Volume 13, Issue 1 (3-2025)
Ecol Iran For 2025, 13(1): 120-132 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Taheri Sarteshnizi M J, Fallah A, Ramezani Moziraji H, Mohammadi J. (2025). Designing the HCP Software for Measuring Tree Height and Canopy Diameter Simultaneously in Southern Zagros. Ecol Iran For. 13(1), 120-132. doi:10.61186/ifej.2024.524
URL: http://ifej.sanru.ac.ir/article-1-524-en.html
URL: http://ifej.sanru.ac.ir/article-1-524-en.html
Mohammad Javad Taheri Sarteshnizi *1 
, Asghar Fallah2, Habibollah Ramezani Moziraji3, Jahangir Mohammadi4
, Asghar Fallah2, Habibollah Ramezani Moziraji3, Jahangir Mohammadi4
1- Department of Forestry, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
2- Department of Forestry, Faculty of Natural Resources, University of Agricultural Sciences and Natural Resources, Sari, Iran
3- Swedish University of Agriculture and Natural Resources, Umeå, Sweden
4- Department of Forestry, Faculty of Forest Science, Gorgan University of Agricultural Sciences and Natural Resources, Sari, Iran
2- Department of Forestry, Faculty of Natural Resources, University of Agricultural Sciences and Natural Resources, Sari, Iran
3- Swedish University of Agriculture and Natural Resources, Umeå, Sweden
4- Department of Forestry, Faculty of Forest Science, Gorgan University of Agricultural Sciences and Natural Resources, Sari, Iran
Abstract: (803 Views)
Extended Abstract
Background: The height of trees is among the important components in the field of forest inventory and measurement. Over successive decades, estimating tree height has been a major concern in forest statistics and measurement. The height variable is used in estimating tree volume, classifying stand productivity, and estimating growth rates of trees. It is also one of the important variables in calculating biomass. Another essential characteristic at the individual tree level is canopy diameter, which plays a role in modeling and practical attribute estimation, such as forest canopy cover percentage, leaf area index, and biomass. Until now, the devices and tools introduced in forestry science have only been able to measure a specific component, e.g. tree height, and the need for different tools or techniques arises for measuring canopy diameter. The approach of this research not only leads to the development and use of software capable of measuring the mentioned components but also enables the recording of inventory in designed forms, recording of geographic coordinates of tree bases, storing images taken from the trees, and ultimately creating a data archive from the study area. To achieve these goals, the design and development of a software (application) called HCP, which stands for H: Height, C: Canopy, and P: Pixel, based on the Android operating system, will be considered the essence and necessity of this research.
Methods: In this research, the HCP software based on the Android platform was designed and developed according to the devices with the Android operating system and with the Java language. HCP has three parts: database, image processing, and information display. Its user interface was presented in six activity pages, including Set Image, Set Data, Set View, Set Reference, Set Pin, and Set Final, with a SQL database structure. The entire coding, execution, debugging, and testing process was carried out based on API 27 (Application Program Interface) associated with the Android SDK (Software Development Kit), in the Android Studio development environment (IntelliJ). The initial testing process was also carried out with real images stored or produced by the device camera used. A total of 150 trees from Persian Oak species (Quercus brantii Lindl) were selected and their height and canopy diameter were measured to conduct tests and compare the measured height data using Vertex and HCP methods, as well as the measured canopy diameter data using laser meter and HCP. Descriptive indices independent of the distribution type, including the first quartile, median, third quartile, minimum, and maximum, were calculated for the height and canopy diameter components measured by the four methods mentioned above. The Shapiro-Wilk test was used to examine the distribution of the difference in the height and canopy diameter of all trees. Paired t-test was used to evaluate the accuracy of HCP. The HCP error was evaluated by the root mean square error (RMSE) and the Index of Disagreement, which is the result of dividing the absolute value of the difference of the estimated component from the actual value by the actual value. The accuracy of HCP was evaluated using linear correlation and the Index of Agreement. To further investigate the dispersion of the error distribution and to examine the accuracy and precision, the data were grouped into three levels: total trees, 75 tallest trees in terms of height (wider in terms of canopy diameter), and 75 smallest trees in terms of height (canopy diameter). All results were obtained using the R statistical software.
Results: The result of the paired t-test at a 95% confidence level indicates no significant difference between the measured heights using the Vertex and HCP methods, as well as no significant differences between the canopy diameter measurements using the laser meter and HCP. The RMSE values were calculated as 0.07 and 0.065 meters for the height and canopy diameter, respectively. The values of the linear correlation coefficient and the index of agreement were reported as 0.999 for both the height and canopy diameter measured with the mentioned methods. The disagreement percentages were calculated as 1.068 and 0.963 for the height and canopy diameter, respectively.
