Therefore, a non-intrusive and economical technique was developed to differentiate 1) the mixed grass cover under the shade tree (MUT);. Field monitoring was conducted in an urban green space to understand the spatial and temporal heterogeneity of surface hydraulic conductivity during the mixed grass life period. 82 6.5 Flow diagram to distinguish different colors of mixed grass 84 6.6 Procedure for (a) measuring the area of the selected part in the image and (b).
Introduction 1
Urban green space (i.e., lawns and parks) promotes the health, psychological activity, and physical well-being of urban residents (Wolch et al., 2014). A large number of high-capacity tensiometers or water potential sensors and moisture content sensors are usually installed to monitor the spatial heterogeneity of absorption in green infrastructure (Garg et al., 2015c). Then, periodic maintenance of the sensors would not be feasible during long-term monitoring (Bhatt et al., 2016).
Review of literature 6
- General 7
- Crack formation in vegetated soil 7
- Effect of plant parameters on suction induced in vegetated soil 9
- Design of instrumentation plan in vegetated soil 13
- Effect of suction on plant parameters 16
- Interpretation of soil surface water content 17
- Spatial and temporal heterogeneity of surface hydraulic conductivity in urban
- Objective and scope of the study 24
- Organization of the thesis 25
This low suction can further influence hydraulic conductivity (Garg et al., 2015b), i.e. the water balance of green infrastructure. Furthermore, the share of shade grass cover in green infrastructure could change over time (Garg et al., 2015b). Furthermore, surface hydraulic conductivity influences the long-term performance of green infrastructure (Garg et al., 2015 a, b, c).
Crack formation in soil vegetated with crop species 26
General 27
This chapter presents the effect of vegetation (cowpea) growth on crack formation to investigate correlation between vegetation growth and cracking. Plant growth was expressed in terms of shoot parameters (shoot length (SL) and leaf area index (LAI)). Experimental test pots were used to observe crack formation on vegetated and bare soil in greenhouse.
Material and methods 27
- Soil property 27
- Plant species and germination condition 29
- Test plan 29
- Experimental setup 31
- Procedure for analysis of CIF and LAI 32
Photographs were carefully taken from the same angle (450) and height (0.75 m from the floor) using an adjustable height frame (Fig. 3.3), to minimize any observational errors for CIF analysis. In this study, the image processing software ImageJ (Rasband, 2011) was used to analyze the surface crack and blade surface. The ratio between the leaf area and the total plant roof area is used to calculate the LAI.
Results and discussion 35
- Variation of CIF with time for both bare and vegetated soil 35
- Relationship between shoot parameters and CIF for vegetated soil 37
Binary imaging of pot cracks shows that there was an increase in cracks up to 33 days after transplanting. A linear increase in mean SL was observed initially up to 50 days in the vegetation phase of the plant, as shown in Fig. This increase in cracking is attributed to absorption caused by transpiration in the case of vegetated soil.
Crack formation in soil vegetated with non-crop species 41
- General 42
- Material and methods 42
- Test plan, setup and instrumentation 42
- Test procedure 45
- Results and discussion 46
- Summary and conclusion 50
Vegetation density (VD) is defined as the ratio between the two-dimensional vegetation area and the considered two-dimensional total area. The consequence of soil drying in a 7-day interval is naturally desiccation cracks in both bare and overgrown soil. The maximum crack in each drying cycle and the corresponding vegetation growth were quantified by CIF and vegetation density using the color threshold technique.
It is seen that up to the 3rd cycle the cracks in both bare and vegetated soil are seen to be of similar size where the increase in vegetation density is relatively less Fig. 4.2b). However, in the case of vegetated soil, there is almost no increase in CIF after 40 % vegetation density and the 6th drying cycle (DC). This is in contrast to the case of the same vegetative soil where the cultivar species Vigna unguiculata was grown.
It is seen that for the first 5 cycles, the absorption exhibited by the vegetated soil is relatively greater than the bare soil. However, in the last 3 monitoring cycles, the uptake exhibited by both bare soil and vegetated soil is seen to be relatively the same. It can be seen that the grass species helped to reduce (≈ 20 %) the maximum CIF of the bare soil.
An increase in vegetation density of 40% limits further increases in CIF for mixed native species.
Modeling soil-plant-atmosphere interaction: Effects of plant
Methodology 52
- Finite element model 52
- Finite element mesh and boundary conditions 54
- Input properties of bare soil, soil-root composite and plant 58
- Analysis plan and procedures 60
The Rdfs proposed by Feddes et al. 1978 ) and Prasad ( 1988 ) were adopted in previous studies to understand the suction induced in the soil–root composite ( Zhu and Zhang, 2015a , b ). These illustrate the effect of roots on the suction induced in the soil-root composite during the absence of transpiration. For the second scenario, three simulations were performed to analyze the effect of changing Rdf alone on suction induced in the soil-root composite due to evapotranspiration.
The reason for such difference between calculated suctions can be attributed to the dissimilar hydraulic properties of bare soil and soil-root composite (Leung et al., 2015b). The depth of the EDZ can be inferred from the large difference between the suction values within and below the EDZ (i.e. kPa). The value of suction at the depth of maximum RAI at S2-L is higher than that of S2-U and S2-C.
It is observed that the depth of SIZ for uniform Rdf and nonlinear Rdf is 11% and 13% higher than that of linearly decreasing Rdf. Any variation in the depth of the SIZ due to a change in LAI was found to be insignificant. It can also be observed that the suction at the depth of EDZ increases with the increase of LAI.
However, it can be observed from the suction values that the soil-root composite with the highest leaf area (i.e., LAI = 3.5) reaches the wilting point earlier than the others at the depth of the highest RAI.
