Remote sensing of impervious surface growth: A framework for quantifying urban expansion and re-densification mechanisms

A substantial body of literature has accumulated on the topic of using remotely sensed data to map impervious surfaces which are widely recognized as an important indicator of urbanization. However, the remote sensing of impervious surface growth has not been successfully addressed. This study proposes a new framework for deriving and summarizing urban expansion and re-densification using time series of impervious surface fractions (ISFs) derived from remotely sensed imagery. This approach integrates multiple endmember spectral mixture analysis (MESMA), analysis of regression residuals, spatial statistics (Getis_Ord) and urban growth theories; hence, the framework is abbreviated as MRGU. The performance of MRGU was compared with commonly used change detection techniques in order to evaluate the effectiveness of the approach. The results suggested that the ISF regression residuals were optimal for detecting impervious surface changes while Getis_Ord was effective for mapping hot spot regions i n the regression residuals image. Moreover, the MRGU outputs agreed with the mechanisms proposed in several existing urban growth theories, but importantly the outputs enable the refinement of such models by explicitly accounting for the spatial distribution of both expansion and re - densification mechanisms. Based on Landsat data, the MRGU is somewhat restricted in its ability to measure re-densification in the urban core but this may be improved through the use of higher spatial resolution satellite imagery. The paper ends with an assessment of the present gaps in remote sensing of impervious surface growth and suggests some solutions. The application of impervious surface fractions in urban change detection is a stimulating new research idea which is driving future research with new models and algorithms

Spin transport in higher n-acene molecules

We investigate the spin transport properties of molecules belonging to the acenes series by using density functional theory combined with the non-equilibrium Green's function approach to electronic transport. While short acenes are found to be non-magnetic, molecules comprising more than nine acene rings have a spin-polarized ground state. In their gas phase these have a singlet total spin configuration, since the two unpaired electrons occupying the doubly degenerate highest molecular orbital are antiferromagnetically coupled to each other. Such an orbital degeneracy is however lifted once the molecule is attached asymmetrically to Au electrodes via thiol linkers, leading to a fractional magnetic moment. In this situation the system Au/n-acene/Au can act as an efficient spin-filter with interesting applications in the emerging field of organic spintronics.Comment: 13 pages,9 figure

Investigation of new two-dimensional materials derived from stanene

In this study, we have explored new structures which are derived from stanene. In these new proposed structures, half of the Sn atoms, every other Sn atom in two-dimensional (2D) buckled hexagonal stanene structure, are replaced with a group- IV atom, namely C, Si or Ge. So, we investigate the structural, electronic and optical properties of SnC, SnGe and SnSi by means of density functional theory based first-principles calculations. Based on our structure optimization calculations, we conclude that while SnC assumes almost flat structure, the other ones have buckled geometry like stanene. In terms of the cohesive energy, SnC is the most stable structure among them. The electronic properties of these structures strongly depend on the substituted atom. We found that SnC is a large indirect band gap semiconductor, but SnSi and SnGe are direct band gap ones. Optical properties are investigated for two different polarization of light. In all structures considered in this study, the optical properties are anisotropic with respect to the polarization of light. While optical properties exhibit features at low energies for parallel polarization, there is sort of broad band at higher energies after 5 eV for perpendicular polarization of the light. This anisotropy is due to the 2D nature of the structures. © 2017 Elsevier B.V

Reconstructing historical urban landscapes from KH-9 HEXAGON mapping camera system imagery: an example of Hangzhou City

Declassified images from the Keyhole (KH)-9 HEXAGON mapping camera system (MCS) offer fine-scale details of urban regions. However, these images have seldom been utilized in urban research due to challenges in labelling (collecting training samples), having only a single panchromatic band and classification. To tackle these limitations, this paper focuses on developing a multi-stage reconstructed historical fine-scale urban landscape (RHFUL) pipeline for KH-9 HEXAGON MCS. The proposed pipeline first integrates internalized parameters, hierarchical object-based image analysis properties and class variability to synthesize new features, abbreviated to IHC. Second, the pipeline uses a weak semi-automated supervised labelling (WSSL) approach to acquire training samples. Finally, the training samples and generated features are subjected to the SegNet deep learning architecture. The performance of each step was assessed against corresponding state-of-the-art benchmark approaches for each of synthesizing features, labelling and classification. In the proposed RHFUL pipeline, the proposed IHC provided the most salient information for urban classification, WSSL labelled urban features more accurately, and the SegNet architecture classified more accurately the urban features relative to the benchmarks. Considering the potential advantages, but also limitations of KH-9 HEXAGON MCS images, further research should be undertaken, particularly drawing on the current advances in pattern recognition techniques for contemporary digital satellite sensors

