Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data has been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches.
In this context, the central goal of this research is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for using LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR. The fourth study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning.
In summary, this research has paved a new way of harnessing LiDAR big data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales.
