P. K. Garg

Monitoring and modelling of urban sprawl using remote sensing and GIS techniques

Abstract The concentration of people in densely populated urban areas, especially in developing countries, calls for the use of monitoring systems like remote sensing. Such systems along with spatial analysis techniques like digital image processing and geographical information system (GIS) can be used for the monitoring and planning purposes as these enable the reporting of overall sprawl at a detailed level. In the present work, urban sprawl of the Ajmer city (situated in Rajasthan State of India) has been studied at a mid scale level, over a period of 25 years (1977–2002), to extract the information related to sprawl, area of impervious surfaces and their spatial and temporal variability. Statistical classification approaches have been used for the classification of the remotely sensed images obtained from various sensors viz. Landsat MSS, TM, ETM+ and IRS LISS-III. Urban sprawl and its spatial and temporal characteristics have been derived from the classified satellite images. The Shannons entropy and landscape metrics (patchiness and map density) have been computed in terms of spatial phenomenon, in order to quantify the urban form (impervious area). Further, multivariate statistical techniques have been used to establish the relationship between the urban sprawl and its causative factors. Results reveal that land development (160.8%) in Ajmer is more than three times the population growth (50.1%). Shannons entropy and landscape metrics has revealed the spatial distribution of the urban sprawl over a period of last 25 years.

Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India)

The concentration of people in densely populated urban areas, especially in developing countries like India and China, calls for the use of sophisticated monitoring systems, like remote sensing and Geographical Information Systems (GIS). Time series of land use/cover changes can easily be generated using sequential satellite images, which are required for the prediction of urban growth, verification of growth model outputs, estimation of impervious area, parameterization of various hydrological models, water resources planning and management and environmental studies. In the present work, urban growth of Ajmer city (India) in the last 29 years has been studied at mid‐scale level (5–25 m). Remote sensing and GIS have been used to extract the information related to urban growth, impervious area and its spatial and temporal variation. Statistical classification approaches have been used to derive the land use information from satellite images of eight years (1977–2005). The Shannons entropy and landscape metrics (patchiness and map density) are computed in order to quantify the urban form (impervious area) in terms of spatial phenomena. Further, multivariate statistical techniques have been used to establish the relationship between the urban growth and its causative factors. Results reveal that land development (200%) in Ajmer is more than three times the population growth (59%). Shannons entropy and landscape metrics has revealed the spatial distribution of the sprawl.

Methodological Review: Health GIS and HIV/AIDS studies: Perspective and retrospective

GIS (Geographic Information System) is a useful tool that aids and assists in health research, health education, planning, monitoring and evaluation of health programmes that are meant to control and eradicate certain life threatening diseases and epidemics. HIV/AIDS is one such epidemic that poses a serious challenge and threatens the overall human welfare. This communication is an attempt to link and understand the health scenario in a GIS context with emphasis on HIV/AIDS. Various GIS based functionalities for health studies and their scope in analyzing and controlling epidemiological diseases are explored. Overall scenario of the spread of HIV/AIDS around the world is presented along with the Indian perspective. Finally, we conclude with the general management problems, issues and challenges related to HIV/AIDS prevailing in India.

Remote Sensing and GIS Based Approach for Identification of Artificial Recharge Sites

The main aim of this paper is to demonstrate the capabilities of Remote Sensing (RS) and Geographic Information System (GIS) techniques for the demarcation of suitable sites for artificial recharge of groundwater aquifers, in the Loni watershed, located in Unnao and Raebareli districts, Uttar Pradesh, India. In this study, the SCS-CN model, groundwater depth data and morphological parameters (bifurcation ratio, elongation ratio, drainage density, ruggedness number, relief ratio, and circulatory ratio) have been used to delineate the recharge sites for undertaking water conservation measures. Augmentation of water resource is proposed in the watershed by constructing runoff storage structures, like check dam, percolation tank and nala bund. The site suitability for these water harvesting structures is determined by considering spatially varying parameters, like runoff potential, slope, groundwater fluctuation data and morphometric information of the watershed. GIS has been used as an effective tool to store, analyse and integrate spatial and attribute information pertaining to runoff, slope, drainage, groundwater fluctuation and morphometric characteristics for such studies.

A comparative study of various pixel-based image fusion techniques as applied to an urban environment

In this paper, the use of high-resolution images for identification of urban features through pixel-based image fusion techniques is discussed. Fusion techniques are used to merge high spatial resolution panchromatic (PAN) image with low spatial resolution multispectral (MS) image to enhance the visual quality/appearance of some of the urban features present in the image. In this paper nine pixel-based fusion techniques, viz., Principal Component Analysis (PCA), multiplicative, Brovey transformation, wavelet analysis, subtractive, HPF, modified IHS, Ehlers, and hyperspherical color space, are discussed for the fusion of PAN and MS IKONOS images. The fused images are interpreted on the basis of visual comparison, correlation coefficients and histogram statistics. All the nine fusion techniques improved the resolution and the visual appearance of the original MS image. These are comparable to the resolution of the original PAN image. All the three resampling methods (nearest neighborhood, bilinear interpolation and the cubic convolution) do not have any significant effect on the final visual appearance of the fused images. All fusion techniques result in a change of the statistical parameters of the original images. The multiplicative technique results in major changes of the statistical parameters than the other techniques. After doing visual comparison and analyzing the statistical parameters, the Brovey and the PCA techniques seem to gather more information of urban features in fused PAN and MS IKONOS images.

