Comparative evaluation of Landsat 9 vegetation indices for estimating urban forest biomass in the botanical garden of University of Ibadan, Nigeria

Rebecca Israel; Akintunde A. Alo; Tomiwa Victor Oluwajuwon

doi:10.1007/s44415-025-00031-2

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Comparative evaluation of Landsat 9 vegetation indices for estimating urban forest biomass in the botanical garden of University of Ibadan, Nigeria

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Comparative evaluation of Landsat 9 vegetation indices for estimating urban forest biomass in the botanical garden of University of Ibadan, Nigeria

Rebecca Israel, Akintunde A. Alo and Tomiwa Victor Oluwajuwon

Discover Forests, Vol.1, pp.1-26

2025

DOI: https://doi.org/10.1007/s44415-025-00031-2

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Abstract

Aboveground biomass

Allometric model

Vegetation index

Landsat

Urban forestry

Remote sensing

Urban forests play a crucial role in carbon sequestration, serving as carbon sinks and contributing to climate change mitigation. However, quantitative scientific data on the carbon sequestration potential of trees in botanical gardens is relatively limited, with no documented aboveground biomass (AGB) assessments in any of Nigeria’s 30 botanical gardens. While the increasing application of remote sensing techniques has substantially enhanced forest biomass estimation through multispectral vegetation indices (VIs), few studies have focused on urban forests, and none has assessed Nigeria’s botanical gardens. Moreover, the application of Landsat 9 in urban forest biomass studies globally remains limited. This study evaluated the sensitivity and predictive performance of seven VIs derived from Landsat 9 in estimating urban forest biomass, using the University of Ibadan Botanical Garden as a case study. The VIs included Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index 2 (EVI2), Ratio Vegetation Index (RVI), Normalized Ratio Vegetation Index (NRVI), and Chlorophyll Vegetation Index (CVI). Field data from 358 trees across 25 sample plots, combined with Landsat 9 imagery, were analyzed using predictive modelling approach. All VIs, except CVI, correlated significantly with field-observed biomass (p < 0.0001). Among the models, EVI2 performed best and provided the most accurate AGB estimates (R2 = 0.58, RMSE = 43.90 Mg/ha), followed by SAVI (R2 = 0.56) and NDVI (R2 = 0.54). The spatial EVI2-based biomass map estimated a mean AGB of 160.50 Mg/ha and a corresponding carbon stock of 75.44 Mg C/ha, highlighting the significant carbon storage potential of the botanical garden. Furthermore, the garden hosts 68 tree species from 22 families, with 15% classified as endangered, vulnerable, or near threatened, emphasizing its ecological importance and the need for sustained conservation and management. Recommendations for improving remote sensing-based biomass estimation in urban ecosystems are also provided.

Details

Title: Comparative evaluation of Landsat 9 vegetation indices for estimating urban forest biomass in the botanical garden of University of Ibadan, Nigeria
Authors: Rebecca Israel (Author) - University of Ibadan
Akintunde A. Alo (Author) - University of Ibadan
Tomiwa Victor Oluwajuwon (Corresponding Author) - University of the Sunshine Coast, Queensland, Forest Research Institute
Publication details: Discover Forests, Vol.1, pp.1-26
Publisher: Springer Cham
Date published: 2025
DOI: 10.1007/s44415-025-00031-2
ISSN: 3059-2666
Copyright note: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Data Availability: The field dataset used in this study will be made available upon request from the corresponding author. However, the remote sensing dataset is freely accessible on the USGS platform.
Organisation Unit: School of Science, Technology and Engineering; Forest Research Institute
Language: English
Record Identifier: 991155339902621
Output Type: Journal article

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