The dynamic of vegetation coverage and its response to climate factors in Inner Mongolia, China

Abstract

Normalized Difference Vegetation Index (NDVI) is widely recognized as a good indicator of vegetation productivity. Diagnosing the NDVI trend and understanding climatic factors influences on NDVI can predict the productivity changes under different climatic scenarios. This paper examined NDVI dynamic and its response to climate factors during a 10 year period (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008) in Inner Mongolia. The main findings are as follows: (1) The NDVI multi-scale characters can be revealed well by wavelet transform, and the average NDVI and the NDVI amplitude show a gradually decreased trend from northeast to southwest in Inner Mongolia during the past 10 years, furthermore, this trend is consistent with the heat and water distribution caused by latitude difference in north-south direction and Asia monsoon effect in east-west direction.(2) The relation between NDVI and temperature is the most close, followed by precipitation, sunshine hours and relative humidity. Different vegetation cover types show different strengths in correlation between NDVI and climate variables with the correlation values decreasing from forest, meadow steppe to desert steppe in whole. (3) The precipitation and temperature have the same change cycle, both nearly 290 days in the 20 selected stations. The NDVI has the same change cycle with the precipitation and temperature or either 10 days earlier or later than precipitation and temperature, which supports the significant correlation between NDVI and its climatic factors from a new perspective. The nearly 290 days change cycle implies that the vegetation growth cycle is nearly 10 months and there are no obvious differences change cycles in different vegetations. (4) Vegetation dynamic is significantly correlated to the temperature and precipitation at the time scale of 10, 20, 40, 80, 160, and 320-day, respectively, and the S3 scale (i.e., the time scale of 80-day), nearly 3 months (one season), is most significant and suitable for evaluating the vegetation dynamic to climatic factors.

Variability in MODIS NDVI in Relation to Southwest Monsoon over Western Ghats, India

“…They delineated that the NDVI and rainfall relation is significant and NDVI maintained the 3 ~ 4 years periodicity which is associated with rainfall. Also, it is reported that the wavelet analysis is a very useful technique in order to investigate the impact of climate factors on NDVI with most suitable time scale (Yang et al, 2012). In the present study also, we used NDVI and API which is a by-product of rainfall to understand the periodicities associated with them.…”

“…This wavelet analysis can also be applied to geophysical variables (Foufoula-Georgiou and Kumar, 1995) and the theoretical treatment of wavelet analysis is reported by Daubechies (1992). Studying NDVI variation with respect to the rainfall changes have been carried out by many people (Lark and Webster, 1999;He et al, 2012;Yang et al, 2012). He et al (2012) used 10 day composite AVHRR NDVI data from 1985 to 2007 and examined the seasonal and inter-annual relationships with rainfall by applying the wavelet spectral analysis over Canadian Ecosystems.…”

Vegetation Dynamics and Their Response to Climatic Variability in China

“…The relationship between NDVI and temperature or precipitation based on meteorological stations was mainly done in Excel 2013 and ArcGIS 10.2 software. The formula of correlation coefficient is as follows [23]:…”

“…Globally, the NDVI in much of the surface of the land indicated great changes from 1982 to 2012, and about half of the change displayed seasonal dynamics [23]. For example, the Annual NDVI had a slightly increasing trend in Central Asia from 1982 to 2012 [24].…”

Characterization of land surface phenology of land cover types on the Flooding Pampa (Argentina): an approach using NDVI time series

“…En particular, el NDVI (Normalized Difference Vegetation Index) es uno de los más utilizados ya que es un estimador lineal de la radiación fotosintéticamente activa interceptada por la vegetación y su mapeo permite describir los patrones regionales de productividad primaria neta y los diferentes atributos del comportamiento fenológico a partir de la curva anual de crecimiento (ALCARAZ-SEGURA et al, 2009;JÖNSSON, 2009;EKLUNDH, 2002EKLUNDH, , 2004GANDINI, 2013b;PARUELO et al, 1997;PARUELO et al, 2001;VÁZQUEZ et al, 2013). De esta manera, el NDVI se ha empleado en la caracterización de unidades funcionales de ecosistemas (ATZBERGER; EILERS, 2011;GANDINI et al, 2012;IVITS et al, 2013;GANDINI, 2013b;PARUELO et al, 2001), en el mapeo de la heterogeneidad de paisajes (ALI et al, 2013;BARRAZA et al, 2013) y su relación con variables climáticas (MELENDEZ-PASTOR et al, 2010;VAN LEEUWEN et al, 2013;YANG et al, 2012).…”