Analyzing Pixel-Level Relationships between Luojia 1-01 Nighttime Light and Urban Surface Features by Separating the Pixel Blooming Effect

Nighttime light (NTL) remote sensing data can effectively reveal human activities in urban development. It has received extensive attention in recent years, owing to its advantages in monitoring urban socio-economic activities. Due to the coarse spatial resolution and blooming effect, few studies can explain the factors influencing NTL variations at a fine scale. This study explores the relationships between Luojia 1-01 NTL intensity and urban surface features at the pixel level. The Spatial Durbin model is used to measure the contributions of different urban surface features (represented by Points-of-interest (POIs), roads, water body and vegetation) to NTL intensity. The contributions of different urban surface features to NTL intensity and the Pixel Blooming Effect (PIBE) are effectively separated by direct effect and indirect effect (pseudo-R2 = 0.915; Pearson correlation = 0.774; Moran’s I = 0.014). The results show that the contributions of different urban surface features to NTL intensity and PIBE are significantly different. Roads and transportation facilities are major contributors to NTL intensity and PIBE. The contribution of commercial area is much lower than that of roads in terms of PIBE. The inhibitory effect of water body is weaker than that of vegetation in terms of NTL intensity and PIBE. For each urban surface feature, the direct contribution to NTL intensity is far less than the indirect contribution (PIBE of total neighbors), but greater than the marginal indirect effect (PIBE of each neighbor). The method proposed in this study is expected to provide a reference for explaining the composition and blooming effect of NTL, as well as the application of NTL data in the urban interior.

The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas

The accurate and efficient extraction of urban areas is of great significance for better understanding of urban sprawl, built environment, economic activities, and population distribution. Night-Time Light (NTL) data have been widely used to extract urban areas. However, most of the existing NTL indexes are incapable of identifying non-luminous built-up areas. The high-resolution NTL imagery derived from the Luojia 1-01 satellite, with low saturation and the blooming effect, can be used to map urban areas at a finer scale. A new urban spectral index, named the Modified Normalized Urban Areas Composite Index (MNUACI), improved upon the existing Normalized Urban Areas Composite Index (NUACI), was proposed in this study, which integrated the Human Settlement Index (HSI) generated from Luojia 1-01 NTL data, the Normalized Difference Vegetation Index (NDVI) from Landsat 8 imagery, and the Modified Normalized Difference Water Index (MNDWI). Our results indicated that MNUACI improved the spatial variability and differentiation of urban components by eliminating the NTL blooming effect and increasing the variation of the nighttime luminosity. Compared to urban area classification from Landsat 8 data, the MNUACI yielded better accuracy than NTL, NUACI, HSI, and the EVI-Adjusted NTL Index (EANTLI) alone. Furthermore, the quadratic polynomial regression analysis showed the model based on MNUACI had the best R2 and Root-Mean Square Error (RMSE) compared with NTL, NUACI, HSI, and EANTLI in terms of estimation of impervious surface area. It is concluded that MNUACI could improve the identification of urban areas and non-luminous built-up areas with better accuracy.

Adaptive Optics for Reduction of Thermal Blooming Effects by Phase Compensation

The use of an AO system for the reduction of thermal blooming effects by phase correction of a 1.064-µm laser was studied. The energy concentration of the beam spot in the far-field increased greatly when the adaptive optics system performed in a closed loop. The phase compensation of the AO system was effective for a Bradley–Hermann distortion number of less than 130. The experimental results were in good agreement with the simulation results. This study provides many physical explanations and important conclusions for using adaptive optics to reduce the thermal blooming effect.

A POI and LST Adjusted NTL Urban Index for Urban Built-Up Area Extraction

Nighttime light (NTL) images have been broadly applied to extract urban built-up areas in recent years. However, the typical NTL images provided by Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) have the drawbacks of low resolution and blooming effect, which bring difficulty for the application of them in urban built-up area extraction. Therefore, this paper proposes the POI (point of interest) and LST (land surface temperature) adjusted NTL urban index (PLANUI) to extract the urban built-up areas with high accuracy. PLANUI is the first urban index to integrate POI and NTL for urban built-up area extraction. In this paper, NPP/VIIRS and Luojia 1-01 images were introduced as the original NTL data and the vegetation adjusted NTL urban index (VANUI) was selected as the comparison item. The threshold method was utilized to extract urban built-up areas from these data. The results show that: (1) Based on the comparison with the reference data, the PLANUI can make up the shortcoming of low resolution and the blooming effect of NTL effectively. (2) Compared with the VANUI, the PLANUI can significantly improve the accuracy of the urban built-up areas extracted and characterize urban features. (3) According to the results based on NPP/VIIRS and Luojia 1-01 images, the PLANUI has extensive applicability, both for regions with different degrees of economic development and NTL data with different resolutions. PLANUI can enhance the features of urban built-up areas with social sensing data and natural remote sensing data, which helps to weaken the NTL blooming effect and improve the extraction accuracy. PLANUI can provide an effective approach for urban built-up area extraction, which plays a certain guiding role for the study of urban structure, urban expansion, and urban planning and governance.

Coronary stent imaging

Percutaneous coronary intervention (PCI) with stent implantation is now the most common form of coronary revascularization. This chapter covers an introduction to coronary stent imaging, and further details including the blooming effect, technical requirements, diagnostic performance, and new developments.