The Glenelg to Adelaide Parklands Recycled Water Project (GAP) ... 0 from weather station. - Ground data. Satellite images. - Satellite data. ET. L. = ET. 0. X K. L.
Water Demand of Heterogeneous Urban Vegetation
B. Sc. Irrigation Engineering, Isfahan University of Technology, 2000 Isfahan, Iran
Industry Experience Irrigation specialist (Design and implementation of pressurized irrigation), Arya Agri-tech company, Isfahan Science and Technology Town, Isfahan, Iran (2001- 2008). Chair of the Board (COB) of the Arya Agri-tech Company, Isfahan Science and Technology Town, Isfahan, Iran (20052008)
M. Sc. Soil and Water Engineering, Universiti Putra Malaysia (UPM), 2009. Kuala Lumpur, Malaysia
PhD. Civil Engineering, University of South Australia, 2014 Adelaide, Australia
Postdoctoral research associate. Ecohydrology, University of South Australia, 2014. Adelaide, Australia
Ecosystem Service Valuation
Green Infrastructure
Water Use Optimization soil moisture, salinity & ET
Maximizing Human Well-being
http://www.chikyu.ac.jp/rihn_e/project/PR-2014-01.html
Increasing urbanization and growing population places greater demands on dwindling water supplies especially in arid and semi arid areas like Australia, which is known as the driest inhabited continent on earth.
Glenelg to Adelaide Parklands Pipeline - GAP
Reclaimed wastewater would be an alternative water resource that provides both water and nutrients.
The Glenelg to Adelaide Parklands Recycled Water Project (GAP) Recycled water is now flowing to the Adelaide Parklands following the early completion of the Federal and State Gov funded $76.25 million project. The 32 kilometre network currently provides recycled water from the Glenelg Wastewater Treatment Plant to Adelaide’s Parklands and city gardens.
http://www.aquaplanirrigation.com.au/systems.html
Adelaide Parklands The Adelaide Parklands are 28 parks with an area of 760ha of green open space that provides a rich social, environmental and recreational resources.
Sustainable and efficient irrigation management necessitates better understanding of water requirements of plants. ET = Evaporation + Transpiration Evaporation: the movement of water to the air from sources such as soil, vegetation interception and water bodies. Transpiration: the movement of water within a plant and the subsequent loss of water as vapour through stomata in its leaves.
• Research on water requirements of agricultural crops is well established. However, less research has been conducted on this aspect for urban green space mixed plantings. • Most research so far has focused on water needs for turf grasses and not for other landscape plant species; various species of trees, shrubs and turf grasses with different planting densities and microclimates.
Traditional methods of ET estimation are mostly -time-consuming
-relatively expensive
-point coverage insufficient for large areas
ET estimation has benefitted from advances in remote sensing and GIS techniques especially in agriculture, riparian vegetation and forestry.
ET & Vegetation Indices
Potential relationship between urban vegetation ET and vegetation indices in an urban park - Satellite
data Determination of vegetation indices (VI)
Satellite images
Modelling ET - VI
- Ground data ET0 from weather station
X
In-situ approaches to estimate = ETL = ET0 X KL landscape plant coefficient - KL Sustainable irrigation scheduling
WorldView-2 for urban studies From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences) was selected. Image specification
Band No.
