Remote Sensing and its application to urban studies

Abstract

Remote Sensing, in the form of air photographs, has been used in urban analysis since the nineteenth century. This paper chronicles some of its contributions from then until the present day and introduces the reader to the current and future high resolution spaceborne systems that will increasingly provide information to the urban analyst. Resolution requirements are examined and potential applications of the data are proposed.     

Cell Signal Distribution and Imprint Reliability in FeRAM with Hybrid Bit Line Architecture

We have investigated the cell signal distributions of ferroelectric random access memories (FeRAMs) using newly developed design scheme, hybrid bit line architecture and their reliability against imprint degradation. Since FeRAMs have relatively large signal distributions due to nonuniform ferroelectric storage capacitors, cell signal interference between neighboring bit lines severely degrades the sensing signal margin. In order to remove the cross-talk noise, hybrid bit line architecture is developed, which is a combination of the conventional folded and open bit line schemes. The lifetime against imprint degradation of FeRAM devices is also increased using hybrid bit line architecture.     

Mapping the total phosphorus concentration of biosolid amended surface soils using LANDSAT TM data

Conventional methods for soil sampling and analysis for soil variability in chemical characteristics are too time-consuming and expensive for multi-seasonal monitoring over large-scale areas. Hence, the objectives of this study are: 1) to determine changes in chemical concentrations of soils that are amended with treated sewage sludge; and 2) to determine if LANDSAT TM data can be used to map surface chemical characteristics of such amended soils. For this study, we selected two fields in NW Ohio, designated as F34 and F11, that had been applied with 34 and 11 ton acre^− 1 of biosolids, respectively. Soil samples from a total of 70 sampling locations across the two fields were collected one day prior to LANDSAT 5 overpass and were analyzed for several elemental concentrations. The accumulation of Ba, Cd, Cu, S and P were found to be significantly higher in the surface soils of field F34, compared to field F11. Regression equations were established to search for algorithms that could map these five elemental concentrations in the surface soils using six, dark-object-subtracted (DOS) LANDSAT TM bands and the 15 non-reciprocal spectral ratios derived from these six bands for the May 20, 2005, LANDSAT 5 TM image. Phosphorus (P) had the highest R² adjusted value (67.9%) among all five elements considered, and the resulting algorithm employed only spectral ratios. This model was successfully tested for robustness by applying it to another LANDSAT TM image obtained on June 5, 2005. Our results enabled us to conclude that LANDSAT TM imagery of bare-soil fields can be used to quantify and map the spatial variation of total phosphorous concentration in surface soils. This research has significant implications for identification and mapping of areas with high P, which is important for implementing and monitoring the best phosphorous management practices across the region.     

Room temperature response and enhanced hydrogen sensing in size selected Pd-C core-shell nanoparticles: Role of carbon shell and Pd-C interface

In the present work, the effect of carbon shell around size selected palladium (Pd) nanoparticles on hydrogen (H₂) sensing has been studied by investigating the sensing response of Pd-C core-shell nanoparticles having a fixed core size and different shell thickness. The H₂ sensing response of sensors based on Pd and Pd-C nanoparticles deposited on SiO₂ and graphene substrate has been measured over a temperature range of 25 °C–150 °C. It is observed that Pd-C nanoparticle sensor shows higher sensitivity with increase in shell thickness and faster response/recovery in comparison to that of Pd nanoparticle samples. Pd-C nanoparticles show room temperature H₂ sensitivity in contrast to Pd nanoparticles which respond only at higher temperatures. Role of carbon shell is also understood by investigating H₂ sensing properties of Pd and Pd-C nanoparticles on graphene substrates. These results show that higher catalytic activity and electronic interaction at Pd-C interface, a complete coverage and protection of Pd surface by carbon and presence of structural defects in nanoparticle core are important for room temperature and higher sensing response.     

Comparative Analysis of Soil Water and Salt Dielectric Model

The soil dielectric constant，the basis of the microwave remote sensing inversion of soil water and salt，is one of the main parameters of microwave remote sensing research.It is very important to select the high precision soil water and salt dielectric model to improve the precision of soil water and salt inversion.However，the existing soil water and salt model still can’t quantitatively describe the effect of salt factor on soil permittivity.This paper simulates the complex permittivity of different texture，water content and salinity wet soil by Dobson model，Dobson\|S model，GRMDM model，HQR model and WYR model at L，Cand X bands when soil temperature equal 25 ℃.Comparison and analysis the simulation values with measured values by microwave vector network analyzer.The results show：(1)Dobson model and GRMDM model can accurately simulate the real part of dielectric constant of non\|saline soil，while the stimulated values of imaginary part is less than the measured values;(2)Dobson\|S model can well simulate the real part of the dielectric constant of saline soil，at L，C\nd Xbands the correlation coefficient R equal 0.97，the RMSE is less than 2.10.But for the imaginary part of the dielectric constant of saline soil，the Dobson\|S model，HQR model and WYR model with different simulation accuracy，when soil water content different.This study would benefit the choice of a suitable soil dielectric model for soil moisture and salinity retrieval.     

