Global vegetation dynamics: satellite observations over Asia

Malingreau, J. P.

doi:10.1080/01431168608948914

Cited by 201 publications

(75 citation statements)

References 12 publications

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“…The traditional smooth method [28][29][30] describes the general characteristics of the curve, rather than the cyclicity implied by the curve. Harmonic Analysis of Time Series (HANTS) is a new method used to analyze plant phenology.…”

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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“…There may be anomalous or missing data, which caused the seasonal trend of EVI curve to be insignificant. Therefore, in order to make EVI time series data reflect seasonal variation of vegetation correctly, it was necessary to conduct a filtering process [9,37,38]. Firstly, the invalid pixels in the quality control file attached to MODIS13Q1 with a value of 65,535 were removed.…”

Section: Data Sourcesmentioning

confidence: 99%

Temporal and Spatial Characteristics of EVI and Its Response to Climatic Factors in Recent 16 years Based on Grey Relational Analysis in Inner Mongolia Autonomous Region, China

Zhang

et al. 2018

Remote Sensing

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Abstract:The Inner Mongolia Autonomous Region (IMAR) is a major source of rivers, catchment areas, and ecological barriers in the northeast of China, related to the nation's ecological security and improvement of the ecological environment. Therefore, studying the response of vegetation to climate change has become an important part of current global change research. Since existing studies lack detailed descriptions of the response of vegetation to different climatic factors using the method of grey correlation analysis based on pixel, the temporal and spatial patterns and trends of enhanced vegetation index (EVI) are analyzed in the growing season in IMAR from 2000 to 2015 based on moderate resolution imaging spectroradiometer (MODIS) EVI data. Combined with the data of air temperature, relative humidity, and precipitation in the study area, the grey relational analysis (GRA) method is used to study the time lag of EVI to climate change, and the study area is finally zoned into different parts according to the driving climatic factors for EVI on the basis of lag analysis. The driving zones quantitatively show the characteristics of temporal and spatial differences in response to different climatic factors for EVI. The results show that: (1) The value of EVI generally features in spatial distribution, increasing from the west to the east and the south to the north. The rate of change is 0.22/10 • E from the west to the east, 0.28/10 • N from the south to the north; (2) During 2000-2015, the EVI in IMAR showed a slightly upward trend with a growth rate of 0.021/10a. Among them, the areas with slight and significant improvement accounted for 21.1% and 7.5% of the total area respectively, ones with slight and significant degradation being 24.6% and 4.3%; (3) The time lag analysis of climatic factors for EVI indicates that vegetation growth in the study area lags behind air temperature by 1-2 months, relative humidity by 1-2 months, and precipitation by one month respectively; (4) During the growing season, the EVI of precipitation driving zone (21.8%) in IMAR is much larger than that in the air temperature driving zone (8%) and the relative humidity driving zone (11.6%). The growth of vegetation in IMAR generally has the closest relationship with precipitation. The growth of vegetation does not depend on the change of a single climatic factor. Instead, it is the result of the combined action of multiple climatic factors and human activities.

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“…Most importantly, the ability of the NDVI to monitor intra-annual and interannual spatial variability of vegetation provides a basis for spatio-temporal phenological investigations (Schwartz and Reed, 1999;Nightingale and Phinn, 2003). For example, Malingreau (1986) described the relationship between the NDVI time series and the phenological characteristics of vegetation in some areas of Asia. Malo and Nicholson (1990) used NDVI to examine relationships between rainfall and vegetation dynamics in the Sahel.…”

Section: Introductionmentioning

confidence: 99%

Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats, India

Prasad

Badarinath

Eaturu

2008

Intl Journal of Climatology

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Bi-weekly National Oceanic and Atmospheric Administration-advanced very high-resolution radiometer (NOAA-AVHRR) satellite data covering a fourteen-year time period (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) were used to examine spatial patterns in the normalized difference vegetation index (NDVI) and their relationships with environmental variables covering tropical evergreen forests of the Western Ghats, India. NDVI values and corresponding environmental variables were extracted from 23 different forested sites using the NOAA-AVHRR global inventory monitoring and modelling studies (GIMMS) dataset. We specifically used the partial least square (PLS) multivariate regression technique that combines features from principal component analysis and multiple regression to link spatial patterns in NDVI with the environmental variables. PLS regression analysis suggested the two-component model to be the best model, explaining nearly 71% of the variance in the NDVI datasets with relatively good R 2 value of 0.78 and a predicted R 2 value of 0.74. The most important positive predictors for NDVI included Riva's continentality index, precipitation indicators summed over different quarters, average precipitation and elevation. Also, the results from PLS regression clearly suggested that bio-climatic indicators that relied only on precipitation parameters had much more positive influence than indicators that combined both temperature and precipitation together. These results highlight the climatic controls of vegetation vigor in evergreen forests and have implications for monitoring bio-spheric activity, developing prognostic phenology models and deriving land cover maps in the Western Ghats region of India.

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Global vegetation dynamics: satellite observations over Asia

Cited by 201 publications

References 12 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

Temporal and Spatial Characteristics of EVI and Its Response to Climatic Factors in Recent 16 years Based on Grey Relational Analysis in Inner Mongolia Autonomous Region, China

Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats, India

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