Detecting Climate Effects on Vegetation in Northern Mixed Prairie Using NOAA AVHRR 1-km Time-Series NDVI Data

Li, Zhaoqin; Guo, Xulin

doi:10.3390/rs4010120

Cited by 43 publications

(28 citation statements)

References 52 publications

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“…In the growing season (Figure 9a), vegetation growth was strongly dominated by temperature in most of the forest-covered (including EBF, ENF and DBF) southern China (26.5% of the research area) and by precipitation in grass-covered arid or semi-arid northern China (17.4% of research area). The study by Li and Guo [26] also found that precipitation has exerted more effects on NDVI than temperature in semi-arid grassland in Canada. A large portion (51.3%) of the research area was temperature-dominated in spring (Figure 9b), especially in northeastern and central China where the majority of vegetation is deciduous broadleaf forest (Figure 1).…”

Section: Relationships Between Ndvi and Multiple Climate Dirversmentioning

confidence: 98%

“…Seasonally, according to the detected abrupt change points (Figures S1f, S2f and S3f), it was mainly in summer and autumn when precipitation dropped. Though it is difficult to separate the effects of precipitation and grazing [26], the correlation and corresponding abrupt change points in NDVI and precipitation partly provide explanations for the decreasing NDVI in this area. Less precipitation limited the growth of grass or even partly destroyed the grassland ecosystem with low ecological resilience, and meanwhile rising temperature could have aggravated water limitation.…”

Section: Temporal Variation In Ndvi In Typical Regionsmentioning

confidence: 99%

“…Long-term remote sensing observations provide effective methods to investigate interannual vegetation dynamics. The Normalized Difference Vegetation Index (NDVI), which is correlated to Leaf Area Index (LAI) [21] and Gross Primary Production (GPP) [22], is a good indicator for vegetation coverage and biomass, and has been widely used to analyze vegetation dynamics [23][24][25][26][27]. Many researches have investigated NDVI trends in different regions, showing that despite great spatial heterogeneity, NDVI generally increased in national and regional scale in China [19,28], North America [2], east Asia [29] and Euraisa [30].…”

Section: Introductionmentioning

confidence: 99%

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Responses of Natural Vegetation Dynamics to Climate Drivers in China from 1982 to 2011

Liu

Lei

2015

Remote Sensing

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This study investigated the spatiotemporal variation of vegetation growth and the influence of climatic drivers from 1982 to 2011 across China using datasets from the Normalized Difference Vegetation Index (NDVI) and climatic drivers. Long term trends, significance and abrupt change points of interannual NDVI time series were analyzed. We applied both simple regression and multi-regression models to quantify the effects of climatic drivers on vegetation growth and compare their relative contributions. Results show that on average, the growing season NDVI significantly increased by 0.0007 year −1 , with 76.5% of the research area showed increasing NDVI from 1982 to 2011. Seasonally, NDVI increased at high rates during the spring and autumn while changed slightly during the summer. At a national scale, the growing season NDVI was significantly and positively correlated to temperature and precipitation, with temperature being the dominant factor. At regional scales, the growing season NDVI was dominated by increasing temperature in most forest-covered areas in southern China and dominated by precipitation in most grassland in northern China. Within the past three decades, the increasing trend of national mean NDVI abruptly changed in 1994, slowing down from 0.0008 year −1 to 0.0003 year −1 . To be regional specific, the growing season NDVI in forest covered southern China has accelerated together with temperature since mid 1990s, while parts of the grassland in northern China have undergone stalled NDVI trends corresponding to slowed temperature increment and dropped precipitation since around 2000. Typical region analysis suggested that apart from

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Section: Relationships Between Ndvi and Multiple Climate Dirversmentioning

confidence: 98%

Section: Temporal Variation In Ndvi In Typical Regionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Responses of Natural Vegetation Dynamics to Climate Drivers in China from 1982 to 2011

