Two-tier classification framework for mapping grassland types using multisource earth observation data

Abstract

The dynamic changes in grassland types are crucial for conserving grassland biodiversity, conducting comprehensive health assessments, and gaining insights into ecosystem evolution. However, accurately mapping grassland types remains an ongoing challenge, especially over large areas. In response, we developed a hierarchical classification framework using random forest to tackle this task. This framework was structured into two tiers: land cover (LC) classification and grassland-type mapping, with each tier using specific features tailored to its respective objectives. In this framework, LC samples were automatically generated using existing LC products combined with vegetation indices and phenological features. We fused spectral information, phenological features, and habitat factors such as topographic indices, soil, and climate from multisource earth observation (EO) data to enhance grassland-type mapping performance. This framework successfully generated distribution maps of grassland types on the Qinghai Plateau for the years 1990–2020. Our findings revealed the following: (1) Using the two-tier classification framework and fusing multisource features, an ideal distribution map of grassland types was obtained, with a macro-average F1 score (F1) of 91% and an overall accuracy (OA) of 96%; (2) Compared to a one-shot classification framework, the two-tier classification framework achieved higher accuracy, with F1 and OA increasing by 11% and 7%, respectively; (3) Climate, topographical, soil, and phenological features assisted in distinguishing grassland types with similar spectral characteristics, especially for zonal grasslands. Adding these features increased F1 by 21%, 10%, 7%, and 3%, respectively, while OA increased by 9%, 5%, 5%, and 2%, respectively. Among these features, relative humidity, total precipitation from May to September, geographic coordinates, and elevation had the greatest effect on grassland type differentiation. June and July were the optimal phenological periods for mapping; (4) Over the 30-year period, the grasslands on the Qinghai Plateau showed an expanding trend, with extensive areas of alpine steppes transforming into alpine meadows. The results of this study provide the first elucidation of grassland-type changes across the Qinghai Plateau from 1990 to 2020. Moreover, they underscore the potential of hierarchical classification frameworks and the integration of multisource EO data in mapping grassland-type distribution.

Keywords

• Grassland type
• hierarchical classification
• random forest
• multisource features
• grassland structure changes