Evaluation of carbon storage in a metropolitan area based on long-term time series of Land Use/Cover Change dynamics

Abstract

This research focuses on analyzing land use and cover changes in Metropolitan Area of Wuhan between 1988 and 2023, utilizing a comprehensive data set from Landsat remote sensing and machine learning techniques to understand their implications for carbon storage. It finds that the Random Forest (RF) algorithm outperforms others like Support Vector Machine (SVM), Gradient Boosting Trees (GBT), and Classification and Regression Trees in identifying land use types, achieving high accuracy and a Kappa coefficient exceeding 0.98. Significant changes in Wuhan’s landscape have been noted, especially the marked decrease in arable land and increase in urban construction, reflecting the pressures of economic development and urban expansion on natural resources and their impact on the ecosystem. The study uses the InVEST model to assess how these land use transformations affect carbon storage, revealing a significant decrease in carbon storage from 1988 to 2023, with a total reduction of approximately 428.59 × 10⁴ t from 1988 to 2023, largely attributed to the conversion of key carbon sequestering lands such as arable lands and forests into urban areas. This transition, particularly from arable land to urban construction land, underscores the challenges faced in managing land use changes without compromising environmental sustainability and carbon storage capacities.

Graphical abstract

Keywords

carbon storage / machine learning methods / InVEST model / land cover/use

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