Estimating NOx removal capacity of urban trees using stable isotope method: A case study of Beijing, China.

It is widely recognized that green infrastructures in urban ecosystems provides important ecosystem services, including air purification. The potential absorption of nitrogen oxides (NOx) by urban trees has not been fully quantified, although it is important for air pollution mitigation and the well-being of urban residents. In this study, four common tree species (Sophora japonica L., Fraxinus chinensis Roxb., Populus tomentosa Carrière, Sabina chinensis (L.)) in Beijing, China, were studied. The dual stable isotopes (15N and 18O) and a Bayesian isotope mixing model were applied to estimate the sources contributions of potential nitrogen sources to the roadside trees based on leaf and soil sampling in urban regions. The following order of sources contributions was determined: soil > dry deposition > traffic-related NOx. The capacity of urban trees for NOx removal in the city was estimated using a remote sensing and GIS approach, and the removal capacity was found to range from 0.79 to 1.11 g m-2 a-1 across administrative regions, indicating that 1304 tons of NOx could be potentially removed by urban trees in 2019. Our finding qualified the potential NOx removal by urban trees in terms of atmospheric pollution mitigation, highlighting the role of green infrastructure in air purification, which should be taken into account by stakeholders to manage green infrastructure as the basis of a nature-based approach.

Robust Building Extraction for High Spatial Resolution Remote Sensing Images with Self-Attention Network.

Building extraction from high spatial resolution remote sensing images is a hot spot in the field of remote sensing applications and computer vision. This paper presents a semantic segmentation model, which is a supervised method, named Pyramid Self-Attention Network (PISANet). Its structure is simple, because it contains only two parts: one is the backbone of the network, which is used to learn the local features (short distance context information around the pixel) of buildings from the image; the other part is the pyramid self-attention module, which is used to obtain the global features (long distance context information with other pixels in the image) and the comprehensive features (includes color, texture, geometric and high-level semantic feature) of the building. The network is an end-to-end approach. In the training stage, the input is the remote sensing image and corresponding label, and the output is probability map (the probability that each pixel is or is not building). In the prediction stage, the input is the remote sensing image, and the output is the extraction result of the building. The complexity of the network structure was reduced so that it is easy to implement. The proposed PISANet was tested on two datasets. The result shows that the overall accuracy reached 94.50 and 96.15%, the intersection-over-union reached 77.45 and 87.97%, and F1 index reached 87.27 and 93.55%, respectively. In experiments on different datasets, PISANet obtained high overall accuracy, low error rate and improved integrity of individual buildings.

Investigating the Spatiotemporal Variability and Driving Factors of Artificial Lighting in the Beijing-Tianjin-Hebei Region Using Remote Sensing Imagery and Socioeconomic Data.

With rapid urbanization and economic development, artificial lighting at night brings convenience to human life but also causes a considerable urban environmental pollution issue. This study employed the Mann-Kendall non-parametric test, nighttime light indices, and the standard deviation method to investigate the spatio-temporal characteristics of artificial lighting in the Beijing-Tianjin-Hebei region. Moreover, nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System, socioeconomic data, and high-resolution satellite images were combined to comprehensively explore the driving factors of urban artificial lighting change. The results showed the following: (1) Overall, there was an increasing trend in artificial lighting in the Beijing-Tianjin-Hebei region, which accounted for approximately 56.87% of the total study area. (2) The change in artificial lighting in the entire area was relatively stable. The artificial lighting in the northwest area changed faster than that in the southeast area, and the areas where artificial lighting changed the most were Beijing, Tianjin and Tangshan. (3) The fastest growth of artificial lighting was in Chengde and Zhangjiakou, where the rates of increase were 334% and 251%, respectively. The spatial heterogeneity of artificial lighting in economically developed cities was higher than that in economically underdeveloped cities such as Chengde and Zhangjiakou. (4) Multi-source data were combined to analyse the driving factors of urban artificial lighting in the entire area. The Average Population of Districts under City (R2 = 0.77) had the strongest effect on artificial lighting. Total Passenger Traffic (R2 = 0.54) had the most non-obvious effect. At different city levels, driving factors varied with differences of economy, geographical location, and the industrial structures of cities. Urban expansion, transportation hubs, and industries were the major reasons for the significant change in nighttime light. Urban artificial lighting represents a trend of overuse closely related to nighttime light pollution. This study of artificial lighting contributes to the rational planning of urban lighting systems, the prevention and control of nighttime light pollution, and the creation of liveable and ecologically green cities.

Eliminating the Effect of Image Border with Image Periodic Decomposition for Phase Correlation Based Remote Sensing Image Registration.

