Built-up Density Research Articles

Image Classification for Different Land Use and Land Covers Using Fuzzy Logic for the Improvement of Accuracies

The aim of present research is to classify the satellite images of Ranchi area using fuzzy logic for different land use and land covers. An IRS-LISS III (Linear Imaging Self Scanning Sensor) image has been used for classification. Fuzzy logic is relatively a new theory. Now, fuzzy logic is widely used in the classification of remotely sensed images, for various land use and land cover classes. Classification of images includes pervious and impervious categories. Pervious categories contain mainly standing water bodies, natural vegetation and agricultural lands. Impervious categories contain dense built-up, moderate built-up and low density built-up area. The images of Ranchi area has been classified using standard maximum likelihood (ML) as well as fuzzy techniques using supervised method of classification using ERDAS IMAGINE 9.1. After classification of images, producer’s accuracy, user’s accuracy, overall accuracy and kappa coefficient values have been calculated with the help of confusion / error matrix. Result shows that in pervious category, standing water body exhibits highest accuracy (100%), then natural vegetation and agricultural land exhibits lowest accuracy. Standing water exhibits highest accuracy due to more clear pixels. Among the impervious categories, low density built-up area exhibits highest producer’s accuracy due to small area, dense built-up has second highest and moderate built-up has lowest producer’s accuracies. Comparison among accuracies have been done for both techniques and it is observed that the fuzzy logic is a better classification methodology than the standard ML method because overall accuracy and kappa value are higher for fuzzy classified images.

Built-up and vegetation extraction and density mapping using WorldView-II

This study demonstrates the use of high resolution WorldView-II satellite data in extraction of built-up land and vegetation using normalized index techniques. The PCA 1 and NIR 2 bands-based built-up index was proposed for extracting built-up land, which exhibit high accuracy. The normalized difference vegetation index based on Red Edge and NIR 2 bands of WorldView-II produced high accuracy inthe estimation of vegetation compared to the use of Red and NIR bands. The grid technique used in estimating built-up and vegetation density from precisely classified images provided better and accurate assessment of built-up and vegetation density in heterogeneous landscape of urban areas. This shows areas of very high to high built-up density are located in the central, western and southern parts, which are primarily devoid of vegetation. This study indicates possibilities of utilizing high resolution satellite data in urban landscape characterization using a grid-based technique.

Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area

Remotely sensed imagery is a type of data that is compatible with the monitoring and mapping of changes in built-up and bare land within urban areas as the impacts of population growth and urbanisation increase. The application of currently available remote sensing indices, however, has some limitations with respect to distinguishing built-up and bare land in urban areas. In this study, a new index for transforming remote sensing data for mapping built-up and bare land areas is proposed. The Enhanced Built-Up and Bareness Index (EBBI) is able to map built-up and bare land areas using a single calculation. The EBBI is the first built-up and bare land index that applies near infrared (NIR), short wave infrared (SWIR), and thermal infrared (TIR) channels simultaneously. This new index was applied to distinguish built-up and bare land areas in Denpasar (Bali, Indonesia) and had a high accuracy level when compared to existing indices. The EBBI was more effective at discriminating built-up and bare land areas and at increasing the accuracy of the built-up density percentage than five other indices.

Pattern-Based Accuracy Assessment of an Urban Footprint Classification Using TerraSAR-X Data

Assessing the accuracy of land-cover classifications is a major challenge in remote sensing. This is mostly due to the absence of geometrically and thematically highly resolved, reliable, area wide, and up-to-date reference data. This study focuses on a multifaceted accuracy assessment of an urban footprint classification derived from a single-polarized TerraSAR-X image in stripmap mode for the city of Padang in Indonesia. For this purpose, a pixel-based approach was used to identify the urbanized and nonurbanized areas. As reference, a geometrically and thematically highly resolved, accurate, and detailed 3-D city model is available. Based on this data, the classification result is assessed by basic methodologies-square measures and error matrix. Beyond that, the accuracy of the urban footprint classification is analyzed in dependence of the physical structure of the complex urban landscape-defined by built-up density and building volumes. Results reveal that the accuracy of classification results varies in dependence of the structural characteristics of the particular urban environment. Furthermore, the study shows what is thematically mapped by an urban footprint classification.

