Modeling Land-Cover Types Using Multiple Endmember Spectral Mixture Analysis in a Desert City

Spectral mixture analysis is probably the most commonly used approach among sub-pixel analysis techniques. This method models pixel spectra as a linear combination of spectral signatures from two or more ground components. However, spectral mixture analysis does not account for the absence of one of the surface features or spectral variation within pure materials since it utilizes an invariable set of surface features. Multiple endmember spectral mixture analysis (MESMA), which addresses these issues by allowing endmembers to vary on a per pixel basis, was employed in this study to model Landsat ETM+ reflectance in the Phoenix metropolitan area. Image endmember spectra of vegetation, soils, and impervious surfaces were collected with the use of a fine resolution Quickbird image and the pixel purity index. This study employed 204 (=3x17x4) total four-endmember models for the urban subset and 96 (=6x6x2x4) total five-endmember models for the non-urban subset to identify fractions of soil, impervious surface, vegetation, and shade. The Pearson correlation between the fraction outputs from MESMA and reference data from Quickbird 60 cm resolution data for soil, impervious, and vegetation were 0.8030, 0.8632, and 0.8496 respectively. Results from this study suggest that the MESMA approach is effective in mapping urban land covers in desert cities at sub- pixel level.

Investigation and prediction of urban-sprawl and land-use changes for Chennai city using geo-spatial technologies

1443-1451Monitoring urban sprawl is a vital component to assess landscape changes as it directly affects the quality of life. Multi date land-use and land-cover thematic layers were generated using multi-date high resolution remote-sensing data and Survey of India topo-sheet and spatial changes in urban land-use and urban-sprawl were studied using GIS. The residential and commercial urban area of city increased from 14,865.8 and 2,121.27 hectares in 1991 to 35,564 and 3,527.34 hectares in 2014. This study revealed that 51% of agricultural land and 2% of water bodies have been transformed as other urban land use features, in the form of built-ups. Based on current landscape trends, a 29-year forward simulation for the years 1991 to 2020 was performed using GIS land use change modeller analysis tool. The results show that by 2020 the residential and commercial urban of Chennai would increase to 51,059 and 4,246.7 hectares, respectively

Un panorama de la télédétection de l'étalement urbain

The objective of this review paper is to provide an overview of remote sensing based research tackling urban sprawl issue. 113 articles were indexed and analyzed after research on bibliographical databases. These 113 articles are presented in the form of summary table giving highlights of the listed publications. Articles are divided into 6 categories (F, A, B, C, D, E) according to whether they are articles of methodology, characterization, prospective modeling-simulation, retrospective modeling-simulation, analysis of impacts or monitoring of urban sprawl. The summary table is conceived as a tool which can help researchers interested by the measurement and the analysis of urban sprawl.Cette note rend compte d'une recherche bibliographique dont l'objectif est de fournir un panorama des recherches utilisant la télédétection pour aborder la problématique de l'étalement urbain. 113 articles ont été répertoriés et analysés à la suite de recherches dans des bases de données bibliographiques. Ces 113 articles sont présentés sous forme de tableau récapitulatif donnant un aperçu général des publications recensées. Les articles sont répartis en 6 catégories (F, A, B, C, D, E) suivant qu'il s'agit d'articles de méthodologie, de caractérisation, de modélisation-simulation prospective, de modélisation-simulation rétrospective, d'analyse d'impacts ou de monitorage de l'étalement urbain. Le panorama est conçu comme un outil d'aide aux chercheurs qui s'intéressent à la mesure et à l'analyse de l'étalement urbain

Land Cover Change Detection in the Urban Catchments of Dar es Salaam, Tanzania using Remote Sensing and GIS Techniques

