Accuracy comparison of remote sensing methods (UAV, LiDAR, Radar) and field allometry in mangrove blue carbon quantification: A systematic literature review

Ludwick Satria Romadoni (Department of Biology Education, Faculty of Teacher Training and Education, Universitas Muhammadiyah Malang, Indonesia)
H. Husamah (Scopus ID: 57195803428 Department of Biology Education, Universitas Muhammadiyah Malang, East Java Province)
Bello Ganiyu (Department of Science Education, Faculty of Education, University of Ilorin, Nigeria)

Green and Tropical Laboratory for Sustainability
Vol. 2 No. 2 (2025): December

Background: Mangrove ecosystems are crucial blue carbon reservoirs, yet quantifying their carbon stocks faces methodological challenges. Field allometric methods, while accurate, are destructive and labor-intensive. Non-destructive Remote Sensing (RS) (UAV, LiDAR, Radar) offers an efficient alternative for landscape-scale mapping. However, literature comparing the accuracy and bias of these diverse RS sensors remains fragmented. Objectives: This study aims to (1) synthesize and compare accuracy metrics  & RMSE from various RS methods in estimating mangrove biomass and carbon stocks, and (2) identify factors in forest structure and sensor types that most significantly influence estimation bias Methods: This research employs a Systematic Literature Review (SLR) guided by PRISMA. Data were extracted from 10 core empirical articles (2018-2025) covering various platforms from optical satellites to UAV-LiDAR. Results: The SLR results indicate that high-cost technologies like LiDAR do not always guarantee the highest accuracy compared to optical sensors (Sentinel-2) under certain conditions. The primary identified bias factor is signal saturation in dense canopies. Conclusion: These findings provide evidence-based guidance for researchers and biology educators in selecting the most robust methodology for carbon inventory.