The resilience and vulnerability of terrestrial ecosystem in the Tarim River Basin, Xinjiang is critical in sustainable development of the northwest region in China. To learn more about causes of the ecosystem evolution in this wide region, vegetation dynamics can be a surrogate indicator of environmental responses and human perturbations. This paper aims to use the inter-annual and intra-annual coefficient of variation (CoV) derived by the SPOT-VGT Normalized Difference Vegetation Index (NDVI) as an integrated measure of vegetation dynamics to address the environmental implications in response to climate change. To finally pin down the vegetation dynamics, the intra-annual CoV based on monthly NDVI values and the inter-annual CoV based on seasonally accumulated NDVI values were respectively calculated. Such vegetation dynamics can then be associated with precipitation patterns extracted from the Tropical Rainfall Measuring Mission (TRMM) data and irrigation efforts reflecting the cross-linkages between human society and natural systems. Such a remote sensing analysis enables us to explore the complex vegetation dynamics in terms of distribution and evolution of the collective features of heterogeneity over local soil characteristics, climate change impacts, and anthropogenic activities at differing space and time scales. Findings clearly indicate that the vegetation changes had an obvious trend in some high mountainous areas as a result of climate change whereas the vegetation changes in fluvial plains reflected the increasing evidence of human perturbations due to anthropogenic activities. Some possible environmental implications were finally elaborated from those cross-linkages between economic development and resources depletion in the context of sustainable development.
Arid ecosystems are very sensitive to a variety of physical, chemical and biological degradation processes. Tarim Basin,
the biggest endorheic basin in the Central Asia continent, is considered as one of the least water-endowed regions in the
world and arid and semi-arid environmental conditions are dominant. For the purposes of the convention, arid, semi-arid
and dry sub-humid areas were defined as "areas, other than polar and sub-polar regions, in which the ratio of annual
precipitation to potential evapotranspiration falls within the range from 0.05 to 0.65." In this study, the Aridity Index
(AI), the ratio of precipitation and land surface temperature, was also adopted as the base method for determining dry
land types and thereby delineating boundaries and showing changes of aridity conditions in Tarim Basin. Here,
precipitation is from TRMM/PR, and land surface temperature is from Modis LST. To analyze the spatial and temporal
variations of arid environmental conditions in Tarim basin, we calculated the yearly aridity index (the ratio of total
yearly rainfall to yearly mean Land Surface Temperature) based on the accumulated monthly precipitation and the
monthly Land Surface Temperature in growing season for the period 2000-2009. The results indicated it is possible to
work out an aridity index map with more detailed spatial patterns, which is valuable for identifying human impacts by
associated with vegetation and soil moisture characters.
Satellite images and field temperature data have been used to derive the winter snow cover change in the northwest
inland regions for the period 1978-2005. Decomposition of the satellite-derived snow depth field yields several
statistically-significant EOFs (modes) and spectrums of variability according to the selection rule. The first three leading
EOF modes account for 63%, 5%, and 4% of the total variance respectively. Spatially, their sensitive regions are
characterized significantly with slope orientation, EOF1 towards northwest, EOF2 towards south and EOF3 towards
southeast. A primary analysis shows the three modes can be explained by the precipitation events induced by the water
vapor conveyed by atmospheric circulation originated from northwest, south and southeast oceans accordingly. As for
the temporal dimension, EOF1 shows evidently different from EOF2 and EOF3, and services as a stable process during
the 27 years. Their temporal processes correlate temperature in a complicated way. On the whole, the temperature
correlates EOF1, EOF2 and EOF3 more significantly, which indicates the impact of climate warming to the snow cover
is, to some extent, acted with circulation originated from different orientations, or as a result of their interactions.
Desertification in the arid and semiarid regions directly influences the density and growth status of vegetation, NDVI
(Normalized Difference Vegetation Index) has been widely used to monitor vegetation changes. This study analyzed the
spatial patters of vegetation activity and its temporal variability in Tarim Basin, Xinjiang, China since 1998 to 2007 with
NDVI data derived from SPOT4 Vegetation. The coefficient of variation (CoV) of the NDVI was used as a parameter to
characterize the change of vegetation and to compare the amount of variation in different sets of sample data. The
method of quantifying changes in CoV values for each pixel was based on linear regression. The slope of linear
regression was acted as the criterion for the change direction: pixels with a negative slope are considered to represent
ground area with decreasing amounts of vegetation, vice versa. In this paper, We calculated (1) the inter-annual CoV
based on the yearly ONDVI, the sum of the monthly NDVI in the growing season (from April to October), for each pixel
between 1998-2007 to reveal the spatial patterns of vegetation activity, (2) the intra-annual CoV based on monthly NDVI
by MVC to reflect vegetation seasonal dynamics, (3) the slope (ê) of the intra-annual CoV regression line for each pixel
to identify the overall long-term trend of vegetation dynamics. This experiment demonstrated the feasibility of applying
the CoV and its regression analysis based on long term SPOT-VGT NDVI time-series data for vegetation dynamics monitoring.
Hydrological predictions in ungauged or poorly gauged basin are crucial for sustainable water management and
environmental changes study induced by climate change. Application of remote sensing technology has retrieved lots of
spatio-temporal dataset during the past decades for references. In this study, TRMM/PR and MODIS LST data were
introduced to get spatial patterns of precipitation and temperature changes by Empirical Orthogonal Function (EOF)
technique in a mountainous watershed, southern Tianshan. An input variable group was attempted to be constructed for
the Artificial Neural Networks (ANN) to model the stream flow change based on the patterns achieved above. The
results indicate that the spatial variability patterns of meteorology can be well recognized from the remote sensing data
by EOF analysis. The stream flow process can be satisfyingly simulated with input variables captured from the leading
modes during the study period. While, since the probabilistic model was not based on full physical mechanisms, and
often times, also limited by the amount of input data, uncertainties often implicated in the output. As an example, it is
discussed through the rapidly glaciers melting phenomena induced by climate warming, which is expected to cause
change in the flow generation mechanism.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.