Abstract
Land use/land cover change (LUCC) analysis is a fundamental issue in regional and global geography that can accurately reflect the diversity of landscapes and detect the differences or changes on the earth’s surface. However, a very heavy computational load is often unavoidable, especially when processing multi-temporal land cover data with fine spatial resolution using more complicated procedures, which often takes a long time when performing the LUCC analysis over large areas. This paper employs a graph-based spatial decomposition that represents the computational loads as graph vertices and edges and then uses a balanced graph partitioning to decompose the LUCC analysis on spatial big data. For the decomposing tasks, a stream scheduling method is developed to exploit the parallelism in data moving, clipping, overlay analysis, area calculation and transition matrix building. Finally, a change analysis is performed on the land cover data from 2015 to 2016 in China, with each piece of temporal data containing approximately 260 million complex polygons. It took less than 6 h in a cluster with 15 workstations, which was an indispensable task that may surpass two weeks without any optimization.
Highlights
Land surface changes result from numerous driving forces, including tropical deforestation, rangeland modification, agricultural intensification, urbanization, social-political factors and the worldwide interconnectedness of places and people [1,2,3,4,5]
Procedures of Land use/land cover change (LUCC) Analysis Based on Multi-Temporal Vector Objects In China, the Geographical Conditions Monitoring (GeoCM) project aims to monitor all kinds of indexes for the land surfaces in a dynamic and quantitative way, thereby achieving geographical descriptions of the spatial distributions and spatiotemporal changes of natural, economic and social factors
The second one was performed in a cluster environment, which aimed at testing the validity of the spatial decomposition and the entire efficiency
Summary
Land surface changes result from numerous driving forces, including tropical deforestation, rangeland modification, agricultural intensification, urbanization, social-political factors and the worldwide interconnectedness of places and people [1,2,3,4,5]. Telecoupling provides a new avenue of research that enables natural and social scientists to understand and generate information for managing how humans and nature sustainably coexist [6,7,8,9,10]. With global issues such as climate change, surging population growth and continued energy shortages, more focus is being directed toward global environmental changes [10,11,12]. Land use/land cover change (LUCC) analysis has become a fundamental component of environmental change and sustainability research [17,18]. As stated by Li [19], except for the analysis of the driving mechanism of LUCC, other existing studies are currently dedicated to designing reasonable methods to detect the changes more accurately [11,20,21,22] or predicting the change trends of the spatial-temporal progress of LUCC [23,24,25,26], which can be performed at a fine scale [27,28,29,30,31,32] or a global scale [33,34,35,36,37]
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