De-noised and contrast enhanced KH-9 HEXAGON mapping and panoramic camera images for urban research

In 2002 and 2020–2022, KH-9 HEXAGON mapping camera system (MCS) and panoramic camera system (PCS) images were made available to the public, respectively. Although great efforts have been made by the scientific community to develop applications that utilize KH-9 HEXAGON images, little attention has been paid to de-noising and contrast enhancement of these images particularly over urban landscapes. This paper focuses on developing a de-noising and contrast enhancement pipeline for KH-9 HEXAGON MCS and PCS over urban regions. The proposed approach employs first a wavelet transform trained using a suite of ‘degree of over-smoothing’ metrics (DOSM) for image de-noising. These metrics are sensitive to structure, texture, edges and local homogeneity of image objects. Then the de-noised image is subjected to the multi-resolution Top-hat to optimize the contrast. This method incorporates a range of shapes and neighborhoods at multiple scales. The method was applied to a KH-9 HEXAGON MCS image (acquired in 1975) and PCS image (acquired in 1974) representing a complex urban landscape, to support comprehensive evaluation under a range of settings. Performance was assessed against three state-of-the-art benchmark approaches: residual learning (deep learning), blind deconvolution and spatial filtering. To evaluate the performance of the proposed pipeline against the benchmarks, we employed the saturation image edge difference standard-deviation, co-occurrence metrics and the semivariogram. Additionally, the potential applications of pre-processed results were demonstrated using change detection, identification reference points and stereo images. The proposed method not only improved the quality of the KH-9 image across the different urban landscape types, but also preserved the original spatial characteristics of the image in comparison with the benchmark methods. At a time when understanding the nature of our changing planet is paramount, the proposed pipeline should be of great benefit to investigators wishing to use KH program images to extend their historical or time-series analyses further back in time

Statistical models of the variability of plasma in the topside ionosphere:1. Development and optimisation

This work presents statistical models of the variability of plasma in the topside ionosphere based on observations made by the European Space Agency’s (ESA) Swarm satellites. The models were developed in the “Swarm Variability of Ionospheric Plasma” (Swarm-VIP) project within the European Space Agency’s Swarm+4D-Ionosphere framework. The configuration of the Swarm satellites, their near-polar orbits and the data products developed, enable studies of the spatial variability of the ionosphere at multiple scale sizes. The statistical modelling technique of Generalised Linear Modelling (GLM) was used to create models of both the electron density and measures of the variability of the plasma structures at horizontal spatial scales between 20 km and 100 km. Despite being developed using the Swarm data, the models provide predictions that are independent of these data. Separate models were created for low, middle, auroral and polar latitudes. The models make predictions based on heliogeophysical variables, which act as proxies for the solar and geomagnetic processes. The first and most significant term in the majority of the models was a proxy for solar activity. The most common second term varied with the latitudinal region. This was the Solar Zenith Angle (SZA) in the polar region, a measure of latitude in the auroral region, solar time in the mid-latitude region and a measure of latitude in the equatorial region. Other, less significant terms in the models covered a range of proxies for the solar wind, geomagnetic activity and location. In this paper, the formulation, optimisation and evaluation of these models are discussed. The models show very little bias, with a mean error of zero to two decimal places in 14 out of 20 cases. The models capture some, but not all, of the trends present in the data, with Pearson correlation coefficients of up to 0.75 between the observations and the model predictions. The models also capture some, but not all, of the variability of the ionospheric plasma, as indicated by the precision, which ranged between 0.20 and 0.83. The addition of the thermospheric density as an explanatory variable in the models improved the precision in the polar and auroral regions. It is suggested that, if the thermosphere could be observed at a higher spatial resolution, then even more of the variability of the plasma structures could be captured by statistical models. The formulation and optimisation of the models are presented in this paper. The capability of the model in reproducing the expected climatological features of the topside ionosphere, in supporting GNSS-based ionospheric observations and the performance of the model against the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM), are provided in a companion paper (Spogli L et al. 2024. J Space Weather Space Clim https://doi.org/10.1051/swsc/2024003)