In the remote sensing community, accurate image registration is the prerequisite of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. However, when the Discrete Fourier Transform (DFT) of an image is computed, the image is implicitly assumed to be periodic. In practical application, it is impossible to meet the periodic condition that opposite borders of an image are alike, and image always shows strong discontinuities across the frame border. The discontinuities cause a severe artifact in the Fourier Transform, namely the known cross structure composed of high energy coefficients along the axes. Here, this phenomenon was referred to as effect of image border. Even worse, the effect of image border corrupted its registration accuracy and success rate. Currently, the main solution is blurring out the border of the image by weighting window function on the reference and sensed image. However, the approach also inevitably filters out non-border information of an image. The existing understanding is that the design of window function should filter as little information as possible, which can improve the registration success rate and accuracy of methods based on phase correlation. In this paper, another approach of eliminating the effect of image border is proposed, namely decomposing the image into two images: one being the periodic image and the other the smooth image. Replacing the original image by the periodic one does not suffer from the effect on the image border when applying Fourier Transform. The smooth image is analogous to an error image, which has little information except at the border. Extensive experiments were carried out and showed that the novel algorithm of eliminating the image border can improve the success rate and accuracy of phase correlation based image registration in some certain cases. Additionally, we obtained a new understanding of the role of window function in eliminating the effect of image border, which is helpful for researchers to select the optimal method of eliminating the effect of image border to improve the registration success rate and accuracy.

Potentiality of Using Luojia 1-01 Nighttime Light Imagery to Investigate Artificial Light Pollution.

The successful launch of Luojia 1-01 complements the existing nighttime light data with a high spatial resolution of 130 m. This paper is the first study to assess the potential of using Luojia 1-01 nighttime light imagery for investigating artificial light pollution. Eight Luojia 1-01 images were selected to conduct geometric correction. Then, the ability of Luojia 1-01 to detect artificial light pollution was assessed from three aspects, including the comparison between Luojia 1-01 and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the source of artificial light pollution and the patterns of urban light pollution. Moreover, the advantages and limitations of Luojia 1-01 were discussed. The results showed the following: (1) Luojia 1-01 can detect a higher dynamic range and capture the finer spatial details of artificial nighttime light. (2) The averages of the artificial light brightness were different between various land use types. The brightness of the artificial light pollution of airports, streets, and commercial services is high, while dark areas include farmland and rivers. (3) The light pollution patterns of four cities decreased away from the urban core and the total light pollution is highly related to the economic development. Our findings confirm that Luojia 1-01 can be effectively used to investigate artificial light pollution. Some limitations of Luojia 1-01, including its spectral range, radiometric calibration and the effects of clouds and moonlight, should be researched in future studies.

Investigation of inclusion complex of epothilone A with cyclodextrins.

The inclusion complexation of Epothilone A with native cyclodextrin (ß- or γ-CD) and its derivative hydroxypropyl-ß-cyclodextrin (HPßCD) were prepared. Their behavior, characterization, and binding ability were investigated in both solution and the solid state by means of UV-vis, NMR, XRD, DSC and SEM. The results show that the water solubility and solution stability obviously increased in the inclusion complex with cyclodextrins. Meanwhile, the inclusion complexes still retained anticancer activity against A549 and MCF-7 cells, similar to free Epothilone A. This satisfactory water solubility, high solution stability, and high anticancer activity of the Epothilone A/CD complexes will be potentially useful as an anticancer therapy.

A method of spatial mapping and reclassification for high-spatial-resolution remote sensing image classification.

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.

[Extracting buildings height and distribution information in Tianjin City from the shadows in ALOS images].

Spectral difference is an important aspect for extracting shadows of buildings. Based on the analysis of the relationship between the building heights and the shadows in ALOS Images, the paper presents the principle and the method for building heights estimation in a city from the shadows of an image, and works out a feasible approach to determining shadow zones in a panchromatic ALOS Images. It has also contributed a standard process for extracting buildings distribution information of different heights in a city from the shadows in a panchromatic ALOS Images. A result with about 87.6% accuracy has been achieved while applying this technique to Tianjin City, which has demonstrated prospective applications of satellite remote sensing to urban purposes.

[An improved physical model to correct topographic effects in remotely sensed imagery].

Topographic correction for remotely sensed imagery is an important preprocessing step in order to improve the retrieval accuracy of land surface spectral reflectance in mountainous area. Various kinds of topographic correction models have been proposed in the literature. Each model has its advantages and limitations. In consideration of the limitations of the topographic correction models in the literature, an improved Shepherd topographic correction model is proposed in this paper. Diffuse irradiance is an essential factor in the physically based topographic correction model. While in the Shepherd model (originally proposed by Shepherd et al. in 2003), accuracy of the method to compute the diffuse irradiance is relatively low; therefore, the accuracy of the land surface spectral reflectance retrieved with the Shepherd model is impacted. In order to improve the accuracy of diffuse irradiance, hence the accuracy of land surface spectral reflectance, a different method (named the Perez model), is used to obtain the diffuse irradiance with higher accuracy in the improved Shepherd model. Landsat 5 Thematic Mapper (TM) imagery acquired on July 12th 2006, over the mountainous areas in the north of Beijing city, was employed to retrieve land surface spectral reflectance with the improved Shepherd topographic correction model and 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) atmospheric radiative transfer model. Correction results were tested with three different methods. Testing result shows that the improved Shepherd topographic correction model can achieve a good correction result and is better than Shepherd and C topographic correction model. What is more, this improved model is physically based and can be applied to all kinds of optical satellite imagery.