Abstract
Evapotranspiration estimation has benefitted from recent advances in remote sensing and GIS techniques particularly in agricultural applications rather than urban environments. This paper explores the relationship between urban vegetation evapotranspiration (ET) and vegetation indices derived from newly-developed high spatial resolution WorldView-2 imagery. The study site was Veale Gardens in Adelaide, Australia. Image processing was applied on five images captured from February 2012 to February 2013 using ERDAS Imagine. From 64 possible two band combinations of WorldView-2, the most reliable one (with the maximum median differences) was selected. Normalized Difference Vegetation Index (NDVI) values were derived for each category of landscape cover, namely trees, shrubs, turf grasses, impervious pavements, and water bodies. Urban landscape evapotranspiration rates for Veale Gardens were estimated through field monitoring using observational-based landscape coefficients. The relationships between remotely sensed NDVIs for the entire Veale Gardens and for individual NDVIs of different vegetation covers were compared with field measured urban landscape evapotranspiration rates. The water stress conditions experienced in January 2013 decreased the correlation between ET and NDVI with the highest relationship of ET-Landscape NDVI (Landscape Normalized Difference Vegetation Index) for shrubs (r2 = 0.66) and trees (r2 = 0.63). However, when the January data was excluded, there was a significant correlation between ET and NDVI. The highest correlation for ET-Landscape NDVI was found for the entire Veale Gardens regardless of vegetation type (r2 = 0.95, p > 0.05) and the lowest one was for turf (r2 = 0.88, p > 0.05). In support of the feasibility of ET estimation by WV2 over a longer period, an algorithm recently developed that estimates evapotranspiration rates based on the Enhanced Vegetation Index (EVI) from MODIS was employed. The results revealed a significant positive relationship between ETMODIS and ETWV2 (r2 = 0.9857, p > 0.05). This indicates that the relationship between NDVI using high resolution WorldView-2 imagery and ground-based validation approaches could provide an effective predictive tool for determining ET rates from unstressed mixed urban landscape plantings.
Highlights
In macro-scale water management, a precise prediction of water lost through evapotranspiration (ET) is always challenging, when estimating ET from mixed vegetation types [1]
The significant difference in median of NDVI1 and aNDVI1 compared to similar pairs resulted in the selection of aNDVI1
Turf grasses often have low Normalized Difference Vegetation Index (NDVI) and so prediction of vegetation water requirement based on turf grasses may often lead to insufficient irrigation volumes
Summary
In macro-scale water management, a precise prediction of water lost through evapotranspiration (ET) is always challenging, when estimating ET from mixed vegetation types [1]. Some vegetation dynamics such as plant vigor, vegetation density and canopy cover can be quantified into biophysical parameters. Vegetation biophysical characteristics can be estimated from field measurements, aerial photography or satellite data visualization and interpretation. The two traditional methods of ground measurement and aerial photography are regularly used, there have been significant advances in remote sensing (RS) that have increased the accuracy of vegetation analysis. Satellite data cover large regions that do not need intensive ground measurement, some field work helps to improve image interpretation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
