Assessing species distribution using Google Street View: a pilot study with the Pine Processionary Moth.

Area of Science:

• Ecology
• Biodiversity research
• Geospatial analysis

Background:

• Species distribution mapping is crucial for ecological studies.
• Collecting field occurrence data is data-intensive and time-consuming.
• Large-scale databases offer potential for deriving occurrence data.

Purpose of the Study:

• To assess the utility of Google Street View for deriving species occurrence data.
• To evaluate the accuracy of Google Street View data against field observations.
• To determine the influence of spatial scale and sampling grid on data accuracy.

Main Methods:

• A standardized procedure was developed to evaluate Pine Processionary Moth (PPM) presence using Google Street View.
• Visual records from Google Street View were compared with field data.
• Two landscapes were analyzed using sampling grids of varying extents and mesh sizes (16 km and 2 km).

Main Results:

• Google Street View data showed high similarity (96% match) to field data at a large scale (16 km grid).
• A finer grid size (2 km) resulted in significant divergence (46% match) between Google Street View and field data.
• Accuracy is dependent on spatial scale and sampling grid resolution.

Conclusions:

• Google databases can be a valuable resource for mapping species with visually identifiable signs, such as the PPM.
• The effectiveness of using Google Street View for species mapping is contingent upon the chosen spatial scale and sampling grid.
• Factors like Google Street View network coverage and host tree distribution relative to road networks influence data reliability.