This act, combined with the growing popularity of the automobile, cheap land and electricity, and changing national attitudes toward gambling, led to the fantastical casinos and opulent resorts that became the trademark industry of the city and created the ambiance that has made Las Vegas an international icon of pleasure and entertainment. Eugene Moehring and Michael Green have studied and written about Las Vegas for many years, and in Las Vegas: A Centennial History they offer a detailed and deeply knowledgeable account of the growth of this unique city, the impact of politics and of wars, and the city's struggle to establish diversified economy.
Here are the larger-than-life characters who shaped the city, as well as the business and civic decisions. The authors' scope extends chronologically from the first Paiute people who settled around the artesian springs that gave the city its name, right up to the construction of the latest megaresort, and geographically far beyond the original township to include the several municipalities that make up the metropolitan Las Vegas area.
They consider various aspects of city building such as the role of developers; the creation of infrastructure, services, and transportation; the struggle to obtain a reliable source of water; the function of cultural, civic, educational, and religious institutions; and of ethnic minorities. Las Vegas: A Centennial History celebrates the city's unparalleled growth in the brief century of its existence.
It also offers fresh insight into the process of city building in the American West, where urban needs and aspirations must contend with water scarcity, isolation, erratic economies, highly diverse populations, and the rocky relationship between the need for civic order and the Western spirit of independence. The farmlands of Southern Louisiana bright green contrast sharply with the local wetlands in this Thematic Mapper scene.
Geological Survey. Las Vegas, Nevada. NASA Photograph. Cape Canaveral, the launch site for America's space exploration programs.
Towards the northeast corner is Launch Pad 39A. Originally designed to support the Apollo program, it was later modified for Space Shuttle launches. Flooding from the storm destroyed 7, homes and caused severe agricultural damage throughout the eastern portion of the state. This sequence of images, collected by the Landsat Thematic Mapper, illustrates the physical effects of flooding, as seen along the Missouri River in the fall of During the flood, the river's boundaries are extended, creating wetland areas beyond the active river channel.
One month later, water-saturated land can still be seen in the dark blues, blue-grays, and olive greens of the post-flood image. The white to light gray areas reveal the presence of overlying sand deposits. The Missouri River in the fall of This Landsat TM image shows the river before the flood.
This Landsat TM image shows the river at the peak of the flood. This Landsat TM image shows the river after the flood. These Landsat scenes vividly illustrate how much damage gypsy moths can inflict on forested regions. The image on the top is a view of an area in Pennsylvania in May The red color represents healthy vegetation. The picture on the bottom is from July when the gypsy moths have grown to full size. Note how much vegetation has been removed. Courtesy of D.
- Professor David Sanderson;
- A Review of Health Sector Aid Financing to Somalia (World Bank Working Papers) (World Bank Working Papers; Africa Human Development).
- Air survey of sand deposits by spectral luminance!
Williams, Goddard Space Flight Center. This scene is from May This scene is from July This Landsat image shows cornfields in Kansas planted using a circular irrigation pattern. The blue areas indicate healthy corn while the pink represents areas damaged by lack of water. Courtesy of Patricia Jaccobberger-Jellison. Landsat images have been used to map sites of possible archaeological interest.
Study of this image helped scientists to locate the lost city of Ubar in Southern Oman, on the Arabian Peninsula. Courtesy of Jet Propulsion Laboratory. Satellites like Landsat can have sensors which "see" in visible light, but they may also be able to record radiation such as infrared that it is beyond our capability to see. Visible light is only one kind of electromagnetic radiation that satellites can monitor. Infrared and radar are also part of the electromagnetic spectrum, and each represents radiation in a different wavelength.
For example, yellow light has a longer wavelength than blue, and red is longer than yellow. Infrared and radar wavelengths are longer still. By collecting data in different regions of the spectrum, satellites can reveal information that would go undetected by our eyes alone. The same scene can appear very different when viewed by different satellite sensors. The utilization of a two-dimensional imaging detector allows for recording interferograms at adjacent locations simultaneously. Integrated in a six-unit CubeSat, the instrument is designed for limb sounding of the atmosphere.
