Google Street View as an alternative method to car surveys in large-scale vegetation assessments

Environ Monit Assess. 2015 Oct;188(10):560. doi: 10.1007/s10661-016-5555-1. Epub 2016 Sep 13.

Abstract

Car surveys (CS) are a common method for assessing the distribution of alien invasive plants. Google Street View (GSV), a free-access web technology where users may experience a virtual travel along roads, has been suggested as a cost-effective alternative to car surveys. We tested if we could replicate the results from a countrywide survey conducted by car in Portugal using GSV as a remote sensing tool, aiming at assessing the distribution of Eucalyptus globulus Labill. wildlings on roadsides adjacent to eucalypt stands. Georeferenced points gathered along CS were used to create road transects visible as lines overlapping the road in GSV environment, allowing surveying the same sampling areas using both methods. This paper presents the results of the comparison between the two methods. Both methods produced similar models of plant abundance, selecting the same explanatory variables, in the same hierarchical order of importance and depicting a similar influence on plant abundance. Even though the GSV model had a lower performance and the GSV survey detected fewer plants, additional variables collected exclusively with GSV improved model performance and provided a new insight into additional factors influencing plant abundance. The survey using GSV required ca. 9 % of the funds and 62 % of the time needed to accomplish the CS. We conclude that GSV may be a cost-effective alternative to CS. We discuss some advantages and limitations of GSV as a survey method. We forecast that GSV may become a widespread tool in road ecology, particularly in large-scale vegetation assessments.

Keywords: Alien invasive plants; Eucalypt; Remote sensing; Road ecology; Roadside; Wildling.

MeSH terms

  • Environmental Monitoring / methods*
  • Eucalyptus*
  • Geographic Mapping*
  • Internet
  • Introduced Species / statistics & numerical data*
  • Plant Weeds*
  • Portugal
  • Remote Sensing Technology*
  • Surveys and Questionnaires