Geo-cognitive computing method for identifying “source-sink” landscape patterns of river basin non-point source pollution

摘要

The aim of this study was to quantitatively evaluate the influences of landscape composition and spatial structure on the transmission process of non-point source pollutants in different regions. The location-weighted landscape contrast index, using the hydrological response unit (HRULCI) as the minimum research unit, was proposed in this paper. Through the description of the endemic landscape types and various geographical factors in the basin, the index calculation can reflect the impact of the “source-sink” landscape structure on the non-point source pollution in different regions and quantitatively evaluate the contribution of different landscape types and geographical factors to non-point source pollution. This study constructed a method of geo-cognitive computing for identifying “source-sink” landscape patterns of river basin non-point source pollution at two levels. 1) The basin level: the spatial distribution and landscape combination of the entire basin are identified, and the crucial “source” and “sink” landscape types are obtained to measure the differences in the non-point source pollutant transmission processes between the “source” and “sink” landscapes in the different watersheds. 2) The landscape level: HRULCI is calculated based on multiple geographical correction weighting factors. By using the idea of intersecting geographic information system (GIS) and landscape ecology, the landscape spatial pattern and ecological processes are linked. Compared with the traditional method for studying landscape patterns, the calculation of HRULCI makes the proposed method more ecologically significant. Lastly, a case study was evaluated to verify the significance of the proposed research method by taking the Yanshi River basin, a sub-basin belonging to the Jiulong River basin located in Fujian Province, China, as the experimental study zone. The results showed that this method can reflect the spatial distribution characteristics of the “source-sink” types and their relationship with non-point source pollution. By comparing the resulting calculation based on HRULCI, the risk of nutrient loss and the influence of landscape patterns and ecological processes on non-point pollution in different catchments can be obtained. Keywords: non-point source pollution, “source-sink” landscape pattern, remote sensing, hydrological response unit, quantitative calculation DOI: 10.25165/j.ijabe.20171005.3272 Citation: Zhang X, Cui J T, Liu Y Q, Wang L. Geo-cognitive computing method for identifying “source-sink” landscape patterns of river basin non-point source pollution. Int J Agric & Biol Eng, 2017; 10(5): 55–68.