Population Growth and Its Expression in Spatial Built-up Patterns: The Sana’a, Yemen Case Study

In light of rapid global urbanisation, monitoring and mapping of urban and population growth is of great importance. Population growth in Sana’a was investigated for this reason. The capital of the Republic of Yemen is a rapidly growing middle sized city where the population doubles almost every ten years. Satellite data from four different sensors were used to explore urban growth in Sana’a between 1989 and 2007, assisted by topographic maps and cadastral vector data. The analysis was conducted by delineating the built-up areas from the various optical satellite data, applying a fuzzy-rule-based composition of anisotropic textural measures and interactive thresholding. The resulting datasets were used to analyse urban growth and changes in built-up density per district, qualitatively as well as quantitatively, using a geographic information system. The built-up area increased by 87 % between 1989 and 2007. Built-up density has increased in all areas, but particularly in the northern and southern suburban districts, also reflecting the natural barrier of surrounding mountain ranges. Based on long-term population figures, geometric population growth was assumed. This hypothesis was used together with census data for 1994 and 2004 to estimate population figures for 1989 and 2007, resulting in overall growth of about 240%. By joining population figures to district boundaries, the spatial patterns of population distribution and growth were examined. Further, urban built-up growth and population changes over time were brought into relation in order to investigate changes in population density per built-up area. Population densities increased in all districts, with the greatest density change in the peripheral areas towards the North. The results reflect the pressure on the city’s infrastructure and natural resources and could contribute to sustainable urban planning in the city of Sana’a.

Urbanization in India – Spatiotemporal analysis using remote sensing data

Urbanization is arguably the most dramatic form of irreversible land transformation. Though urbanization is a worldwide phenomenon, it is especially prevalent in India, where urban areas have experienced an unprecedented rate of growth over the last 30 years. In this uncontrolled situation, city planners lack tools to measure, monitor, and understand urban sprawl processes. Multitemporal remote sensing has become an important data-gathering tool for analysing these changes. By using time-series of Landsat data, we classify urban footprints since the 1970s. This lets us detect temporal and spatial urban sprawl, redensification and urban development in the tremendously growing 12 largest Indian urban agglomerations. A multi-scale analysis aims to identify spatiotemporal urban types. At city level, the combination of absolute parameters (e.g. areal growth or built-up density) and landscape metrics (e.g. SHAPE index) quantitatively characterise the spatial pattern of the cities. Spider charts can display the spatial urban types at three time stages, showing temporal development and helping the reader compare all cities based on normalized scales. In addition, gradient analysis provides insight into location-based spatiotemporal patterns of urbanization. Therefore, we analyse zones defining the urban core versus the urban edges. The study aims to detect similarities and differences in spatial growth in the large Indian urban agglomerations. These cities in the same cultural area range from 2.5 million inhabitants to 20 million (in the metropolitan region of Mumbai). The results paint a characteristic picture of spatial pattern, gradients and landscape metrics, and thus illustrate spatial growth and future modelling of urban development in India.

A conceptual vulnerability and risk framework as outline to identify capabilities of remote sensing

Abstract. This study aims at creating a holistic conceptual approach systematizing the interrelation of (natural) hazards, vulnerability and risk. A general hierarchical risk meta-framework presents potentially affected components of a given system, such as its physical, demographic, social, economic, political or ecological spheres, depending on the particular hazard. Based on this general meta-framework, measurable indicators are specified for the system "urban area" as an example. This framework is used as an outline to identify the capabilities of remote sensing to contribute to the assessment of risk. Various indicators contributing to the outline utilizing diverse remote sensing data and methods are presented. Examples such as built-up density, main infrastructure or population distribution identify the capabilities of remote sensing within the holistic perspective of the framework. It is shown how indexing enables a multilayer analysis of the complex and small-scale urban landscape to take different types of spatial indicators into account to simulate concurrence. The result is an assessment of the spatial distribution of risks within an urban area in the case of an earthquake and its secondary threats, using an inductive method. The results show the principal capabilities of remote sensing to contribute to the identification of physical and demographic aspects of vulnerability, as well as provide indicators for the spatial distribution of natural hazards. Aspects of social, economic or political indicators represent limitations of remote sensing for an assessment complying with the holistic risk framework.

Spatio-temporal analysis for monitoring urban growth – a case study of Indore City

Urban sprawl is characterized by haphazard patchwork of development, which leads to an improper development in any city. To prevent this kind of sprawl in future, it is necessary to monitor the growth of the city. Hence, an attempt has been made in the present study to monitor the urban growth over a period of time by employing Remote Sensing and Geographic Information System techniques in conjunction with Shannon entropy. Shannon entropy is a measure to determine the compactness or dispersion of built-up land growth in the urban areas. The growth patterns of urban built-up land have been studied initially by dividing the area into four zones. The observations have been made with respect to each zone. Then, the study area has been divided into concentric circles of 1 km buffers and the growth patterns have been studied based on urban built-up density with respect to each circular buffer in all four zones. These observations have been integrated with road network to check the influence of infrastructure on haphazard urban growth. It has been found from the study that Shannon entropy is a good measure to determine the spatial concentration or dispersion of built-up land in the city. The study also proved the potential of RS and GIS techniques in the spatio-temporal analysis of urban growth trends and their consequences in the lands adjoining to urban areas.

