Inter-annual Variations In Vegetation Research Articles

Vegetation inter-annual variation responses to climate variation in different geomorphic zones of the Yangtze River Basin, China

Despite numerous studies on the response of vegetation change to climate variation, they have mainly been based on annual mean temperature and annual mean precipitation, and have failed to reveal the impact of climate on the inter-annual variation of vegetation in different geomorphic zones based on non-linear methods and considering daily low and high temperature. In this study, the effect of climate variation on the inter-annual variation of vegetation in different geomorphic zones along the area of t the Yangtze River Basin, China from 1982 to 2015 is revealed using Ensemble Empirical Mode Decomposition method. The results show that the inter-annual variation of vegetation is frequently fluctuating, mainly on a short time scale (3-year time scale), and its contribution gradually increases from 62.35% to 73.57% with increasing relief (from plain to high undulating mountain). The vegetation change of more than 75% of the areas is dominated by inter-annual variations, moreover, the vegetation change of the low relief areas is dominated by the 3-year timescale along with the long-term trend, while that of the other geomorphic zones is dominated by the 3-year timescale only. Inter-annual vegetation variation is positively related to precipitation and maximum temperature, but negatively related to mean temperature and minimum temperature in most areas, with area percentages of 67.20%, 86.55%, 65.38% and 75.02%. Inter-annual variation is mainly controlled by maximum temperature in most areas (54.77%). These results will deepen our understanding of the vegetation-climate relationship, suggesting that the responses of inter-annual variation in vegetation to climate vary not only on different time scales, but also in indifferent geomorphic zones.

Observations of Increased Cloud Cover over Irrigated Agriculture in an Arid Environment

Abstract Irrigated agriculture accounts for 20% of global cropland area and may alter climate locally and globally, but feedbacks on clouds and rainfall remain highly uncertain, particularly in arid regions. Nonrenewable groundwater in arid regions accounts for 20% of global irrigation water demand, and quantifying these feedbacks is crucial for the prediction of long-term water use in a changing climate. Here, satellite data are used to show how irrigated crops in an arid environment alter land surface properties, cloud cover, and rainfall patterns. Land surface temperatures (LSTs) over the cropland are 5–7 K lower than their surroundings, despite a lower albedo, suggesting that Bowen ratio is strongly reduced (and latent heat fluxes increased) over the irrigated cropland. Daytime cloud cover is increased by up to 15% points (a relative increase of 60%), with increased cloud development in the morning and a greater afternoon peak in cloud. Cloud cover is significantly correlated with interannual variations in vegetation and LST. Afternoon rainfall also appears to be enhanced around the irrigation. The cloud feedback is the opposite of what has been previously observed in tropical and semiarid regions, suggesting different processes drive land–atmosphere feedbacks in very dry environments. Increased cloud and rainfall, and associated increases in diffuse radiation and reductions in temperature, are likely to benefit vegetation growth. Predictions of changes in crop productivity due to climate change and the impacts of global land-use change on climate and the use of water resources would therefore benefit from including these effects.

Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010

To understand the variations in vegetation and their correlation with climate factors in the upper catchments of the Yellow River, China, Normalized Difference Vegetation Index (NDVI) time series data from 2000 to 2010 were collected based on the MOD13Q1 product. The coefficient of variation, Theil-Sen median trend analysis and the Mann-Kendall test were combined to investigate the volatility characteristic and trend characteristic of the vegetation. Climate data sets were then used to analyze the correlation between variations in vegetation and climate change. In terms of the temporal variations, the vegetation in this study area improved slightly from 2000 to 2010, although the volatility characteristic was larger in 2000–2005 than in 2006–2010. In terms of the spatial variation, vegetation which is relatively stable and has a significantly increasing trend accounts for the largest part of the study area. Its spatial distribution is highly correlated with altitude, which ranges from about 2000 to 3000 m in this area. Highly fluctuating vegetation and vegetation which showed a significantly decreasing trend were mostly distributed around the reservoirs and in the reaches of the river with hydropower developments. Vegetation with a relatively stable and significantly decreasing trend and vegetation with a highly fluctuating and significantly increasing trend are widely dispersed. With respect to the response of vegetation to climate change, about 20–30% of the vegetation has a significant correlation with climatic factors and the correlations in most areas are positive: regions with precipitation as the key influencing factor account for more than 10% of the area; regions with temperature as the key influencing factor account for less than 10% of the area; and regions with precipitation and temperature as the key influencing factors together account for about 5% of the total area. More than 70% of the vegetation has an insignificant correlation with climatic factors.

The use of remote sensing and GIS for spatio-temporal analysis of the physiological state of a semi-arid forest with respect to drought years

Drought years are a very frequent phenomenon in Israel. Between the years 1994/1995 and 2001/2002, Israel experienced four (non-consecutive) years of drought. Consequently the Yatir forest, a pine forest located in the desert fringe, suffered from a notable water shortage. The aim of this research is to detect and assess seasonal/phenological changes and inter-annual changes in the forest trees with respect to the drought effect. The use of a spectral vegetation index, namely the Normalized Difference Vegetation Index (NDVI) to detect stress conditions was implemented by using eight Landsat-TM and ETM+ images. In addition, the change detection NDVI Image Differencing technique was applied for assessing seasonal and inter-annual variations in vegetation. The results indicate similarity between the photosynthetic activity and the NDVI dynamics along the growing season. Considerable NDVI decline was observed between 1995 and 2000 due to the drought events during these years, enabling assessment of the spatial and temporal effects of such a disaster. The NDVI measured from the forest trees was found to be inversely related to the age of the trees due to strong effect of soil background in the younger forest sections that are characterized by lower vegetation density. Topographic attributes such as slope orientation (aspect) were found to affect NDVI only at the year that was not under stress. Under stress conditions, it is evident that environmental factors such as soil type, parent material, and topography are not correlated with NDVI dynamics.

The effect of vegetation on surface temperature: A statistical analysis of NDVI and climate data

We use statistical techniques to quantify the effect of interannual variations in vegetation within land covers on surface temperature in North America and Eurasia from satellite measures of surface greenness and ground based meteorological observations. During the winter, reductions in the extent of snow cover cause (in a statistical sense) temperature to rise. During the summer, increases in terrestrial vegetation within land covers cause (in a statistical sense) temperature to fall. Temperature‐induced increases in vegetation have slowed increases in surface temperature, but this feedback may be limited by the range over which temperature has a positive effect on vegetation.

Satellite observed seasonal and inter-annual variation of vegetation over the Kalahari, The Great Victoria Desert, and The Great Sandy Desert: 1979–1984

We present time-series observations by two spaceborne sensors over three desert regions, the Kalahari (in southern Africa), and the Great Victoria Desert and the Great Sandy Desert (in western Australia). The observations are by the Advanced Very High Resolution Radiometer on board the NOAA-7 satellite from April 1982 to December 1984, and by the Scanning Multichannel Microwave Radiometer on board the Nimbus-7 satellite from January 1979 to February 1985. The objective was to compare and contrast seasonal and interannual variation of vegetation over these three deserts using the normalized difference vegetation index and the 37 GHz brightness temperature. The seasonal variation from both sensors was found to be most pronounced over the Kalahari, followed by the Great Sandy Desert and the Great Victoria Desert. The normalized difference vegetation index was roughly identical over the two Australian deserts and was significantly higher for the Kalahari. Additionally, there was no consistent change from both sensors over the two Australian deserts, but a consistent decrease from 1979 to 1984 over the Kalahari was found in the 37 GHz microwave data.