Chin J Plant Ecol ›› 2020, Vol. 44 ›› Issue (3): 205-213.DOI: 10.17521/cjpe.2019.0236

• Research Articles • Previous Articles     Next Articles

Pedestrian-view urban street vegetation monitoring using Baidu Street View images

FENG Si-Yuan1,WEI Ya-Nan1,WANG Zhen-Juan1,YU Xin-Yang1,2,3,*()   

  1. 1Department of Resources and Environment, Shandong Agricultural University, Tai’an, Shandong 271018, China
    2Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    3Tropical Research and Education Center, University of Florida, Homestead Florida, 33031, USA
  • Received:2019-09-05 Accepted:2020-01-27 Online:2020-03-20 Published:2020-04-30
  • Contact: Xin-Yang YU
  • Supported by:
    National Natural Science Foundation of China(41877003);“Twelfth Five-Year” National Science and Technology Support Plan Project(2015BAD23B0202)

Abstract:

Aims The distribution pattern of green vegetation in urban streets has significant impacts on urban ecological environment and physical/mental health of local residents. Accurate detecting and monitoring of street green information is of great significance for precise urban planning and management, while there are few studies focusing on urban greenery estimation using profile image system.
Methods In this study, combining network information capturing and geospatial information analysis technology, Taiʼan city was selected as the case study area. Based on the Baidu application programming interface (API), a total of 3 276 Baidu Street View (BSV) images of 273 research samples were obtained and processed, and the green vegetation pixels in the image were extracted by computer supervised classification and compared with the artificial extraction results. Based on the proposed Baidu Street Vegetation Distribution Index (BSVDI), we monitored the street vegetation’s distribution characteristics from the pedestrian perspective, and analyzed the street- scale vegetation distribution pattern.
Important findings The BSV image could be used as the main data source to monitor the distribution of green trees and lawns in pedestrian’s perspective on the street scale. BSVDI was higher in the center, northeast and southeast of the study area than the other regions. BSVDI and remote sensing extracted vegetation covered area are significantly and positively correlated, with correlation coefficient of 0.76, 0.63 and 0.49 in the buffered distance of 10, 20 and 50 m, respectively. However, the change trends of the BSVDI and remote sensing results were not completely consistent in each study site. This study implies that the combination of BSVDI and remote sensing monitoring results can better guide urban green landscape planning and precise management.

Key words: street view image, street greenery index, profile view, vegetation pattern, supervised classification