Urban Air Pollution Patterns, Land Use, and Thermal Landscape: An Examination of the Linkage Using GIS

This article investigates the relationship of local air pollution pattern with urban land use and with urban thermal landscape using a GIS approach. Ambient air quality measurements for sulfur dioxide, nitrogen oxide, carbon monoxide, total suspended particles, and dust level were obtained for Guangzhou City in South China between 1981 and 2000. Landsat TM images and aerial photo derived maps were used to examine city's land use and land cover at different times and changes. Landsat thermal infrared data were employed to compute land surface temperatures and to assess urban thermal patterns. Relationships among the spatial patterns of air pollution, land use, and thermal landscape were sought through GIS and correlation analyses. Results show that the spatial patterns of air pollutants probed were positively correlated with urban built-up density, and with satellite derived land surface temperature values, particularly with measurements taken during the summer. It is suggested that further studies investigate the mechanisms of this linkage, and that remote sensing of air pollution delves into how the energy interacts with the atmosphere and the environment and how sensors see pollutants. Thermal infrared imagery could play a unique role in monitoring and modeling atmospheric pollution.

Lasing from GaAs Gunn devices

Lasing from a GaAs Gunn device with a Fabry–Perot (F–P) cavity and a vertical-cavity structures are demonstrated. Light output-electric field characteristic of both F–P and vertical cavity structure Gunn device shows abrupt increase in light output above a negative differential resistance (NDR) threshold, implying cavity gain overcoming over loss. Electroluminescence (EL) spectra from the F–P Gunn device measured at 95 K show a significant spectral narrowing with increasing the applied voltage pulse width, indicative of monochromatic stimulated emission due to band-to-band recombination of impact-ionized carriers. The spectra from the vertical-cavity device are in accord with the cavity resonance as defined by the distributed Bragg reflectances (DBRs). The theoretical model on carrier generation via impact ionization inside the propagating space charge domains shows clearly that the built-up excess carrier density in the cavity indeed reaches the lasing threshold.

Studying town morphology through Remote Sensing

Remotely-sensed data products have got unique advantage over conventional data-gathering techniques in the study of urban morphology. The physical parameters like built-up area density, street pattern, population density, urban structure as well as functional characteristics which can be derived from land use/land cover map, are clearly visible on aerial data products. This technique provides synoptic view of the area which makes the study comprehensive and uniform. Sequential aerial photographs and satellite imagery help in studying the growth of urban area and temporal changes in urban structure. These informations are very useful in the planning of city extension. Here an attempt has been made to study the urban morphology of Saharanpur city by using panchromatic aerial photographs on scale 1∶10,000, IRS-1B LISS II geocoded imagery on 1∶50,000 scale and photo-maps on 1∶4000 scale, and the results are very encouraging.

Immigration and population growth in early fourteenth-century Norwich: evidence from the tithing roll

Estimates of urban population before the Black Death have been hampered by a lack of suitable data. Although it is common knowledge that many towns reached a physical size in the late thirteenth and early fourteenth centuries that was not exceeded until the early-modern period, little definite is known about the gross urban population at this date. Where attempts have been made to estimate urban populations these have traditionally depended on multiplying from one arbitrary sector of the community, such as property owners, taxpayers or freemen, or on back projections from the late-fourteenth-century poll tax returns. The problem with such an approach is not only that there is little independent basis for the multipliers used, but also that the relationship between any such arbitrary sector and the urban population as a whole may alter from town to town as well as from time to time within the same community. An exception to this approach has been the recent work of Derek Keene, who has used other indicators of population size and pressure, namely land values, the extent of the built-up area and density of settlement, to estimate the early-fourteenth-century populations of Winchester and London. It may be significant that this method has produced considerably higher population figures than had been reached by the more traditional approach. In this uncertainty, any source that purports to record the whole of a demographically defined section of the population is of considerable interest. Norwich is fortunate in the survival of such a source in an early-fourteenth-century tithing roll for the leet of Nedham and Mancroft (hereafter Mancroft), one of the four leets of the city.