Single tree-level aboveground biomass estimation using mobile LİDAR in scots pine (Pinus sylvestris L.) stands

Abstract

Forest ecosystems play a pivotal role in balancing the global carbon cycle by storing a substantial proportion of terrestrial organic carbon stocks; therefore, aboveground biomass (AGB) measurements are of critical importance for climate policy development and sustainable forest management. This study assesses the accuracy of AGB estimates derived from GeoSLAM ZEB-Horizon mobile LiDAR (Light Detection and Ranging) system data in pure Scots pine (Pinus sylvestris L.) stands in the Bolu region, through a comparative evaluation with terrestrial inventory measurements. Across 23 sample plots, a total of 443 trees were measured. Terrestrial diameter at breast height data were applied to the allometric models of Çömez (2010) and Muukkonen (2007) to obtain reference AGB values. Mobile LiDAR point clouds underwent pre-processing, including ground classification, height normalization, and CSP (Comparative Shortest Path) based segmentation, to isolate individual tree stems. During the mobile LiDAR survey, individual tree matching was performed by utilizing the temporal trajectory information based on the tree numbering sequence, enabling successful matching between field-measured trees and trees in the point cloud. For each tree, LiDAR-based DBH was extracted through a circle-fitting procedure at 1.30 m height, and AGB were generated using the same allometric equations. The performance indicators of the mobile LiDAR-based estimates were notably high (Çömez: MAE: 4.20 kg, RMSE: 48.72 kg, R²: 0.9930; Muukkonen: MAE: 2.79 kg, RMSE: 31.75 kg, R²: 0.9931), indicating that in homogeneous Scots pine stands, mobile LiDAR achieves accuracy comparable to that of terrestrial inventory while providing significant time efficiency. The study recommends the development of national protocol standards for operationalizing mobile LiDAR applications, integration of multi-sensor fusion approaches, comprehensive validation across diverse forest types, and enhanced operator training.

Keywords

• LiDAR
• Point cloud
• Forest inventory
• SLAM
• AGB

Project Number

222O034

Mobil LiDAR ile sarıçam (Pinus sylvestris L.) meşcerelerinde tek ağaç düzeyinde toprak üstü biyokütle tahmini

Abstract

Orman ekosistemleri, karasal organik karbon stoklarının büyük bölümünü barındırarak küresel karbon döngüsünün dengelenmesinde önemli bir rol oynar; bu nedenle toprak üstü biyokütle ölçümleri iklim politikaları ve sürdürülebilir orman yönetimi için kritik öneme sahiptir. Bu çalışma, Bolu yöresindeki saf sarıçam (Pinus sylvestris L.) meşcerelerinde GeoSLAM ZEB-Horizon mobil LiDAR (Light Detection and Ranging) sistemi verilerinin toprak üstü biyokütle tahminlerindeki doğruluğunu yersel envanterle karşılaştırmalı olarak değerlendirmektedir. 23 adet örnek alanda toplam 443 ağaç ölçülmüş; yersel göğüs çapı verileri Çömez (2010) ve Muukkonen (2007) allometrik modellerine uygulanarak referans toprak üstü biyokütle değerleri elde edilmiştir. Mobil LiDAR nokta bulutları ön işlemden geçirilmiş, zemin sınıflaması, yüksekliğe göre normalizasyon ve CSP (Comparative Shortest Path) tabanlı segmentasyon uygulanarak bireysel ağaç gövdeleri ayrıştırılmıştır. Mobil LiDAR taraması sırasında ağaç numaralandırma sırasına dayalı zamansal rota bilgisinden yararlanılarak tek ağaç eşleştirmesi yapılmış ve böylece arazide ölçülen ağaçlar ile nokta bulutu üzerindeki ağaçlar başarıyla eşleştirilmiştir. Her ağaca ilişkin 1.30 m yükseklikte daire uydurma (circle-fitting) işlemi ile LiDAR-tabanlı göğüs çapları çıkarılmış ve aynı allometrik denklemler kullanılarak toprak üstü biyokütle tahminleri oluşturulmuştur. Mobil LiDAR tabanlı tahminlerin performans göstergeleri oldukça yüksek olup (Çömez: Ortalama Mutlak Hata: 4.20 kg, Kök Ortalama Kare Hatası: 48.72 kg, R²: 0.9930; Muukkonen: Ortalama Mutlak Hata: 2.79 kg, Kök Ortalama Kare Hatası: 31.75 kg, R²: 0.9931), homojen sarıçam meşcerelerinde mobil LiDAR’ın yersel envantere eşdeğer doğruluk ve belirgin zaman-verimliliği sağladığı sonucuna varılmıştır. Çalışma, mobil LiDAR uygulamalarının operasyonelleştirilmesi için ulusal protokol standartlarının oluşturulmasını, çoklu sensör füzyonunu, farklı orman tiplerinde kapsamlı doğrulama çalışmalarını ve operatör eğitiminin güçlendirilmesini önermektedir.

Keywords

• LiDAR
• Nokta bulutu
• Orman envanteri
• SLAM
• TÜB

Supporting Institution

TÜBİTAK

Project Number

222O034

Thanks

Bu çalışma, Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) 1001 – Bilimsel ve Teknolojik Araştırma Projelerini Destekleme Programı kapsamında 222O034 numaralı proje ile desteklenmiştir. Proje kapsamında elde edilen verilerin bir kısmı kullanılmıştır.

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