Watershed Prioritization and Reservoir Sedimentation Using Remote Sensing Data

Abstract To limit siltation, it is essential that soil conservation measures are undertaken in the drainage basin upstream of the reservoir. In this study, the catchment for the Ramganga reservoir has been divided into nine sub‐watersheds to determine the sub‐watershed most prone to soil erosion. Also, temporal IRS‐1B LISS‐III images between years 2000-2001 are used on ILWIS image processing and GIS software for the assessment of reservoir sedimentation in Ramganga catchment. An approach, based on land surface factors mainly responsible for erosion, which include slope, landuse, brightness and greeneness etc., are used in this study. The catchment of reservoir is divided into 9 sub‐watersheds to assess the sub‐watershed contributing maximum sediments to the reservoir. Palain sub‐watershed of Ramganga catchment is identified as being most susceptible to sedimentation. The LISS III images are used to compute the water index, while the GIS system is used to analyse the topography. The integrated effect of all the parameters is evaluated to find sedimentation rate in the reservoir.

Remote sensing and GIS-based assessment of urbanisation and degradation of watershed health

Water resources, ecological quality, i.e. vegetation, flora-fauna, native plants, etc., and geo-morphological characteristics are some of the important elements that represent health of a watershed. Watershed health can be assessed through some of the indirect metrics, such as change in rainfall-runoff response, depletion of groundwater, groundwater pollution and degradation of geo-morphological characteristics. In the present study, application of remote sensing and geographic information system (GIS) technologies have been demonstrated for assessment of health of two urbanised sub-watersheds over a period of last 29 years (1977–2005). Investigation includes estimation of urbanisation and resulting changes in the watershed characteristics representing heath of watershed, such as change in surface runoff response, groundwater level, groundwater quality and morphological characteristics. Remote sensing images of eight years (1977–2005) have been used for extraction of land use/cover and urban growth. Change in surface runoff characteristics have been estimated using a physically based distributed storm water management model (SWMM). Groundwater analysis has been carried out in GIS to determine the change in groundwater level and quality over a period of 1992 to 2005. Results reveal that rate of land development in Ajmer is higher as compared to the population growth. Significant changes have been found in important watershed characteristics leading to deterioration of its health. Remote sensing and GIS technologies have been found to be useful for such studies.

Modeling of spatio-temporal dynamics of land use and land cover in a part of Brahmaputra River basin using Geoinformatic techniques

An attempt has been made to explore and evaluate the Cellular Automata (CA) Markov modelling to monitor and predict the future land use and land cover (LULC) scenario in a part of Brahmaputra River basin using LULC maps derived from multi-temporal satellite images. CA Markov is a combined cellular automata/Markov chain/multi-criteria/multi-objective land allocation (MOLA) LULC prediction procedure that adds an element of spatial contiguity as well as knowledge base of the likely spatial distribution of transitions to Markov chain analysis. Evidence likelihood map was used for as knowledge base of the likely spatial procedure in CA Markov model. The predicting quantity and predicting location change have been analysed and statistically evaluated. The validation statistics indicated how well the comparison map agreed and disagreed with the reference map. Predicted results accuracy is slightly higher when compare to others studies of LULC change using CA Markov approaches.

Land Degradation and Erosion Risk ANalysis in S.E. Spain: a geographic information system approach

Abstract This study demonstrates the use of a Geographic Information System (GIS) for monitoring land use change, land degradation and erosion risk in the Albudeite catchment, S.E. Spain. A land use classification of the area was obtained by Air-photo Interpretation (API). Two sets of air-photos (1957 and 1986) were interpreted enabling land use changes over the 29 year period to be assessed. Gullied areas were also mapped from 1986 photographs and used to compute gully density. A Digital Elevation Model (DEM) derived from elevation contours was used to calculate terrain parameters, such as slope and aspect. The land use data were analysed together with DEM, slope, and aspect using a GIS to study the distribution of land use and land degradation features, and to monitor land use changes. An erosion risk map of the area was also produced by combining land use, gully density and slope data using the GIS. It was found that nearly 50% of the total area is susceptible to erosion risks, ranging from moderate to very severe, and that 56% of the cultivated land is threatened by these risks. Slopes of between 2–8° were found to be critical as both land use changes and erosion risks were greatest at these gradients.

Evaluation of radiometric resolution on land use/land cover mapping in an agricultural area

Motivated by the increasing availability and importance of high radiometric resolution remote sensing data, this study aims to determine whether current generation high radiometric resolution remote sensing data could be used accurately for land use/land cover classification in place of traditional moderate radiometric resolution multispectral data. A comparative study has been carried out to evaluate the utility of the simulated 12‐bit LISS‐III sensor compared with that of the original 7‐bit LISS‐III sensor for land use/land cover classification. It has been found that there is a small increase of 3% in overall accuracy by using the high radiometric resolution (12‐bit) LISS‐III data over the moderate radiometric resolution (7‐bit) LISS‐III data for land use/land cover classification. A 4–6% difference in classification accuracy of simulated LISS‐III for cotton, chillies, black gram, and sugar cane indicates the usefulness of high radiometric resolution data for ground classes that are more heterogeneous within the same class. This study suggests that high radiometric resolution satellite data may not be a requisite for accurate land use/land cover mapping, whereas the spectral bandwidth, band placement, and method of classification parameters may be more important.