Comparing the utility of LiDAR data vs. multi-spectral imagery for parcel scale water demand modeling

Abstract

In this paper we examine whether land-cover measures derived from multi-spectral (MS) imagery in combination with light detection and ranging (LiDAR) data sources better predict parcel scale urban water consumption than measures derived solely from MS imagery. Land-cover measures such as the percentage of impervious surface and vegetative cover are important predictors of household level water use. This study found that the additional effort required to obtain LiDAR data does not appear to add predictive power for water demand modeling. We suggest that MS imagery is just as useful estimating household level water demand.

Keywords:

• Remote sensing
• water demand
• ground cover
• residential buildings

Acknowledgements

Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Additional information

Funding

This research was supported by NSF EPSCoR grant EPS 1208732 awarded to Utah State University, as part of the State of Utah Research Infrastructure Improvement Award.