DOI: 10.5061/dryad.7m0cfxpxt
title: Feasibility of urban bird and nest amount evaluation by the street view image virtual survey

description: The use of Street View maps in urban bird and nest surveys is worth
 exploring. Using Baidu Street-View (BSV) map, 49338 seamless spherical
 photos of 2741 sites were collected in Qingdao. Study evaluated the
 effectiveness and reproducibility of birds and nests using three
 observation angles and four experimenters. The BSV method and citizen
 science data were compared to evaluate their spatial distribution
 consistency. The BSV time machine is used to assess them time dynamics.
 The environment of the sampling sites with birds or nests was also
 surveyed information and evaluated the optimal environment. BSV photos can
 identify bird and nest, and different people had high repeatability on the
 evaluations. Bird nests selection middle-view BSV images could save 2/3
 time used in the checks with &gt;93% precision. Most citizen
 bird-watching sites overlapped or were close to nest hotspot areas from
 the BSV method and nests were more prevalent. The BSV time machine is
 suitable for investigating bird's nests. Nests and birds are more
 likely to be found in the leafless early spring, on large, traffic-dense
 coastal streets with complex vertical tree structures, and around tall
 buildings. For further urban bird research and conservation, this approach
 provides a pre-experimental and informative supplement.
description: For making a map of birds and nests distribution in
 Qingdao city, we used the grid sampling method for collecting BSV images.
 The grid was generated at a 0.0045° longitude interval and a 0.005°
 latitude interval on the BSV map of Qingdao, i.e., the grid size was about
 500 m * 500 m. There are 2741 suitable sampling sites in total collected
 in this paper. The collection of BSV images was automatically downloaded
 by a web crawler developed using Python (version 2.7) to read the latitude
 and longitude of each site through the BSV image Application Program
 Interface (API) (Xiao et al., 2021). At each sample point, we collected a
 total of 18 pictures in 6 horizontal directions (360° rotated by 60° for
 each viewpoint) and three vertical directions (45° sky-viewing, 0°
 middle-viewing, and -45° ground-viewing). The nest and bird numbers were
 counted by the naked eye of the experimenter. The experimenter needs to
 sift through all BSV images by eye to filter out those with birds or nests
 for counting. All birds in aerial flight, aerial stay, and ground stay
 were counted.  Most of the street maps have been
 updated several times, and the "time machine" allows us to see
 two different times of BSV information at the sampling point in 2016 (the
 first surveying time) and in 2019 (rechecking time for bird and nest
 observation). In the first survey of 2016, we screened the BSV pictures,
 including birds and nests. In 2019, we surveyed the BSV sites of 2016. The
 changes in the number of birds and nests at different times were used as
 characteristics of their temporal changes by data counting and spatial
 distribution. Street view information is also obtained here. The land use
 type in the sampling point was also surveyed in a square of 500m*500m
 around the sampling point as the center in the top-down Baidu Map. The
 "cmd program" was used to segment the 500m*500m Baidu map into
 100 grids (Fig. A1), facilitating the estimate of the proportion of
 different land types in each site. We also use Baidu Map's distance
 tool to measure the distance from each sampling point to the sea and the
 distance to other water surfaces. Citizen science data were
 from the website of the China Birding Records Center
 (http://www.birdreport.cn/home/search/page.html). Citizens' bird data
 upload dates were from December 7, 2014, to March 30, 2021, including
 latitude and longitude coordinates, observation time, bird species, and
 number. There are many bird enthusiasts uploading bird observation
 records. We need to filter the data of
 citizens located in built-up areas.
description: Python Arcgis JMP
 Excel cmd program