Google Street View shows promise for virtual street tree surveys

Geospatial technologies are increasingly relevant to urban forestry, but their use may be limited by cost and technical expertise. Technologies like Google Street View™ are appealing because they are free and easy to use. We used Street View to conduct a virtual survey of street trees in three municipalities, and compared our results to existing field data from the same locations. The virtual survey analyst recorded the locations of street trees, identified trees to the species level, and estimated diameter at breast height. Over 93% of the 597 trees documented in the field survey were also observed in the virtual survey. Tree identification in the virtual survey agreed with the field data for 90% of trees at the genus level and 66% of trees at the species level. Identification was less reliable for small trees, rare taxa, and for trees with multiple species in the same genus. In general, tree diameter was underestimated in the virtual survey, but estimates improved as the analyst became more experienced. This study is the first to report on manual interpretation of street tree characteristics using Street View. Our results suggest that virtual surveys in Street View may be suitable for generating some types of street tree data or updating existing data sets more efficiently than field surveys.

[1]  K. Schwarz,et al.  How Environmental Justice Patterns are Shaped by Place: Terrain and Tree Canopy in Cincinnati, Ohio, USA , 2015 .

[2]  Dar A. Roberts,et al.  Mapping urban forest structure and function using hyperspectral imagery and lidar data , 2016 .

[3]  D. Roberts,et al.  Urban tree species mapping using hyperspectral and lidar data fusion , 2014 .

[4]  Jarlath O'Neil-Dunne,et al.  A Versatile, Production-Oriented Approach to High-Resolution Tree-Canopy Mapping in Urban and Suburban Landscapes Using GEOBIA and Data Fusion , 2014, Remote. Sens..

[5]  Ian Hanou,et al.  High-Resolution Remote Sensing Image Analysis for Early Detection and Response Planning for Emerald Ash Borer , 2009 .

[6]  Randolph H. Wynne,et al.  Using Geospatial Tools to Assess the Urban Tree Canopy: Decision Support for Local Governments , 2012 .

[7]  Jayajit Chakraborty,et al.  Street Trees and Equity: Evaluating the Spatial Distribution of an Urban Amenity , 2009 .

[8]  E. Gregory McPherson,et al.  Structure, function and value of street trees in California, USA , 2016 .

[9]  T. Lucke,et al.  A review of benefits and challenges in growing street trees in paved urban environments , 2015 .

[10]  Christian Früh,et al.  Google Street View: Capturing the World at Street Level , 2010, Computer.

[11]  Jean-Pierre Rossi,et al.  Assessing Species Distribution Using Google Street View: A Pilot Study with the Pine Processionary Moth , 2013, PloS one.

[12]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[13]  Weidong Li,et al.  Who lives in greener neighborhoods? The distribution of street greenery and its association with residents' socioeconomic conditions in Hartford, Connecticut, USA , 2015 .

[14]  H. Badland,et al.  Can Virtual Streetscape Audits Reliably Replace Physical Streetscape Audits? , 2010, Journal of Urban Health.

[15]  G. R. Johnson,et al.  Geospatial methods provide timely and comprehensive urban forest information , 2007 .

[16]  D. McKenney,et al.  A street tree survey for Canadian communities: Protocol and early results , 2013 .

[17]  Weixing Zhang,et al.  Assessing street-level urban greenery using Google Street View and a modified green view index , 2015 .

[18]  Jennifer Ailshire,et al.  Using Google Earth to conduct a neighborhood audit: reliability of a virtual audit instrument. , 2010, Health & place.

[19]  M. Hopton,et al.  Comparing street tree assemblages and associated stormwater benefits among communities in metropolitan Cincinnati, Ohio, USA , 2014 .