Abstract
Massive spatial data poses increasing challenges to traditional analysis software. For example, landscape pattern analysis software FRAGSTATS has been unable to process provincial-level high-resolution land cover data. Based on Two-Pass connected component labeling algorithm, this paper provides an improved parallel algorithm with GPU programming to solve the landscape metrics computation problem about massive land use data. This parallel algorithm for massive landscape metrics calculation takes full advantage of a general computer, and focuses on patch perimeter and area calculation. It can also accelerate computation speed by multithreading and iteration times reduction to decrease computation time than traditional serial algorithms. We apply the proposed algorithm and serial algorithm to calculate landscape metrics of the land use classification raster images at different resolutions under patch scale.The experiment result shows great improvement of calculation performance of landscape metrics, and the efficiency has been improved by 5 times comparing with the serial algorithm, which proves that our proposed algorithm is a better choice for landscape analysis of massive data.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
