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ISPRS Int. J. Geo-Inf. 2019, 8(3), 103;

Mr4Soil: A MapReduce-Based Framework Integrated with GIS for Soil Erosion Modelling

College of Environment and Planning, Henan University, Kaifeng 475004, China
Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Kaifeng 475004, China
Urban Big Data Institute, Henan University, Kaifeng 475004, China
School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission of the Ministry of Water Resources, Zhengzhou 450003, China
Environmental Systems Research Institute, Inc. Redlands, 380 New York Street, Redlands, CA 92373-8100, USA
Authors to whom correspondence should be addressed.
Received: 10 January 2019 / Revised: 8 February 2019 / Accepted: 22 February 2019 / Published: 27 February 2019
(This article belongs to the Special Issue Distributed and Parallel Architectures for Spatial Data)
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A soil erosion model is used to evaluate the conditions of soil erosion and guide agricultural production. Recently, high spatial resolution data have been collected in new ways, such as three-dimensional laser scanning, providing the foundation for refined soil erosion modelling. However, serial computing cannot fully meet the computational requirements of massive data sets. Therefore, it is necessary to perform soil erosion modelling under a parallel computing framework. This paper focuses on a parallel computing framework for soil erosion modelling based on the Hadoop platform. The framework includes three layers: the methodology, algorithm, and application layers. In the methodology layer, two types of parallel strategies for data splitting are defined as row-oriented and sub-basin-oriented methods. The algorithms for six parallel calculation operators for local, focal and zonal computing tasks are designed in detail. These operators can be called to calculate the model factors and perform model calculations. We defined the key-value data structure of GeoCSV format for vector, row-based and cell-based rasters as the inputs for the algorithms. A geoprocessing toolbox is developed and integrated with the geographic information system (GIS) platform in the application layer. The performance of the framework is examined by taking the Gushanchuan basin as an example. The results show that the framework can perform calculations involving large data sets with high computational efficiency and GIS integration. This approach is easy to extend and use and provides essential support for applying high-precision data to refine soil erosion modelling. View Full-Text
Keywords: soil erosion modelling; parallel computing; Hadoop; MapReduce; GIS soil erosion modelling; parallel computing; Hadoop; MapReduce; GIS

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Han, Z.; Qin, F.; Cui, C.; Liu, Y.; Wang, L.; Fu, P. Mr4Soil: A MapReduce-Based Framework Integrated with GIS for Soil Erosion Modelling. ISPRS Int. J. Geo-Inf. 2019, 8, 103.

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