The agility of a CubeSat will be used to sweep the line-of-sight through specific regions of interest to derive a three-dimensional image of an atmospheric volume using tomographic reconstruction techniques. Adaptive pulse edge detection algorithm based on short-time Fourier transforms and difference of box filter. Precise radar pulse detection is of great significance for estimating parameters in electronic countermeasure and reconnaissance. First, STFT with the Gaussian window is used to acquire the time-frequency spectrum of the radar pulse signal.
Second, in order to determine the existence of the pulse, CFAR detector is introduced into the frequency domain to generate an adaptive threshold, and then the rough pulse edges are obtained by m n method. The proposed algorithm is processed in the time-frequency domain, which cannot only adapt to low signal-to-noise ratio, but also has a high measurement accuracy.
We also draw parallels to the conventional energy-based detection method, the results validate that the proposed algorithm is more robust and effective in practice. Simulations via various noisy input pulse data demonstrate the viability and validity of our proposed algorithm. The algorithm has been implemented in a spaceborne radar receiver. Spatial—temporal landscape pattern change under rapid urbanization. Junmei Tang , Liping Di , Md. Shahinoor Rahman , Zhiqi Yu. Rapid urbanization has been an important social and economic phenomenon in the last 50 years.
Our study analyzes the spatial—temporal landscape pattern in the National Capital Region NCR of Delhi, one of the most rapid urbanization areas in the world. Delhi metropolitan area and its surrounding satellite cities exhibit a soaring rate of landscape pattern change during the last two decades. A set of landscape metrics with supplementary ecological meaning was chosen to study the changes of landscape pattern in NCR. The results indicate that the rapid urbanization has brought enormous landscape changes in NCR, and consequently, substantial impacts on its landscape pattern. Meanwhile, the landscape pattern is fragmented into a more heterogeneous pattern in both farmland and urban landscape with more irregularly shaped patches during urbanization.
Our research confirms the effectiveness and applicability of a combination of remote sensing, geographic information systems, and landscape metrics in revealing spatial—temporal of landscape change throughout rapid growth periods.
Systematic preparation and processing of interferometric synthetic aperture radar data for monitoring linear transportation infrastructure. We propose a methodology for the systematic preparation and processing of interferometric synthetic aperture radar InSAR data for monitoring linear transportation infrastructure subject to geohazards. Phase unwrapping errors, atmospheric path delay, and the limited number of images were identified as the largest contributors to measurement uncertainty, which was of the same order as the ground deformation field.
To improve coherence and utility of the radar images for monitoring the effects of geohazards on infrastructure, it is recommended that imagery acquisitions consider the use of small incidence angles with moderate image resolution and 6- to day revisit periods. Exploring a combined multispectral multitemporal approach as an effective method to retrieve cloudless multispectral imagery. The increasing availability of satellite information has improved Earth observation applications globally.
However, primary satellite information is not as immediate as desirable. Indeed, besides the geometric and atmospheric limitations, clouds, cloud shadows, and haze generally contaminate optical imagery. Actually, such a contamination is intended as missing information and should be replaced. However, because the most common cloud masking algorithms take advantage by employing thermal images, here the objective is to provide an alternative algorithm suitable for multispectral imagery only.
A multitemporal stack, for the same image scene, is employed to retrieve a composite uncontaminated image over 1 year. The approach relies on a clouds and cloud shadows masking step, based on spectral features, a band-by-band multitemporal effect adjustment to avoid significant seasonal variations, and a data reconstruction phase based on automatic selection of the most suitable pixels from the stack.
Seabed Prehistory | Our Work | Wessex Archaeology
Results have been compared with a recognized masking algorithm approach and tested with uncontaminated image samples for the same scene. Accuracy and spectral features of the results provide high consistency. Combining iterative slow feature analysis and deep feature learning for change detection in high-resolution remote sensing images. In order to make full use of local neighborhood information for high-resolution remote sensing images, this study combined iterative slow feature analysis ISFA and stacked denoising autoencoder SDAE to improve the change detection precision.
First, this approach introduced ISFA for initial change detection in an unsupervised way, which enlarged the separability of changed and unchanged areas. Then, by setting different membership degrees, the changed and unchanged samples were obtained through fuzzy-means clustering. Finally, the change model was built by SDAE to represent the local neighborhood features deeply, and the change detection result can be obtained after all the samples were fed into the model. Experiments were performed on three real datasets, and the results validated the effectiveness and superiority of the proposed approach.
