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

Philip Stoker, Robin Rothfeder, Kenneth Dudley, Philip Dennison, Martin Buchert

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)331-335
Number of pages5
JournalUrban Water Journal
Volume14
Issue number3
DOIs
StatePublished - Mar 16 2017
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Water Science and Technology

Fingerprint

Dive into the research topics of 'Comparing the utility of LiDAR data vs. multi-spectral imagery for parcel scale water demand modeling'. Together they form a unique fingerprint.

Cite this