función de la evolución temporal del IV (Zhang et al, 2012). El patrón de la Figura 1b es propio del crecimiento y senescencia del follaje de los diferentes tipos de vegetación.…”

Section: Follaje De La Vegetaciónunclassified

“…Una alternativa para contrarrestar estas limitaciones es tomar fotografías digitales con cámaras que se instalan en estructuras por encima de la vegetación (Richardson et al, 2009;Parihar et al, 2013) y caracterizar la fenología a partir de las imágenes, pero la técnica aún conserva el problema de la limitación geográf ica. Otra opción es la tecnología de los sensores remotos que, dado su enfoque espacial exhaustivo y la temporalidad de las visitas a un mismo pixel, plantea una solución ef iciente y de bajo costo (Zhang et al, 2012).…”

Section: Introductionunclassified

“…En relación al uso de sensores remotos para la modelación de la fenología se utilizan diferentes índices de vegetación o IV (Zhang et al, 2012), de los que sobresalen el NDVI Rouse et al (1974), el EVI (Liu y Huete, 1995) y el EVI2 (Jiang et al, 2008), en los que los datos se f iltran para reducir su variabilidad (Kathuroju et al, 2007;Geerken, 2009;Atkinson et al, 2012) y poder detectar umbrales que def inan los estadios fenológicos (Zhang et al, 2003(Zhang et al, y 2012. Con relación a la fenología del follaje, los IV permiten def inir los patrones fenológicos de ecosistemas o biomas a escala regional o global (Zhang et al, 2004;Stöckli y Vidale, 2004;Zhang et al, 2006).…”

Section: Introductionunclassified

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Índice espectral de vegetación para la caracterización simultánea de la floración y crecimiento vegetal

Domínguez¹,

Pellat

2018

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Es importante caracterizar los patrones fenológicos de la floración y desarrollo del follaje en la vegetación con algún grado de caducidad, debido a su relación con el ciclo del carbono y del agua. Las técnicas de campo y el uso de fotografías digitales en torres de observación tienen alcances geográf icos limitados, por lo que la tecnología de los sensores remotos es una opción costo-efectiva. En este trabajo se revisó un modelo fenológico floración-follaje de desarrollo previo y se presenta un nuevo modelo que incluye el uso de un índice de vegetación de floración y follaje (IVFF) para caracterizar y diferenciar los patrones fenológicos. Para correlacionar el IVFF con la cobertura de la floración (CF) en la vegetación, se desarrolló un experimento de validación que incluyó un arbusto de bajo porte, en el cual se colocaron diferentes coberturas (<15%) de flores blancas. Los resultados mostraron que el IVFF se correlaciona linealmente con la cobertura de flores, y que la relación depende de la cobertura del follaje donde se colocan. El desarrollo propuesto permitió establecer correlaciones claras entre el IVFF y la cobertura floral de la vegetación con diferentes coberturas aéreas, por lo que fue signif icativamente ef icaz, aún con porcentajes bajos de floración, imposibles de detectar con otras técnicas. En las estimaciones sólo se utilizan las bandas del rojo y el infrarrojo cercano, por lo que convierte al método propuesto en una herramienta asequible para la mayoría de las aplicaciones de sensores remotos.

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Leaf phenology of thirteen African origins of baobab (Adansonia digitata (L.)) as influenced by daylength and water availability

Lucchio

Fensholt

Markussen

et al. 2018

Ecology and Evolution

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Water availability is often described as one of the main drivers of phenology of tropical dry forests but experimental studies to identify the cues triggering phenological changes are few. In a greenhouse trial, we exposed seedlings of thirteen origins, seven from West and six from East Africa, respectively, of Adansonia digitata L.(baobab) to a well‐watered control treatment and a water withholding treatment in combination with exposure to three different daylengths (11.5, 12.0, and 12.5 hr). Responses were measured in terms of meristematic activity, number of leaves, and height growth followed over 6.5 months. Multi‐level mixed‐effects statistical models were used to analyze how environmental and inter‐population variables affected phenological behavior. Flushing was influenced by the daylength with the lowest degree of meristematic activity observed in the short daylength treatment. Daylength also influenced the number of leaves through an interaction with the water regime. The water regime influenced all variables through interactions with the origins. Seedlings subjected to water stress had higher meristematic activity, but initially lower numbers of leaves than continuously watered plants. Height growth in continuously watered plants was fastest or at par with water‐stressed plants, depending on the origin. Origins from West Africa tended to have higher meristematic activity and their phenology was found to be less influenced by water withholding than East African origins. There were no signs of significant differences between origins in their response to photoperiod. In conclusion, baobab seedlings show opportunistic behavior, setting leaves when water is available, but larger activity is found when days are long. We discuss the results in terms of triggering factors for baobab phenology and adaptation to specific environmental conditions at the site of origins.

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Long-Term Detection of Global Vegetation Phenology from Satellite Instruments

Cited by 24 publications

References 76 publications

Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

Índice espectral de vegetación para la caracterización simultánea de la floración y crecimiento vegetal

Leaf phenology of thirteen African origins of baobab (Adansonia digitata (L.)) as influenced by daylength and water availability

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