A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery

Lloyd, D.

doi:10.1080/01431169008955174

Cited by 341 publications

(188 citation statements)

References 26 publications

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“…Currently, many scholars put forward different remote sensing phenology extraction methods. They are: 1) threshold method [33,34]; 2) maximum ratio method [18,35]; 3) integration of methods 1) and 2) (requires determining first the phenology threshold according to the maximum ratio and determining the annual occurrence date of such phenology based on the threshold [36,37]); and 4) logistic function or harmonic decomposition function, used to determine the turning point of NDVI time series to estimate phenological characters [38,39]. However, no method is universally accepted at present [40].…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal variation in alpine grassland phenology in the Qinghai-Tibetan Plateau from 1999 to 2009

et al. 2012

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Plant phenology is the most salient and sensitive indicator of terrestrial ecosystem response to climate change. Studying its change is significantly important in understanding and predicting impressively changes in terrestrial ecosystem. Based on NDVI from SPOT VGT, this paper analyzed the spatiotemporal changes in alpine grassland phenology in Qinghai-Tibetan Plateau from 1999 to 2009. The results are enumerated as follows: (1) The spatial distribution of the average alpine grassland phenology from 1999 to 2009 is closely related to water and heat conditions. Accompanying the deterioration in heat and water conditions from southeast to northwest, the start of growth season (SOG) was delayed gradually, the end of growth season (EOG) advanced slowly, and the length of growth season (LOG) shortened gradually. Elevation played an important role in the regional differentiation of phenology, but a dividing line of approximately 3500 m existed. Below this line, the phenology fluctuated irregularly with altitude change, whereas above the line, the phenology is closely related to altitude change. (2) From 1999 to 2009, SOG of the alpine grassland came earlier by six days per decade (R 2 =0.281, P=0.093), EOG was late by two days per decade (R 2 =0.031, P=0.605), and LOG lengthened by eight days per decade (R 2 =0.479, P=0.018). The early SOG, the late EOG, and the extended LOG mainly occurred at the center and east of the Plateau. SOG in most of the Plateau advanced significantly, especially in the eastern Plateau. (3) The inter-annual phenology changes of the alpine grassland in the Qinghai-Tibetan Plateau exhibited significant differentiation at different elevation and natural zones. The inter-annual changes at high altitude were more complicated than that at low altitude. The most significant phenology changes were found in the eastern Qinghai-Qilian montane steppe zone, and non-significant changes occurred in the Southern Tibet montane shrub-steppe zone. . Plant phenology not only provides a substantial theoretical and practical significance in farming forecast, agricultural and animal-husbandry production guidance, pest indication, seed introduction and selection, and many other aspects but also serves as an important parameter in land process model and global vegetation model [3][4][5][6]. Qinghai-TibetanThis information is very important in promoting people's understanding of the response of vegetation to climate change and improving the simulation accuracy of mass and energy exchanges between the atmosphere and vegetation [7]. As the best indicator in monitoring the influence of climate on vegetation, plant phenology has become the key point of global-change research [8]. Plant phenology analysis based on remote sensing data shows that the growth season of plants in northern hemisphere has lengthened gradually during the last decades [9][10][11][12]. These conclusions are supported by ground observation data. The spring phenophase of most plants in Europe and North America have

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal variation in alpine grassland phenology in the Qinghai-Tibetan Plateau from 1999 to 2009

et al. 2012

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“…Some authors use single arbitrary thresholds, e.g. 0.17 (Fischer, 1994), 0.09 (Markon et al, 1995), and 0.099 (Lloyd, 1990), whereas some use threshold specifiers like the long-term average (Karlsen et al, 2006) or % peak amplitude of vegetation indices (Jonsson and Eklundh, 2002).…”

Section: Vegetation Phenologymentioning

confidence: 99%

Relating seasonal dynamics of enhanced vegetation index to the recycling of water in two endorheic river basins in north-west China

Matin

Bourque

2015

Hydrol. Earth Syst. Sci.

