The process of producing information about dynamic land use and land cover and ecosystem health quickly with high accuracy and low cost is important. This information is one of the basic data used for sustainable land management. For this purpose, remote sensing technologies are generally used, and sampling points are mostly assigned. Determination of the optimum number of sampling points using the I-Tree Canopy tool was the main focus of this study. The I-Tree Canopy tool classifies land cover, revealing the effects of tree cover on ecosystem services, such as carbon (C) sequestration and storage, temperature regulation, air pollutant filtering, and air quality improvement, with numerical data. It is used because it is practical, open source, and user-friendly. This software works based on sampling point assignment, but it is unclear how many sampling points should be assigned. Therefore, determining the optimum number of sample points by statistical methods will increase the effectiveness of this tool and guide users. For this purpose, reference data were created for comparison. Then, 31 I-Tree Canopy reports were created with 100-point increments up to 3100. The data obtained from the reports were compared with the reference data, and statistical analysis based on Gaussian and a second-order polynomial fit was performed. At the end of the analysis, the following results were obtained; the results of this study demonstrated that the optimum number of sample points for a 1-ha area is 760 ± 32 from the comparison of the real area and I-Tree Canopy results. Similar results from the Gaussian fit of annually sequestered and stored C and carbon dioxide (CO2 ) amounts in trees and the reduction in air pollution in grams were obtained as 714 ± 16. Therefore, we may conclude that taking more than 800 sample points will not be statistically significant. Integr Environ Assess Manag 2023;19:726-734. © 2022 SETAC.
Keywords: Gaussian fit; land cover classification; random sample point; remote sensing; tree canopy.
© 2022 SETAC.