In this study, the Maximum Likelihood (ML) classification, Normalized Difference Vegetation Index (NDVI) and Artificial Neural Network (ANN) methods were applied to three (3) Landsat images collected over time (1979, 1998 and 2014), that contained historical land cover features for the urban catchments of Dar es Salaam. Five major land cover classes were identified, mapped, and the land cover changes investigated. The major land cover changes observed from post-classification comparisons of the classified images are: the forest land losing 17.09% of its area in the period 1979-1998 to other land covers, mainly turning to grassland, and from 1998 to 2014, 17.55% of the total study area turned to high and medium/low-density built-up areas. Growth in urban settlement and infrastructure was observed to be continuously increasing and the high and medium/low-density built-up areas are projected to cover 66.09% of the total area by 2030; this is an increment of 29.01% from 37.08% coverage in 2014. This shift in land cover was further validated by the results of the Normalized Difference Vegetation Index (NDVI) analysis which showed a similar trend (shift from thick vegetation towards barren land) from 1998 to 2014, with median NDVI values changing from 0.52 to 0.36 respectively. These land cover changes are most likely the results of activities related to the increase in total population, the influx of urban population and the growth of the economy.Keywords: Maximum Likelihood, NDVI, Artificial Neural Network, Landsat, QGIS

ASSESSING THE SPATIAL DIFFERENCES AMONG SOME URBAN EXPANSION DRIVING FORCES IN CONSTANȚA METROPOLITAN AREA (ROMANIA)

The urban expansion process has become a major challenge for the scientists and urban planners, mainly due to its various spatial and functional expressions. The present study aims to identify the spatial patterns of several driving forces of this phenomenon in Constanta metropolitan area. In order to reach our purpose, multi-temporal remote sensing data was employed; by image processing, we obtained the built-up area for 2001 and 2013. Subsequently, a Geographically Weighted Regression model was employed to explore the relationship between the newly-built-up area and four driving forces related to accessibility: distance to the shoreline, distance to the existing built-up area, distance to the road network and distance to Constanta city center. By studying the spatial distribution of the regression coefficients for the explanatory variables we mapped the relevance of each driving force, leading to the general conclusion that the strongest driving forces are the distance to the built-up area and the distance to the road network. At the same time, the spatial distribution of the correlation coefficient and of the residuals revealed that the model best fits the axial and suburban areas, while the internal, consolidated areas record highly deviated residuals

Online Εvaluation of Earth Observation Derived Indicators for Urban Planning and Management

Extensive urbanization and growth of population density have acquired a paramount interest towards a sustainable urban development. Earth Observation (EO) is an important source of information required for urban planning and management. The availability of EO data provides the immense opportunity for urban environmental indicators development easily derived by remote sensors. In this study, the state of the art methods were employed to develop urban planning and management relevant indicators that can be evaluated by using EO data. The importance of this approach lies on providing alternatives for improving urban planning and management, without consuming time and resources in collecting field or archived data. The evaluated urban indicators were integrated into a Web‐based Information System that was developed for online exploitation. The results for three case studies are therefore available online and can be used by urban planners and stakeholders in supporting their planning decisions

Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods

Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post-classification steps. Within this chapter, each of the four approaches is described in terms of scale and accuracy classifying urban land use and urban land cover; and for its range of urban applications. We demonstrate the overview of four main classification groups in Figure 1 while Table 1 details the approaches with respect to classification requirements and procedures (e.g., reflectance conversion, steps before training sample selection, training samples, spatial approaches commonly used, classifiers, primary inputs for classification, output structures, number of output layers, and accuracy assessment). The chapter concludes with a brief summary of the methods reviewed and the challenges that remain in developing new classification methods for improving the efficiency and accuracy of mapping urban areas

Improving distributed runoff prediction in urbanized catchments with remote sensing based estimates of impervious surface cover

