A comparative analysis of machine learning techniques for aboveground biomass estimation: A case study of the Western Ghats, India

Ayushi, Kurian; Babu, Kanda Naveen; Ayyappan, Narayanan; Nair, Jaishanker Raghunathan; Kakkara, Athira; Reddy, C. Sudhakar

doi:10.1016/j.ecoinf.2024.102479

Cited by 5 publications

(1 citation statement)

References 116 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, its application to wetlands needs to be strengthened. Hybrid machine learning models [67] or ensemble stacking approaches [68] should be able to improve the estimation accuracy. In addition, with the development of unmanned aircraft remote sensing technology, combined with field data samples, it is expected that higher biomass estimation accuracies will be achieved.…”

Section: Discussionmentioning

confidence: 99%

Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment

Chen,

Wang,

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

With climate change and urbanization expansion, wetlands, which are some of the largest carbon stocks in the world, are facing threats such as shrinking areas and declining carbon sequestration capacities. Wetland carbon stocks are at risk of being transformed into carbon sources, especially those of wetlands with strong land use–natural resource conservation conflict. Moreover, there is a lack of well-established indicators for evaluating the health of wetland carbon stocks. To address this issue, we proposed a novel framework for the safety assessment of wetland carbon stocks using the Super Slack-Based Measure (Super-SBM), and we then conducted an empirical study on the Quanzhou Bay Estuary Wetland (QBEW). This framework integrates the unexpected output indicator (i.e., carbon emissions), the expected output indicators, including the GDP per capita and carbon stock estimates calculated via machine learning (ML)-based remote sensing inversion, and the input indicators, such as environmental governance investigations, climate conditions, socio-economic activities, and resource utilization. The results show that the annual average safety assessment for carbon pools in the QBEW was a meager 0.29 in 2015, signaling a very poor state, likely due to inadequate inputs or excessive unexpected outputs. However, there has been a substantial improvement since then, as evidenced by the fact that all the safety assessments have exceeded the threshold of 1 from 2018 onwards, reflecting a transition to a “weakly effective” status within a safe and acceptable range. Moreover, our investigation employing the Super-SBM model to calculate the “slack variables” yielded valuable insights into optimization strategies. This research advances the field by establishing a safety measurement framework for wetland carbon pools that leverages efficiency assessment methods, thereby offering a quantitative safeguard mechanism that supports the achievement of the “3060” dual-carbon target.

show abstract

Section: Discussionmentioning

confidence: 99%