Shi Qing-dong

Impact of habitat heterogeneity on plant community pattern in Gurbantunggut Desert

This paper reports a geomorphologic landscape investigation, vegetation survey and soil sampling at 14 sites across the Gurbantunggut Desert between 87°37′09″-88°24′04″E and 44°14′04″-45°41′52″N. The study encountered 8 species of low trees and shrubs, 5 of perennial herbs, 8 of annual plants and 48 of ephemeral and ephemeroid plants. These species of plants represent one-third of the species found in the Gurbantunggut Desert, and their communities make up a large proportion of desert vegetation with great landscape significance. In the investigation we found that the plant communities are accordingly succeeded with the spatial variation of macro-ecoenvironment. Using Principal Component Analysis (PCA) and Correlation Analysis (CA) we found that the micro-ecoenvironment heterogeneity of aeolian sandy soil’s physical and chemical properties such as soil nutrient, soil moisture, soil salt, pH etc. only impacted the diversity of herb synusia (PIEherb) of the desert, with a negative correlation. Meanwhile, the impact of microhabitat on the plant community pattern with an antagonistic interaction made vegetation’s eco-distribution in a temporary equilibrium.

Carbon uptake and change in net primary productivity of oasis-desert ecosystem in arid western China with remote sensing technique

Arid and semi-arid ecosystems exhibit a spatially complex biogeophysical structure. According to arid western special climate-vegetation characters, the fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil and water are unmixed, using the remote sensing spectral mixture analysis. We try the method to unmix the canopy funation structure of arid land cover in order to avoid the differentiation of regional vegetation system and the disturbance of environmental background. We developed a modified production efficiency model NPP-PEM appropriate for the arid area at regional scale based on the concept of radiation use efficiency. This model refer to the GLO-PEM and CASA model was driven with remotely sensed observations, and calculates not just the conversion efficiency of absorbed photosynthetically active radiation but also the carbon fluxes that determine net primary productivity (NPP). We apply and validate the model in the Kaxger and Yarkant river basins in arid western China. The NPP of the study area in 1992 and 1998 was estimated based on the NPP-PEM model. The results show that the improved PEM model, considering the photosynthetical activation of heterogeneous functional vegetation, is in good agreement with field measurements and the existing literature. An accurate agreement (R2 = 0.85, P<0.001) between the estimates and the ground-based measurement was obtained. The spatial distribution of mountain-oasis-desert ecosystem shows an obvious heterogeneous carbon uptake. The results are applicable to arid ecosystem studies ranging from characterizing carbon cycle, carbon flux over arid areas to monitoring change in mountain-oasis-desert productivity, stress and management.

Possibility of optimized indices for the assessment of heavy metal contents in soil around an open pit coal mine area

Abstract Spectroscopy is regarded as a quick and nondestructive method to classify and quantitatively analyze many elements of the soil. Visible and Near-infrared reflectance spectroscopy offers a conductive tool for investigating soil heavy metal pollution. The main goal of this work is to obtain spectral optimized indices (RSI, NPDI and NDSI) related to soil heavy metal Arsenic (As), to estimate the As contents in soil based on geographically weighted regression model (GWR), and to investigate the plausibility of using these spectral optimized indices to map the distribution of heavy metal Arsenic in the soil of coal mining areas. The spectral optimized indices (RSI, NPDI and NDSI) derived from the original and transformed reflectance (the reciprocal (1/R), logarithm (lg R ), logarithm-reciprocal (1/lg R ) and root mean square method ( R ) were used to construct the GWR models. Then, the variables (RSIs, NPDIs and NDIs) were applied in estimating the Arsenic (As) concentration and in the mapping of the As distribution in this study region. The NPDIs calculated by the original and transformed reflectance ( R , 1/ R , lg R , 1/lg R , and R ) indicated higher correlation coefficient values than NDSI and RSI. The highest correlation coefficient and lowest p -values ( r ≥0.73 and p =0.001) were found in thenear-infrared (NIR, 780–1100 nm) and shortwave infrared (SWIR, 1100–1935 nm). From the 4 prediction models (GWR) performances, it can be seen that Model-a ( R ) showed superior performance to the other three models (Model-b (1/ R ), Model-c ( R ) and Model-d (lg R )), and it has the highest validation coefficients ( R 2 = 0.831, RMSE =4.912 μg/g, RPD=2.321) and lowest AIC (Akaike Information Criterion) value (AIC=179.96). NPDI 1417 nm, 1246 nm is more sensitive and potential hyperspectral index for As in the study area. Thus, the two band optimized index (NPDI 1417 nm, 1246 nm ) might be recommended as an indicator for estimating soil As content. The hyperspectral optimized indices may help to quickly and accurately evaluate Arsenic contents in soil, and furthermore, the results provide theoretical and data support to access the distribution of heavy metal pollution in surface soil, promoting fast and efficient investigation of mining environment pollution and sustainable development of ecology.