Bing Cheng Si

Detecting grassland spatial variation by a wavelet approach

Insight into the spatial variation of an ecosystem can provide better understanding of ecological processes and patterns in different scales. Detecting these multiple scales of spatial variation in grassland landscapes is valuable for determining management options, designing proper sampling regimes, and selecting suitable resolutions of remote sensing products. The objective of this study is to examine how environmental factors affect spatial variation of biophysical properties in mixed grassland ecosystems. Field leaf area index (LAI), soil moisture, and topographical parameters (relative elevation, upslope length, and a wetness index) were obtained in three parallel transects of a grassland ecosystem in Saskatchewan, Canada in 2004. One 20‐m resolution SPOT 4 (HRVIR) image was acquired at the same period of the growing season but in the following year. Normalized difference vegetation index (NDVI) was calculated from the satellite image of the centre 381‐m transect and two extensive 2560‐m perpendicular transects. A wavelet approach was used to identify the scales of variations. Statistical results showed that LAI is significantly correlated to the wetness index (r2 = 0.37) and soil moisture (r2 = 0.43). The wetness index is better than relative elevation and upslope length in demonstrating the effect of topography on grassland vegetation. The variation of soil moisture is significant at two small scales of about 20 m and 40 m, and that of the wetness index is at the large scale of 120 m. The variation of grassland LAI is significant at three scales (20 m, 40 m, and 120 m), which indicates that the spatial variation of LAI might be controlled by both topography and soil moisture, though the 120 m is the dominant scale of variation in LAI. NDVI significantly correlated with grassland LAI along the centre transect. The effect of topography on grassland LAI is also proven by the significant relationships between NDVI and the wetness index. The wavelet analysis identifies the variation of two extensive transects at the scale of about 120 m, which is similar to the dominant variation scale of grassland LAI. These results confirmed that the effect of topography on spatial variation can be identified from the appropriate satellite image. This study suggests that the spatial scales of soil and topographic data aid in the selection of appropriate satellite image resolution for monitoring and managing ecosystems.

Studying mixed grassland ecosystems II: optimum pixel size

It was determined in a companion paper that the litter-corrected adjusted transformed soil-adjusted vegetation index (L-ATSAVI) was the best leaf area index (LAI) indicator in a mixed grassland ecosystem. To optimize the sampling procedures and address the scaling issues for the mixed grass ecosystem, this study examined the dominant scale of spatial variation in both LAI and L-ATSAVI using two methods, namely Mexican hat wavelet analysis and semivariogram analysis. The results showed that both methods can identify grassland spatial variation, and the cyclicity (the nature repetition in a dataset) of grassland LAI was about 140 m along the central transect of five parallel transects within the study area. The advantage of wavelet analysis over semivariogram analysis for spatial pattern interpretation was that it could identify the exact location of the transition. The wavelet analysis demonstrated that the cyclicity of L-ATSAVI also corresponded well with features of grassland LAI along the transect. Therefore, following the sampling theorems, a pixel size of less than 35 m will retain most of the spatial variation of grassland LAI in our study area. In terms of this optimum pixel size, the scale of ground-based hyperspectral data and LAI along the transect was simulated using a low-pass filtering procedure with a 30 m moving window. Statistical analysis indicated that scale-simulated L-ATSAVI could significantly explain more grassland LAI (r2 up to 89%) than the original 3 m resolution. This conclusion can be further applied to select the optimal pixel size of remote sensing images and detect the hierarchical characteristics in a grassland landscape.

A novel method for identifying hydrophobicity on fungal surfaces

Fungal surface hydrophobicity has many ecological functions and water contact angles measurement is a direct and simple approach for its characterization. The objective of this study was to evaluate if in-vitro growth conditions coupled with versatile image analysis allows for more accurate fungal contact angle measurements. Fungal cultures were grown on agar slide media and contact angles were measured utilizing a modified microscope and digital camera setup. Advanced imaging software was adopted for contact angle determination. Contact angles were observed in hydrophobic, hydrophilic and a newly created chronoamphiphilic class containing fungi taxa with changing surface hydrophobicity. Previous methods are unable to detect slight changes in hydrophobicity, which provide vital information of hydrophobicity expression patterns. Our method allows for easy and efficient characterization of hydrophobicity, minimizing disturbance to cultures and quantifying subtle variation in hydrophobicity.