Liu

Lei

2015

Remote Sensing

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“…Since this NDVI data set is used as input to or for benchmarking global carbon, water and energy cycle models, it is important that we understand how much variability in vegetation growth is captured by this NDVI data set and if the causes of variability are linked to climate (precipitation, temperature, and solar radiation), disturbance (fires and large area outbreaks of pests), human management (e.g., irrigation and fertilization), or residual errors. Other studies have been published that reveal correlations between climate and NDVI [26][27][28][29][30], but none have combined analyses on monthly anomalies, lead time dynamics, cumulative climate effects and non-climate signal interference at global scales [31,32], and none have used global NDVI time series longer than 20 years.…”

Section: Introductionmentioning

confidence: 99%

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

et al. 2013

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Satellite observations of surface reflected solar radiation contain information about variability in the absorption of solar radiation by vegetation. Understanding the causes of variability is important for models that use these data to drive land surface fluxes or for benchmarking prognostic vegetation models. Here we evaluated the interannual variability in the new 30.5-year long global satellite-derived surface reflectance index data, Global Inventory Modeling and Mapping Studies normalized difference vegetation index (GIMMS NDVI3g). Pearson's correlation and multiple linear stepwise regression analyses were applied to quantify the NDVI interannual variability driven by climate anomalies, and to evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVI signal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systems where in some regions and seasons > 40% of the NDVI variance could be explained by precipitation anomalies. Temperature correlations were strongest in northern mid-to high-latitudes in the spring and early summer where up to 70% of the NDVI variance was OPEN ACCESSRemote Sens. 2013, 5 3919 explained by temperature anomalies. We find that, in western and central North America, winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wet season precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

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“…In the case of pasture, there is yet another important variable that must be taken into account: grazing. The distinction of the impact of each of these variables on the pasture can only be performed in controlled environments, where the history of grazing and precipitation is known [51,52].…”

Section: Regional Analysismentioning

confidence: 99%

MODIS Time Series to Detect Anthropogenic Interventions and Degradation Processes in Tropical Pasture

Aguiar

Mello²,

Nogueira

et al. 2017

Remote Sensing

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Abstract:The unavoidable diet change in emerging countries, projected for the coming years, will significantly increase the global consumption of animal protein. It is expected that Brazilian livestock production, responsible for close to 15% of global production, be prepared to answer to the increasing demand of beef. Consequently, the evaluation of pasture quality at regional scale is important to inform public policies towards a rational land use strategy directed to improve livestock productivity in the country. Our hypothesis is that MODIS images can be used to evaluate the processes of degradation, restoration and renovation of tropical pastures. To test this hypothesis, two field campaigns were performed covering a route of approximately 40,000 km through nine Brazilian states. To characterize the sampled pastures, biophysical parameters were measured and observations about the pastures, the adopted management and the landscape were collected. Each sampled pasture was evaluated using a time series of MODIS EVI2 images from 2000-2012, according to a new protocol based on seven phenological metrics, 14 Boolean criteria and two numerical criteria. The theoretical basis of this protocol was derived from interviews with producers and livestock experts during a third field campaign. The analysis of the MODIS EVI2 time series provided valuable historical information on the type of intervention and on the biological degradation process of the sampled pastures. Of the 782 pastures sampled, 26.6% experienced some type of intervention, 19.1% were under biological degradation, and 54.3% presented neither intervention nor trend of biomass decrease during the period analyzed.

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Detecting Climate Effects on Vegetation in Northern Mixed Prairie Using NOAA AVHRR 1-km Time-Series NDVI Data

Cited by 43 publications

References 52 publications

Responses of Natural Vegetation Dynamics to Climate Drivers in China from 1982 to 2011

Responses of Natural Vegetation Dynamics to Climate Drivers in China from 1982 to 2011

Evaluating and Quantifying the Climate-Driven Interannual Variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at Global Scales

MODIS Time Series to Detect Anthropogenic Interventions and Degradation Processes in Tropical Pasture

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