摘要
Measuring the amount of vegetation in a given area on a large scale has long been accomplished using satellite and aerial imaging systems.These methods have been very reliable in measuring vegetation coverage accurately at the top of the canopy,but their capabilities are limited when it comes to identifying green vegetation located beneath the canopy cover.Measuring the amount of urban and suburban vegetation along a street network that is partially beneath the canopy has recently been introduced with the use of Google Street View(GSV)images,made accessible by the Google Street View Image API.Analyzing green vegetation through the use of GSV images can provide a comprehensive representation of the amount of green vegetation found within geographical regions of higher population density,and it facilitates an analysis performed at the street-level.In this paper we propose a fine-tuned color based image filtering and segmentation technique and we use it to define and map an urban green environment index.We deployed this image processing method and,using GSV images as a high-resolution GIS data source,we computed and mapped the green index of Milwaukee County,a 3,082 km^(2) urban/suburban county in Wisconsin.This approach generates a high-resolution street-level vegetation estimate that may prove valuable in urban planning and management,as well as for researchers investigating the correlation between environmental factors and human health outcomes.
基金
This work was supported by the National Science Foundation [DUE-1129056]
This research was completed under the University of Wisconsin-Milwaukee’s Undergraduate Research in Biology and Mathematics(UBM)Program and was supported by a grant from the National Science Foundation DUE-1129056.Additional support was provided from the University of Wisconsin-Milwaukee’s Support For Undergraduate Research Fellowship(SURF),issued by UW-Milwaukee’s Office of Undergraduate Research.The authors of this paper would like to thank Prof.Gabriella Pinter,Prof.Erica Young and Prof.John Berges for their invaluable support.Finally,the authors would like recognize Google LLC for its publicly available image resource and street view API,without which this investigation would not have been possible.