Factors controlling soil water storage in the hummocky landscape of the Prairie Pothole Region of North America

Biswas, A., Chau, H. W., Bedard-Haughn, A. K. and Si, B. C. 2012. Factors controlling soil water storage in the hummocky landscape of the Prairie Pothole Region of North America. Can. J. Soil Sci. 92: 649-663. The Prairie Pothole Region (PPR) in North America is unique hummocky landscape containing hydrologically closed topographic depressions with no permanent inlet or outlet. Knowledge about the controls of soil water distribution in the landscape is important for understanding the hydrology in the PPR. In this study, we investigated the correlation between soil water storage and different controlling factors over time. Time domain reflectometry and neutron probe were used to measure soil water storage up to 1.4 m depth over 4 yr along a 576-m long transect at St. Denis National Wildlife Area, Saskatchewan, Canada, which represent a typical landscape of the PPR. Soil and vegetation properties were measured along the transect, and various terrain indices were calculated from the digital elevation map of the study area. Soil texture (e.g., correlation coefficient, r=-0.57 to -0.73 for sand) provided one of the best explanations for the variations in soil water storage by controlling the entry and transmission of water within soil in the semi-arid climate of study area. Bulk density (r=-0.22 to -0.56), depth of A horizon, (r=0.18 to 0.49), C horizon (r=0.29 to 0.69), and CaCO3 layer (r=0.31 to 0.79) influenced the water transmission through soil and were correlated to soil water storage. Beside soil properties, topographic wetness index (r=0.47 to 0.67), slope (r=-0.41 to -0.56), convergence index (r=-0.29 to -0.60), and flow connectivity (r=0.27 to 0.60) were also correlated to soil water storage. However, multiple linear regressions showed a consistent high contribution from soil properties such as sand, organic carbon, depth of CaCO3 layer, and bulk density in explaining the variability in soil water storage. A substantial contribution from topographic variables such as wetness index, gradient, and solar radiation was also observed. Therefore, unlike other geographic regions, the soil-water storage variations in the PPR are controlled by a combination of soil and terrain properties with dominant control from soil characteristics at the field scale.

Tritium analysis shows apple trees may be transpiring water several decades old

&NA; Recent work has shown evidence of ecohydrological separation whereby plants appear to use a less mobile soil water pool that does not mix with more mobile soil water, groundwater, and streamflow. Although many elements of this two water worlds hypothesis remain to be tested and challenged, one key question is “how old might the less mobile water used by plants be?” Such a question is methodologically difficult to answer: stable isotope tracing makes it difficult to resolve any water age older than a few years since the signal gets so damped. Tritium—a useful radiogenic isotope and age dating tool, is now difficult to use in natural systems because most bomb tritium has washed out of soil profiles. Here, we leverage new data from an unusually deep, homogenous soil profile that preserves the mid‐1960s tritium bomb signal. We sample the Fuji apple trees (Malus pumila Mil) growing on this site that have root systems that penetrate over 15 m and utilize water from within the bomb peak soil water distribution (extracted via cryogenic extraction). Our data show that water used by these trees is on average 29 years old. Bayesian mixing analysis suggests that 40 ± 30% of fruit tissue water came from depths between 4 and 9 m within the soil profile (36 ± 9 years old); 60 ± 29% was equally divided between 0 and 4 m and 9–15 m ranges (13 ± 5 years old). These findings suggest that trees can use quite old less mobile water, highlighting the separation in ages between more mobile soil water and water in transit in sap flow.

Assessment of alcohol percentage test for fungal surface hydrophobicity measurement

Aim:  To determine whether assessing the penetration of solutions with different concentrations of ethanol (alcohol percentage test: APT) on fungal surfaces is effective in characterization of hydrophobicity on fungal surfaces.

Soil wetting state and preferential transport of Escherichia coli in clay soils

Transport of Escherichia coli (E. coli) through soil to drinking and recreational water may pose a serious health risk. The objective of this study was to determine how initial preferred soil wetting state influences the preferential transport of E. coli in a clay soil. A strain of E. coli marked with green fluorescent protein (gfp) was gravity-fed-sprinkler-applied as simulated rainfall to three replicates in different wetting states, along with Cl- and adsorptive dye near Plenty, SK. Canada. After 48 h, a 50 × 50 × 50 cm3 block was excavated to determine the transport pathways. Digital image analysis of horizontal sections provided estimates of dye coverage. Escherichia coli were significantly filtered in the top 10 cm of soil with concentration profiles similar to that of Cl-. Ratios of E. coli to Cl- did not show significant differences among treatments (P < 0.05) and indicated that below 10 cm depth, E. coli and Cl- were preferentially transported along the same pathways with no significant differenc...

