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Effects of sampling approaches on quantifying urban forest structure
Landscape and Urban Planning ( IF 9.1 ) Pub Date : 2020-03-01 , DOI: 10.1016/j.landurbplan.2019.103722 Jing Jin , Jun Yang
Landscape and Urban Planning ( IF 9.1 ) Pub Date : 2020-03-01 , DOI: 10.1016/j.landurbplan.2019.103722 Jing Jin , Jun Yang
Abstract Researchers and practitioners obtain information on urban forest structure mainly through sampling a small percentage of the total population of trees. Nevertheless, people seldom examine the suitability of sampling designs for this task. In this study, we evaluated the performance of simple random sampling (SRS), stratified random sampling (STR), systematic sampling (SS), and generalized random-tessellation stratified sampling (GRTS) for obtaining information on urban forests in Philadelphia, United States and Beijing, China. We determined the spatial locations of all urban trees in the two cities from high-resolution aerial images. We ran simulated surveys on the data set using the four sampling designs with varied sample sizes to estimate structural variables of urban forests, including the total number of trees, species richness, and species abundance. We conducted accuracy assessments by comparing the estimates to the real values. In the end, we located 1,381,903 and 2,295,794 urban trees in Philadelphia and Beijing, respectively. The results of simulated surveys showed that SRS had a more stable performance than other sampling designs when used for estimating the total number of trees. STR and GRTS performed better than SRS and SS in estimating species richness and detecting rare species. For estimating species abundance, SRS again displayed a stable performance among all designs. Also, we found that small sample sizes (≤200) used in existing studies would lead to estimates of urban forest structure with low accuracy. Our findings provide useful guidance to researchers and practitioners on sampling urban forests.
更新日期：2020-03-01
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