Department of Space Sciences and Technologies, Faculty of Science, Akdeniz University, Antalya, Turkey.
Integr Environ Assess Manag. 2023 May;19(3):726-734. doi: 10.1002/ieam.4704. Epub 2022 Nov 15.
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 (CO ) 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.
快速、准确、低成本地生成有关动态土地利用和土地覆盖以及生态系统健康信息的过程非常重要。这些信息是可持续土地管理的基本数据之一。为此，通常使用遥感技术，并分配采样点。本研究的主要重点是使用 I-Tree 树冠工具确定最佳采样点数量。I-Tree 树冠工具对土地覆盖进行分类，用数字数据揭示树冠对生态系统服务的影响，例如碳 (C) 封存和储存、温度调节、空气污染物过滤和空气质量改善。它被使用是因为它实用、开源且易于使用。该软件基于采样点分配运行，但不清楚应该分配多少个采样点。因此，通过统计方法确定最佳采样点数量将提高该工具的有效性并为用户提供指导。为此，创建了参考数据进行比较。然后，创建了 31 份 I-Tree 树冠报告，每份报告的采样点增加 100 个，直到 3100 个。从报告中获得的数据与参考数据进行了比较，并基于高斯和二阶多项式拟合进行了统计分析。在分析结束时，得到了以下结果：这项研究的结果表明，从实际区域和 I-Tree 树冠结果的比较来看，对于 1 公顷的区域，最佳采样点数量为 760±32。从每年封存和储存的 C 和二氧化碳 (CO ) 量以及空气中的污染物减少量的高斯拟合的相似结果来看，最佳采样点数量为 714±16。因此，我们可以得出结论，超过 800 个采样点在统计上不会有显著意义。综合环境评估与管理 2023；19：726-734。2022 年 SETAC 版权所有。