Polarimetric synthetic aperture radar speckle filtering by multiscale edge detection. To reduce the speckle, a polarimetric filtering is necessary to improve the image quality. The purpose of PolSAR filtering is to use the polarimetric information in the different channels to develop an efficient algorithm adapted to this data type, to reduce well the speckle and preserve the contained information.
We present the PolSAR wavelet filtering applying the stationary wavelet transform: filtering by multiscale edge detection with two improvement techniques of wavelet coefficients, filtering by wavelet thresholding using the hard and soft thresholding and their two enhanced versions. Our contribution is based on the adaptation of wavelet thresholding to PolSAR data and on improvement techniques to filter polarimetric covariance or coherency matrix elements and span.
Ebooks portugues kostenloser Download Las Vegas : A Centennial History auf Deutsch PDF FB2 iBook
We evaluate the performance of each filter based on the following criteria: smoothing homogeneous areas, preserving contours, and polarimetric information. Experimental results and a comparative study are included. Deep learning-based method for reconstructing three-dimensional building cadastre models from aerial images. Mehdi Khoshboresh Masouleh , Saeid Sadeghian.
The purpose of our study is to reconstruct three-dimensional 3-D building cadastre models 3DBCMs with an approach to improve the state of land administration in Tehran metropolis. Our study is being implemented and evaluated in three stages. The first stage involves collecting aerial images.
The interior and exterior orientation parameters are preprepared in this step. The second stage involves automatic interpretation and extraction of buildings from aerial images by providing a method of interpretation called fully automatic interpretation with deep learning FAIDL. The third stage involves 3-D building modeling and evaluating the effect of FAIDL method on the automatic interpretation of images. Analyses of satellite ocean color retrievals show advantage of neural network approaches and algorithms that avoid deep blue bands.
We now extend NN retrievals well beyond the WFS, to include both complex coastal and open ocean waters along the Florida and Atlantic coasts with a large dynamic range of chlorophyll- a values. Most importantly, we add in situ radiometric measurements which in contrast to satellite retrievals, are invulnerable to atmospheric transmission correction errors as inputs to retrieval algorithms, permitting algorithm comparisons for in situ and simultaneous colocated satellite retrievals against sample measurements.
Results unequivocally demonstrate the intrinsic efficacy and unfettered applicability of NN algorithms in widely varying waters beyond the WFS. Furthermore, they show that avoiding deep blue bands in retrieval algorithms significantly improves accuracies. Likely, rationales are that longer wavelengths used with NN are less vulnerable to atmospheric transmission correction errors and to spectral interference by colored dissolved organic matter and nonalgal particles in more complex waters than deeper blue wavelengths used with other algorithms , thereby arguing for development of OC algorithms using longer wavelengths.
Finally, quantitative analysis of temporal, intrapixel, and sample depth variabilities highlights their important impact on retrieval accuracies. As a fundamental prerequisite for a variety of location-based services, indoor location information has received increasing attention in recent years.
Under the line-of-sight condition, the positioning accuracy of the indoor positioning technology based on ultrawideband UWB is acceptable for many applications, but under the non-line-of-sight condition, it degrades dramatically. The positioning accuracy can be significantly improved by the fusion of inertial measurement units and UWB sensors based on the extended Kalman filter EKF algorithm.
However, when UWB measurements are affected by large non-Gaussian noise, the assumption of the EKF algorithm that observations are subject to Gaussian distribution for noise is invalid. Although the non-Gaussian noise can be handled by the robust EKF algorithm, this algorithm only uses the prior information to judge the reliability of the observations, and the positioning result is not stable when the number of beacons is small.
To solve this problem, a method for successive updating of the covariance and posterior state of the observations in iterations based on an iterated extended Kalman filter IEKF is proposed. The marginal distribution of the posterior distribution is constructed and iteratively optimized, inhibiting the effect of non-Gaussian noise on UWB under a complex environment. The positioning results of the proposed method, the standard EKF algorithm, and the robust EKF algorithm, using different numbers of beacons, are compared.
The results show that the positioning accuracy of the proposed algorithm is the highest under all scenarios. The proposed algorithm shows the smallest decrease in accuracy and presents the most stable positioning when the number of beacons is small, which is a common situation in practical applications.
