Persistent changes in NDVI between 1982 and 2003 over India using AVHRR GIMMS (Global Inventory Modeling and Mapping Studies) data

Jeyaseelan, A. T.; Roy, P. S.; Young, Stephen

doi:10.1080/01431160701253279

Cited by 51 publications

(28 citation statements)

References 44 publications

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“…Decreasing trends in NDVI have been reported for ecoregions with the fastest decreases, like the Dry Chaco region in South America [14], northeastern Australia [22], and boreal Canada and Alaska [23,24]. Likewise, increasing trends in NDVI have been reported for ecoregions with the fastest increases across Europe [25,26], northern China [27], central India [2], and the Sudano-Sahelian belt in Africa [28,29].…”

Section: Asiamentioning

confidence: 97%

“…For example, increasing trends in NDVI in India and northern China have been related to increasing rates of fertilization and irrigation [2,28]. Likewise, while previous studies have chiefly related the Sahelian greening trend to climate, e.g., [7], new investigations point to land conversion to croplands as one of the main causes for changes in NDVI [29].…”

Section: Asiamentioning

confidence: 99%

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Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

et al. 2014

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Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth's human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by OPEN ACCESS Remote Sens. 2014, 6 5718 anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects.

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Section: Asiamentioning

confidence: 97%

Section: Asiamentioning

confidence: 99%

Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

et al. 2014

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“…In India, Pakistan, Bangladesh, China, Ukraine, southwestern Russia, several European countries, the USA, and a number of other countries, substantial areas of agricultural land are irrigated (Wada et al, 2010). Evidence that increasing trends in NDVI are caused by irrigation and fertilization has been documented for India (Jeyaseelan et al, 2007) and the North China Plain (Piao et al, 2003), while Liu et al (2013a) showed that positive VOD trends in southern Russia, China, India and the US are the result of increased agricultural production. We used a land cover map of 2005 and for many regions agricultural extent increased during the study period.…”

Section: Arid Drylands (01 < P/et P ≤ 03)mentioning

confidence: 99%

Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data

et al. 2013

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Abstract. Drylands, covering nearly 30 % of the global land surface, are characterized by high climate variability and sensitivity to land management. Here, two satellite-observed vegetation products were used to study the long-term vegetation changes of global drylands: the widely used reflective-based Normalized Difference Vegetation Index (NDVI) and the recently developed passive-microwavebased Vegetation Optical Depth (VOD). The NDVI is sensitive to the chlorophyll concentrations in the canopy and the canopy cover fraction, while the VOD is sensitive to vegetation water content of both leafy and woody components. Therefore it can be expected that using both products helps to better characterize vegetation dynamics, particularly over regions with mixed herbaceous and woody vegetation. Linear regression analysis was performed between antecedent precipitation and observed NDVI and VOD independently to distinguish the contribution of climatic and non-climatic drivers in vegetation variations. Where possible, the contributions of fire, grazing, agriculture and CO 2 level to vegetation trends were assessed. The results suggest that NDVI is more sensitive to fluctuations in herbaceous vegetation, which primarily uses shallow soil water, whereas VOD is more sensitive to woody vegetation, which additionally can exploit deeper water stores. Globally, evidence is found for woody encroachment over drylands. In the arid drylands, woody encroachment appears to be at the expense of herbaceous vegetation and a global driver is interpreted. Trends in semi-arid drylands vary widely between regions, suggesting that local rather than global drivers caused most of the vegetation response. In savannas, besides precipitation, fire regime plays an important role in shaping trends. Our results demonstrate that NDVI and VOD provide complementary information and allow new insights into dryland vegetation dynamics.

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“…Time series approaches to continuous Earth observation-based variables are being adopted by many scholars to advance the understanding of inter-and intra-annual variations in vegetation and to examine and derive relationships between drivers of change and vegetation growth [2,3,[32][33][34][35][36][37][38][39][40]. Application of wavelet analysis [41], Fourier analysis [42,43] and other transformations [44,45] all show promise in elucidating patterns of land cover variation, but rarely are multiple, spatiotemporally variable drivers of landscape change assessed simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Combined Spatial and Temporal Effects of Environmental Controls on Long-Term Monthly NDVI in the Southern Africa Savanna

et al. 2013

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Deconstructing the drivers of large-scale vegetation change is critical to predicting and managing projected climate and land use changes that will affect regional vegetation cover in degraded or threated ecosystems. We investigate the shared dynamics of spatially variable vegetation across three large watersheds in the southern Africa savanna. Dynamic Factor Analysis (DFA), a multivariate time-series dimension reduction technique, was used to identify the most important physical drivers of regional vegetation change. We first evaluated the Advanced Very High Resolution Radiometer (AVHRR)-vs. the Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) datasets across their overlapping period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010). NDVI follows a general pattern of cyclic seasonal variation, with distinct spatio-temporal patterns across physio-geographic regions. Both NDVI products produced similar DFA models, although MODIS was simulated better. Soil moisture and precipitation controlled NDVI for mean annual precipitation (MAP) < 750 mm, and above this, evaporation and mean temperature dominated. A second DFA with the full AVHRR (1982-2010) data found that for OPEN ACCESSRemote Sens. 2013, 5 6514 MAP < 750 mm, soil moisture and actual evapotranspiration control NDVI dynamics, followed by mean and maximum temperatures. Above 950 mm, actual evapotranspiration and precipitation dominate. The quantification of the combined spatio-temporal environmental drivers of NDVI expands our ability to understand landscape level changes in vegetation evaluated through remote sensing and improves the basis for the management of vulnerable regions, like the southern Africa savannas.

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Persistent changes in NDVI between 1982 and 2003 over India using AVHRR GIMMS (Global Inventory Modeling and Mapping Studies) data

Cited by 51 publications

References 44 publications

Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

Human Land-Use Practices Lead to Global Long-Term Increases in Photosynthetic Capacity

Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data

Combined Spatial and Temporal Effects of Environmental Controls on Long-Term Monthly NDVI in the Southern Africa Savanna

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