Multispektral İHA Görüntüleri Kullanılarak Nesne Tabanlı Görüntü Analizi ile Ağaç Tespiti ve NDVI Tabanlı Bitki Sağlığı Analizi

Öz

Tarımsal alanların verimli bir şekilde yönetimi ve doğal kaynakların korunması, modern çevre biliminin en önemli hedeflerinden biridir. Bu hedeflere ulaşmak için uzaktan algılama teknolojileri, özellikle İnsansız Hava Aracı (İHA) tabanlı görüntüleme sistemleri, güçlü araçlar sunmaktadır. Geleneksel yöntemlerin sınırlılıklarını aşarak, İHA görüntüleri yüksek çözünürlükte veri sağlaması ve geniş alanları kapsayabilmesi nedeniyle bitki sağlığı ve ağaç tespiti çalışmalarında önemli bir yer edinmiştir. Özellikle Normalize Edilmiş Fark Bitki Örtüsü İndeksi (Normalized Difference Vegetation Index/NDVI) gibi spektral analiz yöntemleri, bitki örtüsünün sağlık durumunu değerlendirmek ve stres altındaki bölgeleri belirlemek için etkili bir çözüm sunmaktadır. Bu çalışmada, İHA görüntüleri ve nesne tabanlı görüntü analizi yöntemleri kullanılarak tarımsal alanlardaki ağaçların tespiti ve bitki sağlığının değerlendirilmesi amaçlanmıştır. Multispektral görüntülerden elde edilen NDVI değerleri, segmentasyon ve sınıflandırma süreçleri ile birleştirilerek ağaçların etkili bir şekilde tespit edilmesi sağlanmıştır. Doğruluk analizleri, modelin genel performansını ortaya koymuş ve segmentasyon süreçlerinin başarı oranını doğrulamıştır. Model, %89 doğruluk ve %92 kesinlik oranıyla etkili bir sınıflandırma gerçekleştirmiştir. Ayrıca, NDVI analizleri ile sağlıklı, orta sağlıkta ve stres altındaki bitkiler ayrıştırılmış, çalışma alanındaki mekânsal farklılıklar detaylı bir şekilde haritalandırılmıştır. Sonuç olarak, bu çalışma, İHA tabanlı görüntüleme ve Nesne Tabanlı Görüntü Analizi (Object-Based Image Analysis/OBIA) yöntemlerinin tarımsal yönetim ve çevresel izleme uygulamaları için güçlü bir çözüm sunduğunu göstermiştir. Bu yöntemlerin doğruluğu ve uygulama kolaylığı, gelecekte farklı coğrafyalarda ve daha geniş alanlarda yapılacak çalışmalara ışık tutmaktadır.

Anahtar Kelimeler

• Tarımsal yönetim
• multispektral görüntüleme
• nesne tabanlı görüntü analizi
• ağaç tespiti
• bitki sağlığı

Tree Detection and NDVI-Based Plant Health Assessment Using Object-Based Image Analysis on Multispectral UAV Imagery

Abstract

Efficient management of agricultural lands and conservation of natural resources are critical objectives in modern environmental science. Remote sensing technologies, particularly Unmanned Aerial Vehicle (UAV)-based imaging systems, provide powerful tools to achieve these goals. UAV imagery has become increasingly valuable in plant health assessment and tree detection due to its high-resolution data and ability to cover extensive areas, overcoming many limitations of traditional methods. Spectral analysis techniques, such as the Normalized Difference Vegetation Index (NDVI), offer effective means for evaluating vegetation health and identifying stressed regions. In this study, we employed UAV imagery combined with Object-Based Image Analysis (OBIA) to detect trees and assess plant health in agricultural fields. NDVI values derived from multispectral images were integrated with segmentation and classification workflows to accurately identify individual trees. Accuracy assessments demonstrated robust model performance, with classification accuracy reaching 89% and precision at 92%. Furthermore, NDVI analyses enabled the differentiation of healthy, moderately healthy, and stressed vegetation, allowing detailed spatial mapping of plant health across the study area. Overall, our results indicate that UAV-based imaging and OBIA represent a powerful approach for agricultural management and environmental monitoring. The demonstrated accuracy and operational ease of these methods provide a solid foundation for future applications across diverse geographic regions and larger scales.

Keywords

• Agricultural management
• multispectral imaging
• object oriented image analysis
• tree detection
• plant health

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