The amount and intensity of runoff on catchment scale are strongly determined by the presence of impervious land-cover types, which are the predominant cover types in urbanized areas. This paper examines the impact of different methods for estimating impervious surface cover on the prediction of peak discharges, as determined by a fully distributed rainfall-runoff model (WetSpa), for the upper part of the Woluwe River catchment in the southeastern part of Brussels. The study shows that detailed information on the spatial distribution of impervious surfaces, as obtained from remotely sensed data, produces substantially different estimates of peak discharges than traditional approaches based on expert judgment of average imperviousness for different types of urban land use. The study also demonstrates that sub-pixel estimation of imperviousness may be a useful alternative for more expensive high-resolution mapping for rainfall-runoff modelling at catchment scale

Effects of rapid urbanisation on the urban thermal environment between 1990 and 2011 in Dhaka Megacity, Bangladesh

This study investigates the influence of land-use/land-cover (LULC) change on land surface temperature (LST) in Dhaka Megacity, Bangladesh during a period of rapid urbanisation. LST was derived from Landsat 5 TM scenes captured in 1990, 2000 and 2011 and compared to contemporaneous LULC maps. We compared index-based and linear spectral mixture analysis (LSMA) techniques for modelling LST. LSMA derived biophysical parameters corresponded more strongly to LST than those produced using index-based parameters. Results indicated that vegetation and water surfaces had relatively stable LST but it increased by around 2 °C when these surfaces were converted to built-up areas with extensive impervious surfaces. Knowledge of the expected change in LST when one land-cover is converted to another can inform land planners of the potential impact of future changes and urges the development of better management strategies

Use of Earth observation for monitoring soil sealing trends in Flanders and Brussels between 1976 and 2013

The on-going growth of urban area in Flanders and in the Brussels Capital Region over the past decades has resulted in a highly sprawled urban tissue, consisting of large and smaller urban agglomerations, connected by a well-developed transportation network. The conversion of open land to urban area is accompanied by an increase in soil sealing, affecting the hydrological cycle and the urban climate. Despite a growing interest in monitoring the process of soil sealing in urban areas, to date no detailed information on the presence and evolution of sealed surfaces is available for Flanders. In this paper a linear regression unmixing approach is proposed to map and monitor changes of sealed surface cover at the regional scale, using medium as well as high resolution remote sensing data. Applied to Flanders and the Brussels Capital Region, a total sealed area of 2687 km² for 2013 is found, corresponding to an increase of 82% since 1976. Residential areas account for nearly half of the sealed area and show the largest increase in sealed surface cover over the past 37 years.De toenemende verstedelijking van Vlaanderen en het Brussels Hoofdstedelijk Gewest in de laatste decennia heeft geleid tot een sterk gefragmenteerde stedelijke ruimte die zich heeft ontwikkeld rond grote en kleinere bebouwingskernen en langs het dicht vertakte transportnetwerk dat deze kernen verbindt. De conversie van open ruimte naar stedelijk gebied gaat gepaard met een toenemende afdichting van de bodem met verharde oppervlakken. Deze afdichting heeft een impact heeft op de hydrologische cyclus en het klimaat van verstedelijkte zones. Ondanks de groeiende aandacht voor het opvolgen van bodemafdichting in verstedelijkte gebieden is op dit moment geen gedetailleerde informatie omtrent de evolutie van bodemverharding in Vlaanderen beschikbaar. In dit artikel wordt een methode voorgesteld om bodemverharding en veranderingen in verharding doorheen de tijd op regionale schaal in kaart te brengen, gebruik makend van satellietdata. De methode is gebaseerd op spectrale ontmenging van medium resolutie satellietdata, en gebruikt gedetailleerd, hoge resolutie beeldmateriaal om een op lineaire regressie gebaseerd ontmengingsmodel te calibreren en valideren. Toepassing van de methode op Vlaanderen en het Brussels Hoofdstedelijk Gewest resulteert voor 2013 in een totale verharde oppervlakte van 2687 km2, wat overeenstemt met een toename van 82% sinds 1976. Bijna de helft van de verharde oppervlakte situeert zich in residentiële gebieden, die vergeleken met andere landgebruiken ook de grootste toename in verharding kennen