Summary and Conclusions 73
A novel colour analysis technique to quantify the spatial heterogeneity
Materials and Methods 76
- Site description 76
- Soil properties 78
- Overview of testing site comprising mix grass cover in tree vicinity 78
- Field monitoring programme 81
- Colour Analysis 83
- Colour Analysis Procedure 87
In this study, field monitoring was carried out on mixed grass cover in the vicinity of the trees. This field monitoring is intended to better understand the proportion of mixed grass cover variation in the vicinity of trees. It is clear that mixed grass cover conditions and shoot growth are spatially uncertain (Philipp and Rath, 2002).
The laboratory color space was adopted for the present study to investigate the spatial heterogeneity of mixed grass cover. This is because the spatial heterogeneity of the mixed grass can be identified by the difference in lightness between the mixed grass cover. In addition, color differentiation of the entire blend bar in the selected area is found to be possible in the Lab color space.
Calculate the ratio of the different categories of grass mixture to the total area of the cropped image. To separately quantify the area occupied by MUT, MUS and MWS, the grass mixture is categorized with the option of a color threshold (see Fig. The three categories of grass mixture can be identified separately by adjusting the values of L, a and b to the scales marked in Fig.
The processed images for three types of mixed grass cover, i.e., MUT, MUS and MWS were displayed in the month of April, as shown in Fig.
Results and discussions 88
- Mix grass cover variation during monitoring period 88
- Surface area variation during monitoring period 111
- Comparison of normalized surface area of mix grass
The surface area of shoots under the mixed grass was found to be 6% - 7% of the total surface area of the mixed grass. Normalized surface area of the entire mixed grass cover is observed to increase with increase in suction from 1 kPa to 6 kPa. Then, normalized surface area of the entire mixed grass is observed to decrease with increase in suction up to 3642 kPa.
Variation in normalized surface area of completely mixed grass and three categories of mixed grass. The normalized surface area of complete grass mixture appears to decrease by 2% with an increase in suction from 2431 kPa to 3624 kPa. The reason for this small difference is the decrease in the normalized surface area of grass mixtures due to shrinkage (Jeong et al., 2013).
It can be observed that (Fig. 8.2) the water content in the residual zone is close to hygroscopic water content. In addition, mixed grass in 63% of the surface area was partially wilted at the end of November. Spatial and temporal heterogeneity of plant growth and hydraulic conductivity was found over the lifetime of mixedgrass.
Further research is needed to investigate the diurnal variation of stomatal conductance and surface area of mixed grass during a continuous drying period.
Assessment of soil surface water content using light reflection theory 116
Materials and Methods 117
- Soil properties 117
- Test plan 120
- Design and development of experimental setup 122
- Colour analysis procedure 122
- Gray scale image 122
- Quantification of Gray value 123
The green space was divided into 170 grids to monitor vegetation density and surface hydraulic conductivity. The field monitoring program is designed to understand the spatial heterogeneity of mixed vegetation density and surface hydraulic conductivity. Categorization of green space into small grids for monitoring spatial heterogeneity of vegetation cover and surface hydraulic conductivity.
The field monitoring program was conceived to understand the spatial and temporal variation of vegetation density and hydraulic conductivity in green areas. The main focus of the current study is to understand the effect of spatial and temporal heterogeneity of vegetation density on surface hydraulic conductivity. Wood in the center of the edge has been marked as a reference to discuss the variation in vegetation density and surface hydraulic conductivity.
It is observed that hydraulic conductivity increases by only 51% due to an increase in vegetation density by 0.57 m2/m2 during the first three months. While it is observed that hydraulic conductivity increases by 296% due to the above-mentioned change in vegetation density in April. Previous researchers show that an increase in hydraulic conductivity could be possible due to the presence of vegetation.
Further studies are required to investigate the effect of plant regrowth on spatial heterogeneity of vegetation density and surface hydraulic conductivity.
Results and Discussion 128
- Comparison of gray values obtained at the selected surface water
General 133
Materials and Methods 133
- Site description 133
- Soil properties 133
- Overview of the green space 134
- Instrumentation used in the green space 135
- Field monitoring programme 137
- Design of non-destructive image analysis approach to quantify the
- Surface hydraulic conductivity measurement in the green
- Grass cover change during monitoring period 141
- Variation of vegetation density in the site 144
- Spatial heterogeneity of surface hydraulic conductivity 145
In contrast to vegetation density, surface hydraulic conductivity appears to vary spatially throughout the monitoring period. Hydraulic conductivity was not found to vary with change in radial distance during the first three months. While in the subsequent nine months, variation in hydraulic conductivity with change in radial distance from the center was found.
The trend of hydraulic conductivity change at the end of February and March is similar to that of January. The difference between the rates of increase in hydraulic conductivity can be attributed to the presence of shredded leaves and dried grass during February and March. Furthermore, a significant increase in hydraulic conductivity at the end of April can also be attributed to the significant increase in vegetation density during April (van Noordwijk et al., 1991).
The increase in hydraulic conductivity with shoot growth appears to occur at grids within a longitudinal distance of 2 m from the center. In contrast to the previous two months, a substantial increase in hydraulic conductivity at grids close to the tree trunks can be observed in December. The lifespan of grass mixtures and its effect on the hydraulic conductivity of the surface were revealed in this study.
The present study investigated the spatial and temporal heterogeneity of surface hydraulic conductivity in green space vegetated with deciduous species (i.e., Poaceae and Bauhinia purpurea). Whereas, the increase in hydraulic conductivity would be until the absence of chopped leaves and dried grass (at the end of April). Effects of Cynodon dactylon and Schefflera heptaphylla roots on water infiltration rate and soil hydraulic conductivity.
