A GPU-Based Parallel Algorithm for Landscape Metrics

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.