Guiding Field Measurement of Pine Tree Crowns: A Geometric Shape Comparison Using Drone Imagery

Abstract. This paper examines various geometric shapes to determine the most suitable one for calculating tree crown area in field measurements. The study was conducted in an Eldarica pine plantation forest, which was digitally mapped using RGB images captured by a Phantom 4 Pro drone. Tree crowns were manually digitized (MD) from these images to serve as reference data. Field measurements, including the large and small crown diameters, were collected to evaluate crown areas derived from different geometric shapes. The geometric shapes considered were: Oval with Both Diameters (OBD), Circle with Small Diameter (CSD), Circle with Large Diameter (CLD), and Circle with Mean Diameters (CMD). Three analyses were performed to assess the results: correlation analysis (R2), relative root mean square error (RRMSE), and shape analysis, which included overestimation (OverID) and underestimation (UnderID) indices. The results revealed that the choice of geometric shape significantly impacts the accuracy of crown area calculations. The OBD model based on the outer boundary diameter yielded the best results with RRMSE = 0.29, R2 = 0.84, OverID = 0.18, and UnderID = 0.23, followed closely by the CMD method. In contrast, the CSD and CLD models performed less effectively, with RRMSE = 0.52, R2 = 0.42, OverID = 0.11, UnderID = 0.35 (CSD), and RRMSE = 0.59, R2 = 0.37, OverID = 0.46, UnderID = 0.22 (CLD). These differences in performance are likely due to the inclusion of empty spaces within the crown area in some models. However, the findings of this study are not universally applicable to all tree crown area calculations. The geometric shape used for crown area estimation must align with the structural characteristics of the forest and the specific geometry of the tree species under consideration.

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Publication Details

Journal
˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
Published
2025-05-19
DOI
https://doi.org/10.5194/isprs-archives-xlviii-m-6-2025-133-2025
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
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article

Guiding Field Measurement of Pine Tree Crowns: A Geometric Shape Comparison Using Drone Imagery

Virginia García Millán, Saied Pirasteh, Ali Hosingholizade, Yousef Erfanifard et al.
1 links
˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
Remote Sensing and LiDAR Applications
article

Guiding Field Measurement of Pine Tree Crowns: A Geometric Shape Comparison Using Drone Imagery

Virginia García Millán, Saied Pirasteh, Ali Hosingholizade, Yousef Erfanifard, Seyed Kazem Alavipanah
article en

Abstract

Abstract. This paper examines various geometric shapes to determine the most suitable one for calculating tree crown area in field measurements. The study was conducted in an Eldarica pine plantation forest, which was digitally mapped using RGB images captured by a Phantom 4 Pro drone. Tree crowns were manually digitized (MD) from these images to serve as reference data. Field measurements, including the large and small crown diameters, were collected to evaluate crown areas derived from different geometric shapes. The geometric shapes considered were: Oval with Both Diameters (OBD), Circle with Small Diameter (CSD), Circle with Large Diameter (CLD), and Circle with Mean Diameters (CMD). Three analyses were performed to assess the results: correlation analysis (R2), relative root mean square error (RRMSE), and shape analysis, which included overestimation (OverID) and underestimation (UnderID) indices. The results revealed that the choice of geometric shape significantly impacts the accuracy of crown area calculations. The OBD model based on the outer boundary diameter yielded the best results with RRMSE = 0.29, R2 = 0.84, OverID = 0.18, and UnderID = 0.23, followed closely by the CMD method. In contrast, the CSD and CLD models performed less effectively, with RRMSE = 0.52, R2 = 0.42, OverID = 0.11, UnderID = 0.35 (CSD), and RRMSE = 0.59, R2 = 0.37, OverID = 0.46, UnderID = 0.22 (CLD). These differences in performance are likely due to the inclusion of empty spaces within the crown area in some models. However, the findings of this study are not universally applicable to all tree crown area calculations. The geometric shape used for crown area estimation must align with the structural characteristics of the forest and the specific geometry of the tree species under consideration.

˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesVol. XLVIII-M-6-2025
Shaoxing University (CN), University of Tehran (IR), Saveetha University (IN)
Openalex Percentile: Top 91%
Remote Sensing and LiDAR Applications
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