Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis
Singh, Chandrakant ; Karan, Shivesh Kishore ; Sardar, Purnendu LU
and Samadder, Sukha Ranjan
(2022)
In Journal of Environmental Management
308.
- Abstract
- Forests play a vital role in maintaining the global carbon balance. However, globally, forest ecosystems are increasingly threatened by climate change and deforestation in recent years. Monitoring forests, specifically forest biomass is essential for tracking changes in carbon stocks and the global carbon cycle. However, developing countries lack the capacity to actively monitor forest carbon stocks, which ultimately adds uncertainties in estimating country specific contribution to the global carbon emissions. In India, authorities use field-based measurements to estimate biomass, which becomes unfeasible to implement at finer scales due to higher costs. To address this, the present study proposed a framework to monitor above-ground... (More)
- Forests play a vital role in maintaining the global carbon balance. However, globally, forest ecosystems are increasingly threatened by climate change and deforestation in recent years. Monitoring forests, specifically forest biomass is essential for tracking changes in carbon stocks and the global carbon cycle. However, developing countries lack the capacity to actively monitor forest carbon stocks, which ultimately adds uncertainties in estimating country specific contribution to the global carbon emissions. In India, authorities use field-based measurements to estimate biomass, which becomes unfeasible to implement at finer scales due to higher costs. To address this, the present study proposed a framework to monitor above-ground biomass (AGB) at finer scales using open-source satellite data. The framework integrated four machine learning (ML) techniques with field surveys and satellite data to provide continuous spatial estimates of AGB at finer resolution. The application of this framework is exemplified as a case study for a dry deciduous tropical forest in India. The results revealed that for wet season Sentinel-2 satellite data, the Random Forest (adjusted R2 = 0.91) and Artificial Neural Network (adjusted R2 = 0.77) ML models were better-suited for estimating AGB in the study area. For dry season satellite data, all the ML models failed to estimate AGB adequately (adjusted R2 between −0.05 – 0.43). Ensemble analysis of ML predictions not only made the results more reliable, but also quantified spatial uncertainty in the predictions as a metric to identify its robustness. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/0f821724-ce06-4c91-91c5-10031c255658
- author
- Singh, Chandrakant
; Karan, Shivesh Kishore
; Sardar, Purnendu
LU
and Samadder, Sukha Ranjan
- publishing date
- 2022-04-15
- type
- Contribution to journal
- publication status
- published
- in
- Journal of Environmental Management
- volume
- 308
- article number
- 114639
- publisher
- Academic Press
- external identifiers
-
- scopus:85124314044
- ISSN
- 1095-8630
- DOI
- 10.1016/j.jenvman.2022.114639
- language
- English
- LU publication?
- no
- id
- 0f821724-ce06-4c91-91c5-10031c255658
- date added to LUP
- 2025-07-10 11:07:02
- date last changed
- 2025-10-14 10:08:18
@article{0f821724-ce06-4c91-91c5-10031c255658,
abstract = {{Forests play a vital role in maintaining the global carbon balance. However, globally, forest ecosystems are increasingly threatened by climate change and deforestation in recent years. Monitoring forests, specifically forest biomass is essential for tracking changes in carbon stocks and the global carbon cycle. However, developing countries lack the capacity to actively monitor forest carbon stocks, which ultimately adds uncertainties in estimating country specific contribution to the global carbon emissions. In India, authorities use field-based measurements to estimate biomass, which becomes unfeasible to implement at finer scales due to higher costs. To address this, the present study proposed a framework to monitor above-ground biomass (AGB) at finer scales using open-source satellite data. The framework integrated four machine learning (ML) techniques with field surveys and satellite data to provide continuous spatial estimates of AGB at finer resolution. The application of this framework is exemplified as a case study for a dry deciduous tropical forest in India. The results revealed that for wet season Sentinel-2 satellite data, the Random Forest (adjusted R<sup>2</sup> = 0.91) and Artificial Neural Network (adjusted R<sup>2</sup> = 0.77) ML models were better-suited for estimating AGB in the study area. For dry season satellite data, all the ML models failed to estimate AGB adequately (adjusted R<sup>2</sup> between −0.05 – 0.43). Ensemble analysis of ML predictions not only made the results more reliable, but also quantified spatial uncertainty in the predictions as a metric to identify its robustness.}},
author = {{Singh, Chandrakant and Karan, Shivesh Kishore and Sardar, Purnendu and Samadder, Sukha Ranjan}},
issn = {{1095-8630}},
language = {{eng}},
month = {{04}},
publisher = {{Academic Press}},
series = {{Journal of Environmental Management}},
title = {{Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis}},
url = {{http://dx.doi.org/10.1016/j.jenvman.2022.114639}},
doi = {{10.1016/j.jenvman.2022.114639}},
volume = {{308}},
year = {{2022}},
}