Soil moisture estimation with a remotely sensed dry edge determination based on the land surface temperature-vegetation index method. Jinfeng Yang , Dianjun Zhang. As a crucial parameter in land surface systems, soil moisture plays an important role in surface energy balance studies, environmental detection, and global climate change research.
Remotely sensed data have been used for estimating soil moisture through different approaches, which has resulted in many achievements. Previous studies showed that the land surface temperature LST vegetation index method LST-VI method can obtain surface soil moisture with remote sensing sources, and it is relatively simple and easy to operate at a regional scale. In this study, a remote sensing method is proposed to determine the theoretical dry edge from the LST-VI scatter plots, which do not require any ground measured auxiliary data.
The air temperature is parameterized by the LST using a semiempirical formula as the theoretical wet edge. The estimated soil moisture is validated by in situ measurements at a comprehensive weather station of Yucheng. The relevant key parameters in determining the dry edge are also validated from the meteorological observation. The air temperature and net surface shortwave radiation flux all reach a very high level, with an RMSE of 3. The results demonstrated that the proposed method can derive the accurate dry edge to estimate soil moisture from the remote sensing data, which will provide great help for future studies of soil moisture estimation using remote sensing techniques.
Unsupervised change detection method based on saliency analysis and convolutional neural network. Daifeng Peng , Haiyan Guan. Due to great advantages in deep features representation and classification for image data, deep learning is becoming increasingly popular for change detection CD in the remote-sensing community. An unsupervised CD method is proposed by combining deep features representation, saliency detection, and convolutional neural network CNN.
First, bitemporal images are fed into the pretrained CNN model for deep features extraction and difference image generation. Second, multiscale saliency detection is adopted to implement the uncertainty analysis for the difference image, where image pixels can be categorized into three classes: changed, unchanged, and uncertain.
Then, a flexible CNN model is constructed and trained using the interested changed and unchanged pixels, and the change type of the uncertain pixels can be determined by the CNN model. Finally, object-based refinement and multiscale fusion strategies are utilized to generate the final change map. The effectiveness and reliability of our CD method are verified on three very high-resolution datasets, and the experimental results show that our proposed approach outperforms the other state-of-the-art CD methods in terms of five quantitative metrics. Planning lunar observations for satellite missions in low-Earth orbit.
Truman Wilson , Xiaoxiong Xiong. For Earth-observing satellites in low-Earth orbit, radiometric calibration of the sensors on-orbit is critical for maintaining consistent Earth-view EV retrievals as the mission progresses. Many of these satellite instruments use on-board calibration targets, EV sites, and observations of celestial targets in order to perform the sensor characterization. Among the celestial targets, the Moon is widely used across a range of Earth-observing instruments in order to perform radiometric calibration, spatial characterization, and sensor intercomparison.
Since many of these instruments use satellite maneuvers in order to bring the Moon into view at a desired time, calculating the time and geometric parameters of the observations is vital for mission planning purposes. Given a set of satellite orbital data along with a definition of the instrument coordinates, the tool is designed to provide the timing of observations for an arbitrary view-port direction and a maneuver along an arbitrary axis relative to the spacecraft. The tool can be tested versus known lunar observations for the Aqua and Terra moderate resolution imaging spectroradiometer and the Suomi-NPP and NOAA visible infrared imaging radiometer suite instruments for both roll and pitch maneuvers.
We also perform simulations of lunar observations for different instrument configurations, orbits, and maneuver types in order to analyze the change in the potential lunar observations. Finally, we show a simple extension of the tool which can be used for identifying planet and star observations. Building change detection from remotely sensed images based on spatial domain analysis and Markov random field. With the rapid development of urban areas, construction areas are constantly appearing.
Those changed areas require timely monitoring to provide up-to-date information for urban planning and mapping. As a result, it is a challenge to develop an effective change detection technique. In this work, a method for detecting building changes from multitemporal high-resolution aerial images is proposed. Different from traditional methods, which usually depict building changes in the color domain e. Moreover, contextual relations are explored as well, in order to achieve a robust detection result.
In detail, corners are first extracted from the image and an irregular Markov random field model is then constructed based on them. Energy terms in the model are appropriately designed for describing the geometric characteristics of the building. Change detection is treated as a classification process, so that the optimal solution indicates corners belonging to changed buildings. Finally, changed areas are illustrated by linking preserved corners followed by postprocessing steps.
