Riverine ecosystems are increasingly threatened by human activities and are generally under-protected globally. Various chemical, hydrological, geophysical, and biological indicators can be used to monitor and assess different aspects of river ecosystems. Attention is increasingly been paid to the inclusion of biological indicators in watershed monitoring and assessment. Benthic macroinvertebrates has many advantages over fish and algae, and have become the most popular biological indicators of river health. The Benthic Macroinvertebrate Index of Biological Integrity (B-IBI) has a wide range of applications to river health assessment and restoration. Examining the relationships between B-IBI and human activities at the landscape scale is important for watershed management.Compared to local indicators, including physical and chemical factors, GIS (Geographic Information System) data have become increasingly convenient and accessible for landscape-scale assessments of the impacts of human activities on river ecosystems. The use of GIS data instead of direct field measurements is justified based on the assumption that the density of human disturbances is the primary factor contributing to changes in freshwater ecosystems. The relative status of river ecosystems can be predicted by focusing on the drivers of change, rather than field data. In this study, we focused on the relationship between landscape indices and B-IBI. Using environmental and biotic data from 66 sites within the Luan River Basin, the aims of the study were to develop groups of landscape indices and to explore which landscape index bet reflected B-IBI results. Macroinvertebrate assemblages were collected using D-frame nets. Water chemistry and physical parameters were measured at the reach scale. In this study, eight relatively intact reference sites and eight sites showing signs of human impacts were selected to construct the B-IBI index.Reference and impacted sites were selected based on their physical habitat and chemical characteristics. The B-IBI was constructed using seven core parameters. Based on watershed land use, 13 landscape metrics were developed, including nine lumped metrics and four inverse-distance-weighted metrics. Using stepwise multiple linear regression, we compared the 13 landscape metrics to determine whether spatial proximity and the hydrological effects of land use could be used to account for additional variability in the B-IBI. The following results were obtained. First, the model of inverse-distance-weighted metrics had the highest adjusted R² (0.173). The land use percentage is a widely used index in previous studies. However, the percentage attributes of the whole watershed and buffers had little explanatory ability. Second, the percentage of agricultural land use was the only predictive variable in the models, indicating that agriculture is the human activity that causes the most serious declines in B-IBI. Third, the effects of agricultural fields increased with proximity to sampling site. Lumped attributes (i.e., percent land use) are often used to characterize the condition of catchments. However, they are not spatially explicit and do not account for the disproportionate influence of land located near watercourses or connected to them by overland flow. Our results show that watershed management should focus on agricultural impacts at the watershed scale and riparian zone protection at the reach scale.