Analysis of the Relationship between Landuse and Non-point Source Pollution in Ashi River Basin

Landuse is one of the most influential factors of non-point source pollution. Based on the three-year landuse data( 2000,2005 and 2008),Arc GIS and Fragstat were used to analyze the landuse type and the change of landscape pattern. The relationships between landuse and non-point source-total nitrogen( NPS-TN) and nonpoint source-total phosphorus( NPS-TP) were discussed with the methods of spatially statistical analysis,landscape pattern analysis and principal component analysis. The study results conveyed that agricultural land and forestland,which accounted for over 92% of the study area,were the major landuse type of Ashi River Basin.Meanwhile,the NPS pollution had close connections with landuse type and landscape pattern. When it comes to landuse type,the export risks of NPS-TN and NPS-TP were agricultural land > urban land > grassland > forestland. As for landscape pattern,NPS-TN and NPS-TP were positively related to SHDI and SHEI, while negatively connected with LPI,AI and COHESION. Therefore,the study could reach the conclusion that the more fragmented and complicated the landscape patterns were,the more serious the NPS pollution was.

Population Spatial Distribution Based on Luojia 1-01 Nighttime Light Image:A Case Study of Beijing

With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,the quantity and agglomeration of population distribution can be estimated and visualized.It will provide a basis for a more rational urban planning.This paper takes Beijing as the research area and uses a new Luojia1-01 nighttime light image with high resolution,land use type data,Points of Interest(POI)data,and other data to construct the population spatial index system,establishing the index weight based on the principal component analysis.The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018.Then the population spatial distribution was visualize using GIS technology.After accuracy assessments by comparing the result with the WorldPop data,the accuracy has reached 0.74.The proposed method was validated as a qualified method to generate population spatial maps.By contrast of local areas,Luojia 1-01 data is more suitable for population distribution estimation than the NPP/VIIRS(Net Primary Productivity/Visible infrared Imaging Radiometer)nighttime light data.More geospatial big data and mathematical models can be combined to create more accurate population maps in the future.