The changing patterns of watersheds in a landscape, driven by human activities, play an important role in non-point source pollution processes. This paper aims to improve the location-weighted landscape contrast index using remote sensing and GIS technology to account for the effects of scale and ecological processes. The hydrological response unit(HRU) with a single land use and soil type was used as the smallest unit. The relationship between the landscape index and typical ecological processes was established by describing the influence of the landscape pattern on non-point source pollution. To verify the research method, this paper used the Yanshi River basin as a study area. The results showed that the relative intensity of non-point source pollution in different regions of the watershed and the location-weighted landscape contrast index based on the minimum HRU can qualitatively reflect the risk of regional nutrient loss.
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
Remote sensing is an important technical means to investigate land resources.Optical imagery has been widely used in crop classification and can show changes in moisture and chlorophyll content in crop leaves,whereas synthetic aperture radar(SAR)imagery is sensitive to changes in growth states and morphological structures.Crop-type mapping with a single type of imagery sometimes has unsatisfactory precision,so providing precise spatiotemporal information on crop type at a local scale for agricultural applications is difficult.To explore the abilities of combining optical and SAR images and to solve the problem of inaccurate spatial information for land parcels,a new method is proposed in this paper to improve crop-type identification accuracy.Multifeatures were derived from the full polarimetric SAR data(GaoFen-3)and a high-resolution optical image(GaoFen-2),and the farmland parcels used as the basic for object-oriented classification were obtained from the GaoFen-2 image using optimal scale segmentation.A novel feature subset selection method based on within-class aggregation and between-class scatter(WA-BS)is proposed to extract the optimal feature subset.Finally,crop-type mapping was produced by a support vector machine(SVM)classifier.The results showed that the proposed method achieved good classification results with an overall accuracy of 89.50%,which is better than the crop classification results derived from SAR-based segmentation.Compared with the ReliefF,mRMR and LeastC feature selection algorithms,the WA-BS algorithm can effectively remove redundant features that are strongly correlated and obtain a high classification accuracy via the obtained optimal feature subset.This study shows that the accuracy of crop-type mapping in an area with multiple cropping patterns can be improved by the combination of optical and SAR remote sensing images.
