Remote Sensing Data integrated with Landscape Metrics for Urban Sprawl Analysis

Remote Sensing data can be applicable in many fields as direct use to assess change for monitoring and in many models as input data for further analysis. Selection of remote sensing images to create spatial database is mainly depend on user requirement and these satellite image can be selected for spatial database generation based satellite imagecharacteristics, which can be explained by below parameters of satellite image-Spatial ResolutionSpectral ResolutionTemporal Resolution and Radiometric ResolutionBased on above property satellite images can be selected by user then basic image processing steps need to follow to create accurate spatial dataImage Geometric correctionsImage Radiometric correctionsImage EnhancementImage ClassificationAccuracy assessment to accept spatial data for further analysisThese spatial data created using satellite images can be used in simulation andmathematical model/method. In this article these spatial data are analyzed using Landscape metrics to assess …

Remote Sensing and GIS for Spatial Analysis- A Scenario Based Analysis Approach

Remote Sensing process is very significant to keep watch on earth surface features including both man-made and natural. Satellite image acquired and processed in image processing software to create spatial data. These spatial data can be further used as input data in different mathematical models to analyze the change and to find trends in future for policy planning and implementation.GIS software also used to create input data and those spatial data can further integrated in models to analyze current as well as future scenario. GIS has many functions and tools which can be employ to find the impact in that area as well as to analysis impact of neighborhood or vice versa.  In this scenario analysis, the urban area has been re-simulated for 2015 and 2025 by including commercial and industrial areas proposed in the 2025 land use plan. The aim of this analysis is to ascertain the impacts of commercial and industrial areas on urban change. The variable proximity to commercial and industri…

Remote Sensing and GIS Spatial Analysis-Scenario Analysis based on Land use Plan and Policies

This article is in continuation of last article in which remote sensing and GIS used for spatial analysis to assess and prospects of future scenario in urban change. In the below article another case is discussed in which effectiveness of proposed plan of 2025 for city is assessed using remote sensing and GIS based spatial analysis process.Through remote sensing, one can acquire satellite image of different periods or multi date as well as from high resolution to medium resolution and also combination of bands or multi spectral or panchromatic band. In current there are many choices are available related to acquiring of satellite images. This may help user to create spatial data from high resolution to medium resolution as per need and also for different periods like from last 10 or 20 years to ascertain the change and trend of urban sprawl. The basic and important steps of digital image processing such as layer stacking, subset, mosaic, enhancement, interpretation and classification

Remote Sensing and GIS Spatial Analysis-Scenario Based Analysis of Future Urban Sprawl

Remote Sensing data or images can be used to assess past trends and current status of natural as well as man-made features on earth surface. Satellite imagescan be acquired based on need and using image characteristic/properties such as Spatial, temporal and spectral resolutions to create spatial data in the form of land use/land covermaps. GIS is used to create other spatial data such as road network, proximity to central business district etc. The spatial data can be created in both raster and vector format including non-spatial data in the form of attributes for scenario based analysis. These data can also convert from raster to vector and vice versa as per need using image processing and GIS software.These spatial data can be integrated to assess the changes in future based on some specific condition and constraints. As GIS allow to incorporating mathematical and simulation models, it is become easy to use these spatial data for assessment based on certain condition, which can be …

Remote Sensing and GIS for Urban Sprawl Projection/Prediction

Urban sprawl, an outcome of socioeconomic development and population under certain conditions, is continuously increasing. It becomes a major issue facing by many metropolitan cities of developing and developed countries. Urban sprawl is often took as scattered suburban development that create and increase several issues-  Stress on basic amenities  Traffic problems,  Reduce local resources, and  No more open space  It is important to appropriately illustrate urban sprawl in order to prepare a complete understanding of the causes and effects of urban sprawl processes. Urban sprawl is often assessed and characterized based on major socio-economic factors such as  Growth in Population  Transportation/Commuting Costs Employment Shifts  Revenue Generation in City  Commercial Development Status Urban sprawl, in most cases associated with poor land use plan and economic development and activities. 
This above approach based on socio-economic factors cannot effectively identify the influences of urban…

GIS Spatial Analysis-Land Use Mix Spatial Data

IntroductionGIS is a versatile tool and can be used for variety of application as well as creation of spatial data. It can be used to create elevation data, population density, water quality, urban density, air quality etc. and at the same time these data can be spatially represented. The advantage of GIS spatial data, that it can be analyzed to assess the impact on neighborhood or surrounding environment. GIS has various functions,spatial data in both formats such as raster and vector.Here in this article GIS is used to create distribution of commercial and industrial activity within the city to find out the impact on travel demand. For this a spatial data named land use mix created using GIS spatial analytical tool.Land use mix index is used to ascertain the level of commercial activity in each ward of Jaipur city. This indicator is very important to know the level of commercial and industrial activities (in the form of At present, there is no available data source that provides the…

Buffer Analysis Using QGIS

IntroductionGIS provide many tools for understanding and analysis of spatial data base. GIS has capabilities to analyze feature itself as well as its surrounding area and impact of surrounding area on that earth feature. GIS functions play significant role in the data creation, edit, manipulation and analysis. There are ‘n’ number of function available in GIS based on mathematical, statistical and spatial approach such proximity analysis, buffer analysis, spatial analysis etc. 
Buffer analysis is used to delineate areas in proximity to geographic spatial feature. In this a buffer is generated around that existing geographic feature by using distance parameters and this buffer area is used to identify and analyze spatial features comes into that buffer area or outside that buffer area. 
In the buffer analysis a buffer area of specified distance from geographic feature is created then this buffer area can be used to specify the spatial features fall in that buffer area and features are be…

Interpolation Process of Spatial Data in GIS Environment using QGIS

Introduction Continuous spatial data is very important in many applications for spatial analysis. So for such analysis, acquiring continuous spatial data is very difficult, costly and usually takes very long time for study area. In case, if one needs to collect/create continuous data then field sampling and observations is very common practice, such as water sampling for quality analysis. But during this field sampling process, it is nearly impossible to collect data from each and every spot of field, usually samples collected at certain distance. So this distance gap/interval between two samples/more samples is not possible to cover as well as very tedious and time consuming. In this case, some representative samples such as water samples from all well locations or pollution parameters from air monitoring stations will be collected and interpolation method is deployed to create continuous layer. Accuracy of interpolated spatial data depends upon the quantity and spatial distribution …