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Abstract. This study associates the dynamics of enhanced vegetation index in lowland desert oases to the recycling of water in two endorheic (hydrologically closed) river basins in Gansu Province, north-west China, along a gradient of elevation zones and land cover types. Each river basin was subdivided into four elevation zones representative of (i) oasis plains and foothills, and (ii) low-, (iii) mid-, and (iv) highmountain elevations. Comparison of monthly vegetation phenology with precipitation and snowmelt dynamics within the same basins over a 10-year period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) suggested that the onset of the precipitation season (cumulative % precipitation > 7-8 %) in the mountains, typically in late April to early May, was triggered by the greening of vegetation and increased production of water vapour at the base of the mountains. Seasonal evolution of in-mountain precipitation correlated fairly well with the temporal variation in oasisvegetation coverage and phenology characterised by monthly enhanced vegetation index, yielding coefficients of determination of 0.65 and 0.85 for the two basins. Convergent cross-mapping of related time series indicated bi-directional causality (feedback) between the two variables. Comparisons between same-zone monthly precipitation amounts and enhanced vegetation index provided weaker correlations. Start of the growing season in the oases was shown to coincide with favourable spring warming and discharge of meltwater from low-to mid-elevations of the Qilian Mountains (zones 1 and 2) in mid-to-late March. In terms of plant requirement for water, mid-seasonal development of oasis vegetation was seen to be controlled to a greater extent by the production of rain in the mountains. Comparison of water volumes associated with in-basin production of rainfall and snowmelt with that associated with evaporation seemed to suggest that about 90 % of the available liquid water (i.e. mostly in the form of direct rainfall and snowmelt in the mountains) was recycled locally.

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“…Other authors have used the onset and end of greenness and time-integrated (TI) NDVI as surrogates for growing season and primary production, and established statistical relationships between NDVI or TI NDVI and climate variables for gauging and monitoring vegetation dynamics (Yang W. et al, 1997;Yang L. et al, 1998;Fu and Wen, 1999). However, because NDVI metrics and thresholds may not directly correspond to conventional, ground-based phenological events, but rather provide indicators of vegetation dynamics (Justice et al, 1986;Lloyd, 1990), a detailed comparison of these satellite measures with ground-based phenological events is needed (Reed et al, 1994;Schwartz and Reed, 1999).…”

Section: Introductionmentioning

confidence: 99%

Relationships among phenological growing season, time‐integrated normalized difference vegetation index and climate forcing in the temperate region of eastern China

Chen

Pan

2002

Intl Journal of Climatology

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Phenological, meteorological, and time-integrated normalized difference vegetation index (TI NDVI) data from 1982 to 1993, at three sample stations, were used to investigate the response of the growing season of local plant communities to climate change and the linkage of satellite sensor-derived greenness to the surface growing season. Results suggest that mean air temperature and growing degree days (GDDs) above 5°C during late winter and spring, and precipitation in autumn, are the most important controls on the beginning and end dates of the growing season (BGS and EGS). In contrast, annual mean air temperature, annual GDD totals, mean air temperature during late winter and spring, and growing season TI NDVI are the most important controls on length of the growing season (LGS).Using correlation and regression analysis, simple and multiple linear regression models were developed for individual and all stations. Since the standard error of the estimates (SE) of the BGS models for all stations are smaller than those of the EGS models, estimates of the beginning date of the growing season are probably more reliable than estimates of the end date. On average, if the mean air temperature in late winter and spring increases by 1°C, then the beginning date of the growing season will advance 5-6 days, and the end date will be 5 days later. Moreover, if autumn precipitation increases 100 mm, then the end date will advance 6-8 days. In terms of the LGS models, mean air temperature in late winter and spring, annual mean air temperature, and annual GDD totals have significant positive correlations with growing season duration, whereas growing season TI NDVI has a negative relationship. Comparing the SE of different LGS models, those developed with each of the three temperature variables fit the observed growing season duration data much better than those using growing season TI NDVI.

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A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery

Cited by 341 publications

References 26 publications

Spatiotemporal variation in alpine grassland phenology in the Qinghai-Tibetan Plateau from 1999 to 2009

Spatiotemporal variation in alpine grassland phenology in the Qinghai-Tibetan Plateau from 1999 to 2009

Relating seasonal dynamics of enhanced vegetation index to the recycling of water in two endorheic river basins in north-west China

Relationships among phenological growing season, time‐integrated normalized difference vegetation index and climate forcing in the temperate region of eastern China

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