İnsansız Hava Araçları ile Ağaç Metriklerinin Tahmini ve Aralarındaki İlişkilerin Belirlenmesi

Abstract

Günümüzde, İnsansız Hava Araçları (İHA) çeşitli inovatif sektörlerde geniş kullanım alanları bulmuştur. Ormancılık bağlamında bu sistemler; ağaç boyu, göğüs yüksekliğindeki çap (DBH) ve taç boyutları gibi temel metriklerin belirlenmesinde kullanılmaktadır. Bu araştırma, Pinus nigra (karaçam) türü için özel olarak tasarlanmış, bireysel ağaç boylarını tahmin etmeye ve bunların DBH ile olan matematiksel ilişkisini kurmaya odaklanan yapılandırılmış bir iş akışı geliştirmektedir. Önerilen modelin sağlamlığını (robustness) doğrulamak amacıyla, İHA tabanlı veri toplama süreci 394 kapsamlı arazi ölçümü ile desteklenmiştir. Elde edilen denklem, ek bir manuel arazi çalışmasına gerek kalmaksızın hem ağaç boyunun hem de DBH değerinin türetilmesine olanak tanımaktadır. Doğrulama sonuçları, boy için ±70 cm, DBH için ise ±1.5 cm ortalama hata göstermiş olup; bu değerler sırasıyla ortalama değerlerin yaklaşık %8.2 ve %7.9'una tekabül etmektedir. Bu metodoloji, doğru orman envanterleri oluşturmak için maliyet ve zaman açısından verimli bir alternatif sunmakta, yöntemin doğruluğunu vurgulamakta ve daha pahalı yöntemlerle olumlu yönde rekabet etmektedir.

Keywords

• İnsansız Hava Araçları
• Fotogrametri
• Orman Envanteri

Estimating Tree Metrics and Relationships Between Them with Unmanned Aerial Vehicles

Abstract

Nowadays, Unmanned Aerial Vehicles (UAVs) have found extensive applications across various innovative sectors. Within the context of forestry, these systems are utilized to determine key metrics such as tree height, diameter at breast height (DBH), and crown dimensions. This research develops a structured workflow specifically designed for Pinus nigra, focusing on estimating individual tree heights and establishing their mathematical relationship with DBH. To ensure the robustness of the proposed model, UAV-based data collection was complemented by 394 comprehensive field measurements. The resulting equation allows for the derivation of both height and DBH without further manual field labor. Validation results showed a mean error of ±70 cm for height and ±1.5 cm for DBH, representing approximately 8.2% and 7.9% of the respective mean values. This methodology offers a cost-effective and time-efficient alternative for developing accurate forest inventories and highlights the accuracy of this method and compares favorably to more expensive methods.

Keywords

• Unmanned Aerial Vehicles
• Photogrammetry
• Forest Information Systems

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