Abstract
Assessing the architecture of Norway spruce (Picea abies (L.) Karst.) is challenging using terrestrial remote sensing due to its typically dense, evergreen canopy. On the other hand, this species is among the most vulnerable to the effects of climate change. Advanced crown variables may serve as an early warning of tree drought and wind risk, but are still scarce and hardly available for the vast majority of forests, because they are costly and time-consuming to measure in the field. In this study, we used single-image photogrammetry (SIP) based on a high-resolution smartphone camera. Our method was used to assess the architecture of spruce trees growing under two contrasting forest settings: a low-density urban forest (190–668 trees per ha) and an intermediate-density managed forest (636–1018 trees per ha), outside of Norway spruce native range, in North-Western Poland. For trees ranging 18–50 cm in diameter at breast height (DBH), we obtained a ca. 1 cm (3%) mean error in SIP DBH measurements, with little bias (− 0.3%). The principal component analysis based on the relative tree- and crown-level variables revealed two independent trait dimensions explaining 83.9% of the total variance. The axes were driven by tree slenderness and by crown proportions; the former providing a key to disentangle spruce architectures of the two stands. Overall, our results show that spruce architecture may be quickly and reliably measured with SIP using a smartphone application.
Zusammenfassung
Schätzung der Kronenmetriken individueller Fichten mit einem Smartphone. Die Bewertung der Architektur der Fichte (Picea abies (L.) Karst.) ist aufgrund ihres typischerweise dichten, immergrünen Kronendachs mit terrestrischer Fernerkundung sehr schwierig. Andererseits ist diese Baumart besonders anfällig für Auswirkungen des fortschreitenden Klimawandels. Erweiterte Kronenvariablen können als Frühwarnsystem für Dürre- und Windrisiken dienen, sind aber für die meisten Wälder selten und kaum verfügbar, da ihre Bewertung vor Ort kostspielig und zeitaufwendig ist. In der vorliegenden Studie wird die Einzelbildphotogrammetrie (EBP) mit Hilfe einer hochauflösenden Smartphone-Kamera eingesetzt. Unsere Methode wurde zur Beurteilung der Architektur von Fichten in zwei unterschiedlichen Waldumgebungen eingesetzt: in einem städtischen Wald (190-668 Bäume pro ha) und in einem bewirtschafteten Wald mit mittlerer Walddichte (636-1018 Bäume pro ha), außerhalb des ursprünglichen Verbreitungsgebiets der Fichte (im Nordwesten Polens). Für Bäume mit einem Brusthöhendurchmesser (BHD) von 18 cm bis 50 cm ergab sich ein mittlerer Fehler bei den EBP-BHD-Messungen von ca. 1 cm (3%) mit einem Bias von -0.3%. Die Hauptkomponentenanalyse auf der Grundlage der relativen Variablen der Baum- und Kronenebene ergab zwei unabhängige Merkmalsdimensionen, die 83.9% der Gesamtvarianz erklärten. Die Achsen wurden durch die Schlankheit der Bäume und die Kronenproportionen bestimmt, wobei sichersteres Merkmal als ein Schlüssel für die Entflechtung der Fichtenarchitekturen darstellte. Insgesamt zeigen unsere Ergebnisse, dass die Fichtenarchitektur mit EBP unter Verwendung einer Smartphone-Anwendung schnell und zuverlässig gemessen werden kann, falls manuelle In-situ-Aufzeichnungen gemacht werden können.
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source of the background images (from 2021 for spruce1 and 2020 for spruce2): <https://opendata.geoportal.gov.pl>
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Acknowledgements
This work was influenced by the positive and very motivating comments provided by the three anonymous experts (National Science Centre, Poland), on the project proposal “Comparative analysis of tree architectural traits properties, based on the TRY database and up-to-date remotely sensed data” (MINIATURA4), No. 2020/04/X/NZ9/00960 (KK).
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KK conceived the study; IB was involved in the conceptual design; KK wrote the R code and conducted the field work, data processing, and analyses; KK wrote the first draft; KK and IB contributed to writing of the final manuscript.
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Kędra, K., Barbeito, I. Estimation of Individual Norway Spruce Crown Metrics Using a Smartphone Device. PFG 90, 123–134 (2022). https://doi.org/10.1007/s41064-022-00201-3
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DOI: https://doi.org/10.1007/s41064-022-00201-3