Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Abstract

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p<0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p<0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management.

Contraction of the Gobi Desert, 2000–2012

“…The link between NDVI-identified vegetation cover and precipitation is well-established [44]; here NDVI images were selected for August (29 August, MODQ13) from 2000 to 2012 to capture the maximum potential level of plant development and peak biomass. The composite NDVI data were then compared to CGIAR's Aridity Index map that is based on the 50-year annual average from 1950 to 2000 [35].…”

Impacts of temperature extremes on European vegetation during the growing season

“…This implies, that in many parts of Europe, a positive or negative relationship between temperature and NDVI is only valid for one tail of the empirical distribution, which presents a finding that could not be deduced based upon classical linear correlation analyses (e.g., Schultz and Halpert, 1995;Los et al, 2001;Ichii et al, 2002;Wang et al, 2003;Stöckli and Vidale, 2004;Hao et al, 2012).…”

“…Up to now, research on climatic drivers of NDVI variations has often concentrated on a global scale (e.g., the aforementioned Halpert, 1993, 1995;Kawabata et al, 2001;Karnieli et al, 2010;Kim et al, 2010), whereas regional studies like Wang et al (2003) and Hao et al (2012) are still rare. In the face of global warming, however, especially inter-regional climate disparities are projected to increase (Ciscar et al, 2011;Iglesias et al, 2012), thus emphasizing the need for regional research.…”

“…Moreover, most previous studies on the environmental impacts of changing temperatures have focused on the detection of linear relationships between different variables by means of correlation and regression analysis (see, e.g., Schultz and Halpert, 1995;Los et al, 2001;Ichii et al, 2002;Wang et al, 2003;Stöckli and Vidale, 2004;Hao et al, 2012). Linear correlation analysis is a widely applicable tool allowing us to discover linear statistical relationships between environmental variables.…”

Optical Cloud Pixel Recovery via Machine Learning

“…Among the considered climatic factors, precipitation and temperature strongly influenced both temporal and spatial patterns of NDVI. Hao et al [40] explored the linkage of NDVI to temperature and precipitation in northern china. The NDVI response for grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) was analyzed showing that the yearly precipitation and highest temperature were correlated with NDVI.…”