Indicators and Aspects of Hydrological Drought in Lebanon

Change in climate conditions has become a global issue that has given a serious concern by many researchers. However, the availability of data in this regard is considered as a major element for optimum comparative analysis. The Mediterranean region is influenced by climate change, which is reflected mainly by its impact on water sources supply and flow regime. In Lebanon, these water sources are witnessing obvious quantitative decrease, thus affected the supply side, the so-called “hydrologic drought”. Therefore, many studies have been made to figure out a comprehensive understanding on water resources in Lebanon and their interrelation with climatic trends, but they often analyze one component of the water cycle. This study involves different indices of surface and subsurface water, thus, followed a comparative analysis of different hydrologic records. This was achieved by applying graphical illustrations of the numerical values adopted from available records. In this regard, different tools of analysis were used, and more certainly remotely sensed data were helpful for monitoring approaches. Therefore, results of the obtained comparative analysis revealed a clear regression in the amount of available water from different sources in Lebanon. These sources, which are under the impact of human like rivers and groundwater, showed a 23–29% decrease in the amounts of water since the last four decades. While sources, with less human interference, like snow cover and precipitation have been decreased by 12–16%. However, in both cases, the status is quite alarming and needs immediate water management plans to conserve water resources in Lebanon.     

Optical sensing of materials movements occurring at a plant surface with a probe beam

Optical sensing of materials movements across a plant surface with a probe beam is reported. It is based on monitoring of deflection of a probe beam passing through a vicinity of the plant surface. The deflection of the probe beam was caused by concentration gradients of chemical species involved in the materials moments in physiological activities of the plant. Typha angustifolia L. was used as a model plant. The beam deflection at different locations of both leaves and roots in respiration process were monitored and compared. It is found that deflection signals changed with time and locations. Experimental results showed that the beam deflection method could be used for noninvasive sensing and monitoring of materials movements across the plant surface.     

Riparian Vegetation Mapping for Hydraulic Roughness Estimation Using Very High Resolution Remote Sensing Data Fusion

For detailed hydraulic modeling, accurate spatial information of riparian vegetation patterns needs to be derived in automatic fashion. We propose a supervised classification for heterogeneous riparian corridors with a low number of spectrally separate classes using data fusion of a Quickbird image and LIDAR data. The approach considers nine land cover classes including three woody riparian species, brush, cultivated areas, grassland, urban infrastructures, bare soil and water. The classical “stacked vector” approach is adopted for data fusion, while the nonparametric weighted feature-extraction method and the pixel-oriented maximum likelihood algorithm are used for feature-reduction and classification purposes, respectively. We test the approach over a 14-km stretch of the Sieve River (Tuscany Region, Italy). A one-dimensional river modeling is applied over the study reach comparing the results of a classification-derived hydraulic roughness map and a traditional ground-based approach. Despite the complex study reach, the classification method produced encouraging accuracies (OKS = 0.77) and represents a useful tool to delineate application domains of flow resistance models suited to different hydrodynamic patterns (e.g., stiff/flexible vegetation). Hydraulic modeling results showed that the remotely derived floodplain roughness parameterization captures the equivalent Manning coefficient over 20 test cross sections with uncertainty distributions described by low mean and standard deviation values.     

Review of adaptation mechanisms for data-driven soft sensors

In this article, we review and discuss algorithms for adaptive data-driven soft sensing. In order to be able to provide a comprehensive overview of the adaptation techniques, adaptive soft sensing methods are reviewed from the perspective of machine learning theory for adaptive learning systems. In particular, the concept drift theory is exploited to classify the algorithms into three different types, which are: (i) moving windows techniques; (ii) recursive adaptation techniques; and (iii) ensemble-based methods. The most significant algorithms are described in some detail and critically reviewed in this work. We also provide a comprehensive list of publications where adaptive soft sensors were proposed and applied to practical problems. Furthermore in order to enable the comparison of different methods to standard soft sensor applications, a list of publicly available data sets for the development of data-driven soft sensors is presented.     

Controllable fabrication of carbon nanotube-polymer hybrid thin film for strain sensing

A strain sensing material based on carbon nanotubes-polymer hybrid film has been fabricated by the vacuum filtration and poly(dimethylsiloxane) (PDMS) molding transfer methods. The density and thickness of the film is tuned by simply controlling the volume of dilute suspension filtered through the membrane. These composites are resilient under large strain and there is a wide linear range of resistance-strain dependence. We demonstrate that the thin films with thicker CNTs networks exhibit more significant resistance-strain sensitivity under the same stain and the strain sensing material shows reproducible resistance-strain sensitivity depending only on the initial CNTs suspension volume. It may be possible to fabricate strain sensing material in large volume for future smart device applications.