Reliable tree height estimation in agroforestry and reforestation system using Airborne Laser Scanning (ALS)

Tiodionwa Abdoulaye Ouattara ,N’zi Daniel Koua ,Nina Gueulou ,Yao Henri-Michel Ella ,Patrick Muziga ,Kouamé Kissi Patrice Adayé

doi:10.24294/sf11725

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Reliable tree height estimation in agroforestry and reforestation system using Airborne Laser Scanning (ALS)

Tiodionwa Abdoulaye Ouattara

N’zi Daniel Koua

Nina Gueulou

Yao Henri-Michel Ella

Patrick Muziga

Kouamé Kissi Patrice Adayé

Sustainable Forestry 2025, 8(1); https://doi.org/10.24294/sf11725

Submitted：30 Apr 2025

Accepted：14 May 2025

Published：13 Jun 2025

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Abstract

In Côte d’Ivoire, the government and its development partners have implemented a national strategy to promote agroforestry and reforestation systems as a means to combat deforestation, primarily driven by agricultural expansion, and to increase national forest cover to 20% by 2045. However, the assessment of these systems through traditional field-based methods remains labor-intensive and time-consuming, particularly for the measurement of dendrometric parameters such as tree height. This study introduces a remote sensing approach combining drone-based Airborne Laser Scanning (ALS) with ground-based measurements to enhance the efficiency and accuracy of tree height estimation in agroforestry and reforestation contexts. The methodology involved two main stages: first, the collection of floristic and dendrometric data, including tree height measured with a laser rangefinder, across eight (8) agroforestry and reforestation plots; second, the acquisition of ALS data using Mavic 3E and Matrice 300 drones equipped with LiDAR sensors to generate digital canopy models for tree height estimation and associated error analysis. Floristic analysis identified 506 individual trees belonging to 27 genera and 18 families. Tree height measurements indicated that reforestation plots hosted the tallest trees (ranging from 8 to 16 m on average), while cocoa-based agroforestry plots featured shorter trees, with average heights between 4 and 7 m. A comparative analysis between ground-based and LiDAR-derived tree heights showed a strong correlation (R² = 0.71; r = 0.84; RMSE = 2.24 m; MAE = 1.67 m; RMSE = 2.2430 m and MAE = 1.6722 m). However, a stratified analysis revealed substantial variation in estimation accuracy, with higher performance observed in agroforestry plots (R² = 0.82; RMSE = 2.21 m and MAE = 1.43 m). These findings underscore the potential of Airborne Laser Scanning as an effective tool for the rapid and reliable estimation of tree height in heterogeneous agroforestry and reforestation systems.

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