Abstract
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.
Highlights
Soil erosion models are a useful tool for predicting the amount of soil erosion, guiding the allocation of soil and water conservation measures, and optimizing the utilization of water and soil resources in basins
We focus on integrating the Hadoop platform with geographic information system (GIS) for parallel computing involving soil erosion modelling
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Summary
Soil erosion models are a useful tool for predicting the amount of soil erosion, guiding the allocation of soil and water conservation measures, and optimizing the utilization of water and soil resources in basins. The MapReduce parallel computing model for big data analysis developed by Google performs the parallel processing of massive amounts of data through cheaper server clusters [14]. This model has advantages such as few hardware requirements, rapid scaling, and easy modelling [15]. Similar research areas include atmospheric analysis [27], large-scale Light Detection and Ranging (LiDAR) data analysis [28], remote sensing [29], trip recommendation [30], and others [31,32] These platforms and cases are suitable for different spatial data and can be applied in different applications and domains. The proposed approach provides a solution for the refined soil erosion modelling on the Hadoop platform, which is integrated with a GIS platform
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