Enhanced Land-Cover Classification through a Multi-Stage Classification Strategy Integrating LiDAR and SIF Data

Abstract

Light detection and ranging (LiDAR) offers high-precision, 3D information, and the ability to rapidly acquire data, giving it a significant advantage in timely resource monitoring. Currently, LiDAR is widely utilized in land-cover classification tasks. However, the complexity and uneven distribution of land-cover types in rural and township settings pose additional challenges for fine-scale classification. Although the geometric features of LiDAR can provide valuable insights and have been extensively explored, distinguishing between objects with similar 3D characteristics has considerable room for improvement, particularly in complex scenarios where the introduction of additional attribute information is necessary. To address these challenges, this work proposes the integration of solar-induced chlorophyll fluorescence (SIF) features to assist and optimize LiDAR data for land-cover classification, leveraging the sensitivity of SIF to vegetation physiological characteristics. Moreover, a multi-stage classification strategy is introduced to enhance the utilization of SIF information. The implementation of this approach achieves a maximum classification accuracy of 92.45%, yielding satisfactory results with low computational costs. This outcome validates the feasibility of applying SIF information in land-cover classification. Furthermore, the results obtained through the multi-stage classification strategy demonstrate improvements ranging from 6.65% to 9.12% compared with land-cover classification relying solely on LiDAR, effectively highlighting the optimization role of SIF in enhancing LiDAR-based land-cover classification, particularly in complex rural and township environments. Our approach offers a robust framework for precise and efficient land-cover classification by leveraging the combined strengths of LiDAR and SIF.