Determination of the optimum number of sample points to classify land cover types and estimate the contribution of trees on ecosystem services using the I‐Tree Canopy tool

• Published in: Integrated environmental assessment and management Vol. 19; no. 3; pp. 726 - 734
• Main Authors: Selim, Serdar, Dönmez, Burçin, Kilçik, Ali
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.05.2023
• Subjects: Air Pollution; Air quality; Air temperature; Canopies; Canopy; Carbon dioxide; Ecosystem; Ecosystem services; Ecosystems; Environmental Pollutants; Gaussian fit; Information processing; Land cover; land cover classification; Land management; Land use; Outdoor air quality; Plant cover; Polynomials; Quality control; random sample point; Remote sensing; Remote Sensing Technology; Sampling; Statistical analysis; Statistical methods; Statistics; Storage; Sustainability management; tree canopy; Trees; Gaussian fit; remote sensing; land cover classification; random sample point; tree canopy
• ISSN: 1551-3777; 1551-3793
• DOI: 10.1002/ieam.4704

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 Key Points The random sample point method is frequently preferred in determining the land use and land cover classification and ecosystem services. The number of random sample points affects the accuracy of land cover classification; thus multiple sampling points are assigned. Assigning the sample point number requires a great deal of time and effort, and a certain standard must be provided for this. In determining land use and land cover classifications, including the contribution of trees to ecosystem services, the number of random sampling points reaching a certain standard makes the method used more effective.