Modelling the vegetation–climate relationship in a boreal mixedwood forest of Alberta using normalized difference and enhanced vegetation indices

Jahan, Nasreen; Gan, Thian Yew

doi:10.1080/01431160903464146

Cited by 28 publications

(16 citation statements)

References 73 publications

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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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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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“…The global vegetation index products (NDVI and EVI) at 1‐km spatial resolution for the period 2001–2010 were obtained from the National Aeronautics and Space Administration's MODIS and further integrated into 12.5‐km (0.125°) spatial resolution grid cells. The indices are computed from MODIS daily surface reflectance that are radiometrically calibrated, cloud‐filtered, atmospherically corrected (corrected for molecular scattering, ozone absorption, and aerosols), spatially and temporally gridded, and adjusted for view angle influences (Huete et al, ; Jahan & Gan, ).…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

“…The EVI is an optimized index combining blue, red, and near‐infrared bands from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to minimize atmospheric and canopy background effects on NDVI and allows for higher accuracy of vegetation monitoring and better reflection of vegetation conditions (Huete et al, ). The NDVI and EVI are the most commonly used vegetation indices and have been widely applied for land cover classification (Zhang, Sun, Zhang, & Tong, ), plant diversity forecast (John et al, ), vegetation dynamic monitoring (Zhu & Li, ), determination of land use patterns (Vanacker, Linderman, Lupo, Flasse, & Lambin, ), exploration of relationships between vegetation and hydroclimatic factors (Deng, Su, & Liu, ; Jahan & Gan, ; Méndez‐Barroso, Vivoni, Watts, & Rodríguez, ), and many other purposes. However, these two satellite‐based indices have rarely been compared, thus far, in studies on the response of vegetation to climate change, and their performances in predicting vegetation dynamics under changing environment have seldom been examined.…”

Section: Introductionmentioning

confidence: 99%

Prediction of vegetation anomalies over an inland river basin in north‐western China

Niu

Sivakumar

2018

Hydrological Processes

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Vegetation cover plays an important role in linking the atmosphere, water, and land and is deemed as a key indicator in the terrestrial ecological system. Therefore, it is of great importance to monitor vegetation dynamics and understand the mechanisms of vegetation change, including that driven by climate change. This study examines (a) the evolution of vegetation dynamics over the Heihe River Basin in the typical arid zone in north‐western China using nonparametric Mann–Kendall test and Thiel Sen's slope; (b) the relationships between remotely sensed vegetation indices (normalized difference vegetation index [NDVI] and enhanced vegetation index [EVI]) and hydroclimatic variables based on correlation analysis; and (c) the prediction of vegetation anomalies using a multiple linear regression model. For the analysis, the Moderate Resolution Imaging Spectroradiometer NDVI/EVI product and the gridded daily meteorological data at a spatial resolution of 0.125° over the period 2001–2010 are considered. The results indicate that vegetation cover improved over a large proportion during 2001–2010, with a significant trend towards warm and wet, characterized by an increase in average annual temperature and precipitation by 0.042 °C/year and 5.8 mm/year, respectively. We test the feasibility of NDVI and EVI in quantifying the responses of vegetation anomaly to climate change and develop a statistical model to predict vegetation dynamics in the basin. The NDVI‐based model is found to be more reliable than the EVI‐based model, partly due to the vegetation characteristics and geomorphologic properties of the study region. The proposed model performs well when there is no lag time between meteorological factors and vegetation indices for grassland and cropland, whereas 1‐month lead time prediction is found to be best for forest. The soil water content is introduced as an extra explanatory variable, which effectively improves the prediction accuracy for different land use types. In general, the predictive ability of the proposed model is stable and satisfactory, and the model can provide useful early warning information for regional water resources management under changing climate.

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“…Niemeyer and Vogt, 2001). Time series of satellite images have an important role in the monitoring of regional and global ecosystem properties such as carbon storage and primary productivity (Potter et al, 2008;Potapov et al, 2009;Ise et al, 2010;Jahan and Gan, 2011); soil moisture (Temimi et al, 2010); and the influence of human disturbance regimes (Jin and Sader, 2005;Westermann et al, 2011), such as fire (Lozano et al, 2008;Huesca et al, 2009;Dubinin et al, 2010), or land use change (Hu et al, 2009;Ge, 2010;Sulla-Menashe et al, 2011). Satellite sensors are well-suited to monitoring tasks because they provide repeatable measurements at a spatial scale which is appropriate for capturing the effects of changes in key properties of ecosystem functioning.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal MODIS EVI gap filling under cloud cover: An example in Scotland

Poggio

Gimona

Brown

2012

ISPRS Journal of Photogrammetry and Remote Sensing

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Modelling the vegetation–climate relationship in a boreal mixedwood forest of Alberta using normalized difference and enhanced vegetation indices

Cited by 28 publications

References 73 publications

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

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

Prediction of vegetation anomalies over an inland river basin in north‐western China

Spatio-temporal MODIS EVI gap filling under cloud cover: An example in Scotland

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