Conclusion: Modern methods of measuring tree height need to be improved before they can be widely used in forests, and once the shortcomings are addressed, a new chapter will open in measuring forest components, such as the canopy diameter and tree height. The following seven steps outline the general steps for measuring canopy diameter and tree height by using HCP photography. Finally, based on the examination of accuracy, precision, and error assessment test results, HCP can be introduced as an alternative method for measuring the height and canopy diameter of trees. Step 1: Design and develop HCP (other items include defining Java class file functions to determine tree number, recording time and date of photography, calculating the time difference between images, spatial positioning, copying the original image file to internal memory, resizing images for re-matching, and providing output in csv and zip format). Step 2: Taking pictures of the target tree (neighboring trees) and index. Step 3: Defining and determining the numerical value of the index used in the image. Step 4: Determining the lower points (tree trunk and index), upper points (tree tip and index), and the canopy diameter on the image. Step 5: Correcting all measured components, and Step 6: Starting and performing processing by HCP. Step 7: Automatically measuring all defined components for the target tree (neighboring trees), generating output file, and saving all information related to each image.
Background: The height of trees is among the important components in the field of forest inventory and measurement. Over successive decades, estimating tree height has been a major concern in forest statistics and measurement. The height variable is used in estimating tree volume, classifying stand productivity, and estimating growth rates of trees. It is also one of the important variables in calculating biomass. Another essential characteristic at the individual tree level is canopy diameter, which plays a role in modeling and practical attribute estimation, such as forest canopy cover percentage, leaf area index, and biomass. Until now, the devices and tools introduced in forestry science have only been able to measure a specific component, e.g. tree height, and the need for different tools or techniques arises for measuring canopy diameter. The approach of this research not only leads to the development and use of software capable of measuring the mentioned components but also enables the recording of inventory in designed forms, recording of geographic coordinates of tree bases, storing images taken from the trees, and ultimately creating a data archive from the study area. To achieve these goals, the design and development of a software (application) called HCP, which stands for H: Height, C: Canopy, and P: Pixel, based on the Android operating system, will be considered the essence and necessity of this research.
Methods: In this research, the HCP software based on the Android platform was designed and developed according to the devices with the Android operating system and with the Java language. HCP has three parts: database, image processing, and information display. Its user interface was presented in six activity pages, including Set Image, Set Data, Set View, Set Reference, Set Pin, and Set Final, with a SQL database structure. The entire coding, execution, debugging, and testing process was carried out based on API 27 (Application Program Interface) associated with the Android SDK (Software Development Kit), in the Android Studio development environment (IntelliJ). The initial testing process was also carried out with real images stored or produced by the device camera used. A total of 150 trees from Persian Oak species (Quercus brantii Lindl) were selected and their height and canopy diameter were measured to conduct tests and compare the measured height data using Vertex and HCP methods, as well as the measured canopy diameter data using laser meter and HCP. Descriptive indices independent of the distribution type, including the first quartile, median, third quartile, minimum, and maximum, were calculated for the height and canopy diameter components measured by the four methods mentioned above. The Shapiro-Wilk test was used to examine the distribution of the difference in the height and canopy diameter of all trees. Paired t-test was used to evaluate the accuracy of HCP. The HCP error was evaluated by the root mean square error (RMSE) and the Index of Disagreement, which is the result of dividing the absolute value of the difference of the estimated component from the actual value by the actual value. The accuracy of HCP was evaluated using linear correlation and the Index of Agreement. To further investigate the dispersion of the error distribution and to examine the accuracy and precision, the data were grouped into three levels: total trees, 75 tallest trees in terms of height (wider in terms of canopy diameter), and 75 smallest trees in terms of height (canopy diameter). All results were obtained using the R statistical software.
Results: The result of the paired t-test at a 95% confidence level indicates no significant difference between the measured heights using the Vertex and HCP methods, as well as no significant differences between the canopy diameter measurements using the laser meter and HCP. The RMSE values were calculated as 0.07 and 0.065 meters for the height and canopy diameter, respectively. The values of the linear correlation coefficient and the index of agreement were reported as 0.999 for both the height and canopy diameter measured with the mentioned methods. The disagreement percentages were calculated as 1.068 and 0.963 for the height and canopy diameter, respectively.
Conclusion: Modern methods of measuring tree height need to be improved before they can be widely used in forests, and once the shortcomings are addressed, a new chapter will open in measuring forest components, such as the canopy diameter and tree height. The following seven steps outline the general steps for measuring canopy diameter and tree height by using HCP photography. Finally, based on the examination of accuracy, precision, and error assessment test results, HCP can be introduced as an alternative method for measuring the height and canopy diameter of trees. Step 1: Design and develop HCP (other items include defining Java class file functions to determine tree number, recording time and date of photography, calculating the time difference between images, spatial positioning, copying the original image file to internal memory, resizing images for re-matching, and providing output in csv and zip format). Step 2: Taking pictures of the target tree (neighboring trees) and index. Step 3: Defining and determining the numerical value of the index used in the image. Step 4: Determining the lower points (tree trunk and index), upper points (tree tip and index), and the canopy diameter on the image. Step 5: Correcting all measured components, and Step 6: Starting and performing processing by HCP. Step 7: Automatically measuring all defined components for the target tree (neighboring trees), generating output file, and saving all information related to each image.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
