EventsInternational Conference on Advanced Remote Sensing (ICARS 2025)
Published
This submission belongs to the session S7. Remote Sensing for Forests and Carbon of the event International Conference on Advanced Remote Sensing (ICARS 2025)
Published date
25 Mar, 2025
Academic Editor
author-avatarFabio Tosti
Citation
Víctor Cicuéndez, Mateo Pastrana, Cristina Velilla, Carmen Marín, Alfonso Gómez, Comparison of two LiDAR techniques for estimating Above-Ground Biomass in a tropical forest of Costa Rica, in Proceedings of International Conference on Advanced Remote Sensing (ICARS 2025), Barcelona, 26 March–28 March 2025, MDPI: Basel, Switzerland
Share
Email
Facebook
Twitter
LinkedIn

Comparison of two LiDAR techniques for estimating Above-Ground Biomass in a tropical forest of Costa Rica

1. Stereocarto SL, Carretera de Canillas 134, 28043, Madrid, Spain., Spain
2. Departamento de Ingeniería Agroforestal, ETSIAAB, Universidad Politécnica de Madrid (UPM), Avda. Puerta de Hierro 2-4, 28040 Madrid, Spain., Spain
3. Stereocarto SL, Carretera de Canillas 134, 28043, Madrid, Spain.
Abstract

In the last few decades, the role of forests as carbon sinks has become a fundamental scientific issue due to their potential effect on climate change. Tropical forests represent one half of Earth’s carbon stored in terrestrial vegetation. Therefore, estimating the above-ground biomass and carbon of these forests through new technologies is crucial for adopting different strategies that promote sustainable management. In recent years, the LiDAR technique (Laser Imaging Detection and Ranging) has emerged as an important tool to estimate forest biomass accurately, especially in tropical forests where vegetation is dense and the acquisition of field data is a difficult task. The main objective of this work is to compare two technologies of LiDAR, the full-wave LiDAR (LiDARfw) and discrete LiDAR (LiDARd), for estimating biomass in a tropical forest of Costa Rica. The results showed that LiDARfw provided a higher point density (+14.5%) and captured greater vertical structure variability than LiDARd, particularly in lower forest strata. This demonstrates its effectiveness in modeling complex forest environments. In conclusion, LiDARfw excels in capturing detailed vertical profiles and identifying structural heterogeneity, making it ideal for biomass estimation and precise ecological studies.

Keywords
full-wave LiDAR
discrete LiDAR
forest structure
forest strata
Carbon-storage capacity of Woody crops in South Spain: The AGROLiDAR project
Evidence prediction of atmospheric Infrared Thermal Anomaly before Hunga-Tonga volcanic eruption