Parameter estimation using the falling head infiltration model: Simulation and field experiment

[1] Soil hydraulic parameters have high spatial variability. A large number of measurements are needed to characterize these parameters in a field. Therefore there is a need to develop quicker and cheaper methods to determine soil hydraulic parameters. The objective of this study was to examine the uniqueness of the K f s (field saturated hydraulic conductivity) and α (inverse macroscopic capillary length scale) parameters obtained through inverting the falling head infiltration model. Five simulated scenarios were imposed on the cumulative infiltration data [L(t)] during the inverse procedure. The uniqueness of the K f s and α estimates under each scenario was studied. In situ infiltration data were used to verify the scenario that provided unique parameter estimates. It appears that the falling head infiltration model can be used to simultaneously estimate the K f s and α parameters when estimates (or published values) of the α parameter for the site are available.

Model Averaging for Semivariogram Model Parameters

Soil varies considerably from location to location (Nielsen et al., 1973). Knowledge on soil spa‐ tial variability is important in ecological modelling (Burrough, 1983; Corwin et al., 2006), envi‐ ronmental prediction (Trangmar et al., 1985), precision agriculture (Goderya, 1998), soil quality assessment (Heuvelink and Pebesma, 1999; McBratney et al., 2000), and natural resour‐ ces management. Adequate understanding of the variability in soil properties as a function of space and time is necessary for developing logical, empirical and physical models of soil and landscape processes (Burrough, 1993; Foussereau et al., 1993; Wilding et al., 1994). Geostatis‐ tics, a widely used approach, has been used to identify the spatial structure in the variability of soil attributes (Vieira, 2000; Carvalho et al., 2002; Vieira et al., 2002). Semivariance function characterizes the spatial continuity between points. When the semivariance is plotted against the lag distance or separation distance between points, the plot is called semivariogram (Fig. 1; McBratney and Webster, 1986; Isaaks and Srivastava, 1989). The structure of the semivario‐ gram is explained by three properties; the nugget, the sill and the range (Fig. 1). These spatial structures of semivariogram help in identifying autocorrelation and replicating samples, re‐ vealing dominant pattern in data series, identifying major ongoing processes (Si et al., 2007), designing experiments (Fagroud and van Meirvenne, 2002) and monitoring networks (Pra‐ kash and Singh, 2000), selecting proper data analysis method and interpreting data (Lambert et al., 2004), and assessing simulation and uncertainty analysis in a better way (Papritz and Du‐ bois, 1999). The semivariogram structures also help to quantify spatial dependence between observations. Modelling of observed semivariance values helps in predicting the spatial distri‐ bution of attribute values (Goovaerts, 1998). The spatial distribution of attribute values is very important in separating random noise in semivariance, and interpolation and mapping analy‐ sis such as kriging (Deutsch and Journel, 1998; Nielsen and Wendorth, 2003).

Joint Multifractal Analysis of Scaling Relationships Between Soil Water-Retention Parameters and Soil Texture

Abstract Soil water-retention characteristics at measurement scales are generally different from those at application scales, and there is scale disparity between them and soil physical properties. The relationships between two water-retention parameters, the scaling parameter related to the inverse of the air-entry pressure (α v G , cm −1 ) and the curve shape factor related to soil pore-size distribution (n) of the van Genuchten water-retention equation, and soil texture (sand, silt, and clay contents) were examined at multiple scales. One hundred twenty-eight undisturbed soil samples were collected from a 640-m transect located in Fuxin, China. Soil water-retention curves were measured and the van Genuchten parameters were obtained by curve fitting. The relationships between the two parameters and soil texture at the observed scale and at multiple scales were evaluated using Pearson correlation and joint multifractal analyses, respectively. The results of Pearson correlation analysis showed that the parameter α v G was significantly correlated with sand, silt, and clay contents at the observed scale. Joint multifractal analyses, however, indicated that the parameter α v G was not correlated with silt and sand contents at multiple scales. The parameter n was positively correlated with clay content at multiple scales. Sand content was significantly correlated with the parameter n at the observed scale but not at multiple scales. Clay contents were strongly correlated to both water-retention parameters because clay content was relatively low in the soil studied, indicating that water retention was dominated by clay content in the field of this study at all scales. These suggested that multiple-scale analyses were necessary to fully grasp the spatial variability of soil water-retention characteristics.