Experimental results demonstrate the capabilities of the proposed method for change detection. Anthropogenic subsidence along railway and road infrastructures in Northern Italy highlighted by Cosmo-SkyMed satellite data. We use X-band Cosmo-SkyMed InSAR data to highlight several subsidence phenomena resting on some railway and road infrastructures in Lombardia region, Northern Italy, mainly induced by anthropogenic activities. The geological features of this part of Italy and the large presence of industrial areas in the surrounding of Milano, Lecco, and Como cities lead to such phenomena.
The stability and security of the nested road and railway network could be affected by these surface deformation fields. To guarantee the safety of people, continuous maintenance of the condition of railways and roads together with the monitoring of the conditions of the lands on which they rest on should be done. The downscaled product was compared against a unique highly spatially resolved ground-level ambient air temperature dataset collected through the New York City Community Air Survey NYCCAS , a neighborhood level air pollution and temperature monitoring network, for the years and Overall, the downscaled daily minimum temperature was well correlated with ground station data, with NYCCAS minimum temperatures being slightly higher.
Minimum temperature R 2 values were 0. The smallest differences between NYCCAS and the downscaled data were seen at lower temperatures, in less densely urbanized areas, and in areas with higher vegetative cover, suggesting systematic bias in the downscaled data related to land-use. The 1-km dataset discerned neighborhood level temperature differences in high-density urban situations with heterogeneous land cover.
Mineral discrimination by combination of multispectral image and surrounding hyperspectral image. Akihiro Hirai , Hideyuki Tonooka. A hyperspectral HS imager is more effective than a multispectral MS imager in mineral discrimination, but spatial coverage of HS images is limited in comparison to that of MS images. Thus Kruse and Perry have proposed a method that uses coincident HS imaging and MS imaging data to extend mineral mapping to larger areas. Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index.
Remote sensing technique often analyzes the thermal characteristics of any area. Our study focuses on estimating land surface temperature LST of Raipur City, emphasizing the urban heat island UHI and non-UHI inside the city boundary and the relationships of LST with four spectral indices normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index. The entire study is performed on 11 multidate Landsat 8 OLI and TIRS images taken from four different seasons; premonsoon, monsoon, postmonsoon, and winter, in a single-year time period.
The results show that the UHI zones are mainly developed along the northern and southern portions of the city. The common area of UHI for four different seasons is developed mainly in the northwestern parts of the city, and the value of LST in the common UHI area varies from Moreover, the strongest regression between LST and these spectral indices is observed in monsoon and postmonsoon seasons, whereas winter and premonsoon seasons revealed comparatively weak regression. The results also indicate that landscape heterogeneity reduces the reliability of the regression between LST with these spectral indices.
Agricultural drought monitoring based on soil moisture derived from the optical trapezoid model in Mozambique. The OPTRAM was implemented using satellite data from Sentinel-2 and was validated against field SM assessed by gravimetric methods and by Watermark Sensors in sandy-soils with very low water holding capacity 0. The results indicate that OPTRAM can provide useful information to improve water productivity in cropland under the specific conditions of Mozambique agricultural systems and for early warning systems development.
Stress—strain analysis by genetic algorithm-based integration of long-term subsidence time series from different synthetic aperture radar platforms in Darab, Iran. The potential of synthetic aperture radar SAR interferometry was shown to study the compaction of the aquifer system in Darab plain, Iran. In so doing, two different datasets, including Envisat advanced SAR ASAR spanning and Sentinel-1A spanning to , were applied in small baseline subset time series analysis.
To estimate the subsidence in the time period for which there is no SAR data available, i. However, as both deformation time series results were calculated taking into account a distinct temporal reference, fitting the model was not a straightforward task. Accordingly, the main attempt was to find the subsidence value corresponding to the temporal reference of Sentinel-1A time series with respect to that of Envisat ASAR.
This shift value was optimally determined using a genetic algorithm so as to minimize the misfit between the model and the deformation time series corresponding to the entire period. The average value of the root mean square error estimated as the misfit between the model and the calculated time series at all pixels is 0.
The integration results were further used to derive the stress—strain relationships to study the storage properties of the aquifer system. The fact that the strain linearly increases along with the decrease in water level in most piezometric wells indicates that the subsidence is highly correlated with groundwater exploitation.