Panchromatic Multi-spectral
-
• • • •
• • • •
Standard bands Red Green Blue NIR1 New bands Coastal blue Yellow Red-edge NIR2
5 3 2 7
Spectral resolution (nm) 450-800
Spatial resolution (cm) 46
630-690 510-580 450-510 770- 895 184 (at Nadir)
1 4 6 8
400-450 585-625 705-745 860-1040
NDVIs
Bands combination
NDVI1
WV2: (band7 − band5)/(band7 + band5)
NDVI2
WV2: (band8 − band6)/(band8 + band6)
NDVI3
WV2: (band8 − band4)/(band8 + band4)
NDVI4
WV2: (band6−band1)/(band6 + band1)
NDVI5
WV2: (band6 − band5)/(band6 + band5)
Five combinations of 8 bands to estimate NDVI
High resolution WV2 imagery Check for orthorectification Create AOI Atmospheric correction Calculate 5 NDVIs •NDVI1 •NDVI2 •NDVI3 •NDVI4 •NDVI5
•(band7 − band5)/(band7 + band5) •(band8 − band6)/(band8 + band6) •(band8 − band4)/(band8 + band4) •(band6−band1)/(band6 + band1)
Flowchart of the analysis procedure of NDVI-WV2
Calculate 5 aNDVIs •aNDVI1 •aNDVI2 •aNDVI3 •aNDVI4 •NDVI5
•(band6 − band5)/(band6 + band5)
Calculate statistics on each NDVI (1-5) Compare statistics of NDVIs Find the most reliable WV2 - NDVI
•(band7 − band5)/(band7 + band5) •(band8 − band6)/(band8 + band6) •(band8 − band4)/(band8 + band4) •(band6−band1)/(band6 + band1) •(band6 − band5)/(band6 + band5)
Land covers in Veale Gardens: Trees, Shrubs, Turf grasses, Impervious Pavements and Water Bodies
Flow chart of steps to create NDVI image andNDVI to calculate Steps to create imagezonal and statistics calculate
March 2012
Months Vegetation cover Trees Shrubs Turf
Area (m2) 21896.82 10051.26 54834.97
MEAN LNDVI 0.81 0.72 0.68
STD
zonal statistics WV2 multispectral imagery
0.28 0.34 0.34
WV2 panchromatic imagery
Check for orthorectification
June 2012
Trees Shrubs Turf
21896.82 0.61 10051.26 0.51 54834.97 0.53
0.35 0.41 0.41
August 2012
Trees Shrubs Turf
21896.82 0.66 10051.26 0.60 54834.97 0.64
0.32 0.34 0.30
November 2012
Trees Shrubs Turf
21896.82 0.84 10051.26 0.79 54834.97 0.77
0.19 0.26 0.25
January 2013
Create AOI
Trees Shrubs Turf
21896.82 0.74 10051.26 0.67 54834.97 0.61
0.24 0.30 0.28
Geo-referencing
Atmospheric correction (ATCOR2)
Generate NDVI for each image (using bands 5 & 7) using ERDAS IMAGINE
Clip NDVI results to VG borders
Zonal statistics to calculate NDVI of the entire VG using ArcMap
Digitizing 5 different surface cover types: trees, shrubs, turf, water bodies, and impervious pavements
Overlay NDVI image of AOI and digitized landcovers of VG surface
Zonal statistics to calculate the individual NDVIs for each cover types using ArcMap
Linear regression analyses of Landscape NDVI-ET relationships
Zonal statistics of Landscape NDVI for each vegetation type in Veale Gardens Vegetation Types
Area (m2)
Mean Landscape NDVI (STD) March-12
June-12
August-12
November-12
Jane-13
Tree
21,896.8
0.81 (0.28)
0.61 (0.35)
0.66 (0.32)
0.84 (0.19)
0.74 (0.24)
Shrub
10,051.3
0.72 (0.34)
0.51 (0.41)
0.6 (0.34)
0.79 (0.26)
0.67 (0.30)
54,835
0.68 (0.34)
0.53 (0.41)
0.64 (0.30)
0.77 (0.25)
0.61 (0.28)
Turf
Temporal variation of ETL, mean Landscape NDVI, and each vegetation type’s Landscape NDVI
Independent Remotely-Sensed ET Estimation Using MODIS-EVI Remotely sensed algorithm by Nagler et al.,2013 using Enhanced Vegetation Index (EVI) from MODIS for ET estimation : EVI = 2.5 × (NIR − Red)/(1 + NIR + (6 × Red − 7.5 × Blue))
The results show significant positive relationships between ETMODIS & ETWUCOLS
r2 = 0.9902, p>0.05
ETMODIS & ETWV2
r2 = 0.9857, p>0.05
SUMMARY • The significant positive correlation between ETfield-based & ETremote sensing-based demonstrated the performance and validity of a remotely-sensed ET estimation approach using high resolution images. • The results from using coarse resolution MODIS support the capability and feasibility of predicting ET rates for mixed urban vegetation. The use of MODIS over Veale Gardens shows variability in water demand since 2000 demonstrating that this method can be used for monitoring purposes.
Thank You
