Y. V. N. Krishna Murthy

CityGML at semantic level for urban energy conservation strategies

The exponential growth of cities in India due to urbanization resulted in increased use of non-renewable energy resources to meet the essential power requirements of urban built environment. It is essential for urban planners to provide innovative solutions in context of urban energy simulation based on virtual 3D city models. The recent 3D geoinformation science studies are insufficient in providing optimal solutions because of lack of emerging concepts and integrated softwares. Presently 3D GIS data can be generated into various LODs (Levels of Detail) depending upon the application requirement and input data used. There are various 3D GIS softwares like Google SketchUp, ESRI CityEngine etc., which are being used mostly for data creation especially for boundary representation for geometry abstraction without semantic information. The 3D GIS data conversion from native format into City Geography Markup Language (CityGML) enhances it by providing information both at geometric and at semantic level in interoperable format. A building information model of Geoinformatics department building in IIRS campus is created using Google SketchUp and exported to energy modelling program in gbXML schema. The present investigation explores the semantic characteristics of developed CityGML model for solar thermal and photovoltaic energy production potential assessment based on building semantic components. The amount of solar irradiation incident on bounding features and also illumination obtained through openings of building is quantized using SunCast and RadianceIES application of IESVE Software, respectively. The simulated energy data are integrated with building semantic features and stored in open-source PostGIS RDBMS to address basic semantic queries.

Formulation of hierarchical framework for 3D-GIS data acquisition techniques in context of Level-of-Detail (LoD)

Two-Dimensional Geographic Information Science (2D GIS) development has reached its highest level in terms of acquisition, processing, analysis and presentation techniques. Further development in 2D GIS is restricted due to its 2D abstraction of real world objects which are having third dimension (3D) in practical world. The abstraction of 3D real world objects is of extreme importance for user applications to address issues related to infrastructure development, entertainment, tourism, sustainable management of cultural sites and to tackle effects of various social and environmental factors. Hence, formulation of mechanism to model 3D real world objects and its phenomena especially related to urban segment from data acquisition and analysis perspective is essential. The 3D GIS data acquisition techniques such as Satellite Photogrammetry, LIDAR data processing, Building structure extraction algorithms, Close-Range Photogrammetry and total station survey contribute significantly towards generation of 3D digital models. Most of 3D GIS analysis largely depends upon 3D data structure and on data acquisition mechanism in particular. The structured way of data acquisition facilitates encoding of same into common information model which further aids in complex GIS analysis. Therefore, this paper proposes a structured mechanism for data acquisition in context of hierarchical framework of Level-of-Detail (LoD).

An effective hybrid approach to remote-sensing image classification

This article presents a hybrid fuzzy classifier for effective land-use/land-cover (LULC) mapping. It discusses a Bayesian method of incorporating spatial contextual information into the fuzzy noise classifier (FNC). The FNC was chosen as it detects noise using spectral information more efficiently than its fuzzy counterparts. The spatial information at the level of the second-order pixel neighbourhood was modelled using Markov random fields (MRFs). Spatial contextual information was added to the MRF using different adaptive interaction functions. These help to avoid over-smoothing at the class boundaries. The hybrid classifier was applied to advanced wide-field sensor (AWiFS) and linear imaging self-scanning sensor-III (LISS-III) images from a rural area in India. Validation was done with a LISS-IV image from the same area. The highest increase in accuracy among the adaptive functions was 4.1% and 2.1% for AWiFS and LISS-III images, respectively. The paper concludes that incorporation of spatial contextual information into the fuzzy noise classifier helps in achieving a more realistic and accurate classification of satellite images.

Land-use and land-cover change in Western Ghats of India

The Western Ghats (WG) of India, one of the hottest biodiversity hotspots in the world, has witnessed major land-use and land-cover (LULC) change in recent times. The present research was aimed at studying the patterns of LULC change in WG during 1985–1995–2005, understanding the major drivers that caused such change, and projecting the future (2025) spatial distribution of forest using coupled logistic regression and Markov model. The International Geosphere Biosphere Program (IGBP) classification scheme was mainly followed in LULC characterization and change analysis. The single-step Markov model was used to project the forest demand. The spatial allocation of such forest demand was based on the predicted probabilities derived through logistic regression model. The R statistical package was used to set the allocation rules. The projection model was selected based on Akaike information criterion (AIC) and area under receiver operating characteristic (ROC) curve. The actual and projected areas of forest in 2005 were compared before making projection for 2025. It was observed that forest degradation has reduced from 1985–1995 to 1995–2005. The study obtained important insights about the drivers and their impacts on LULC simulations. To the best of our knowledge, this is the first attempt where projection of future state of forest in entire WG is made based on decadal LULC and socio-economic datasets at the Taluka (sub-district) level.

Study of soft classification approaches for identification of earthquake-induced liquefied soil

The existence of mixed pixels led to the development of several approaches for soft (or fuzzy) classification in which each pixel is allocated to all classes in varying proportions. However, while the proportions of each land cover within each pixel may be predicted, the spatial location of each land cover within each pixel is not. There exist many different potential techniques for sub-pixel mapping from remotely sensed imagery to identify specific class. The fuzzy-based possibilistic c-means (PCM), noise cluster (NC) and noise cluster with entropy (NCE) classifiers were applied to identify the Bhuj, India (2001), earthquake induced soil liquefaction and compared as soft computing approaches via supervised classification. The soil liquefaction identification was empirically investigated and compared with class-based sensor-independent (CBSI) spectral band ratio using Landsat-7 temporal images. It has been found that CBSI-based temporal indices yield the better results for identification of liquefied soil areas while it was easily separated with pre-earthquake existing water body in that area. The NCE classifier performed better for conventional temporal indices, while NC classifier performed better for soil liquefaction and PCM classifier performed better for water body identification with CBSI temporal indices.

Moist deciduous forest identification using MODIS temporal indices data

The present research aims to extract moist deciduous forest (MDF) from Moderate Resolution Imaging Spectroradiometer (MODIS) temporal data by using the fuzzy c-means (FCM)-based noise clustering (NC) soft classification approach. Seven-date temporal MODIS data were used to identify MDF, and temporal Advanced Wide Field Sensor (AWiFS) data were used as reference data for testing. Different types of spectral indices were used to generate the temporal data set combinations for both MODIS and AWiFS. The NC resolution parameter delta was optimized to achieve the best output. It was found that for both AWiFS and MODIS data, optimum NC outputs were obtained when reached close to 105. For assessment of the accuracy, NC classified outputs were optimized using the entropy approach. The optimized data set of AWiFS was then used for assessing the accuracy of the optimized data set of MODIS using fuzzy error matrix (FERM), composite operators (MIN-MIN, MIN-PROD, and MIN-LEAST), and a sub-pixel confusion-uncertainty matrix (SCM). It was found that the temporal data set combination corresponding to ‘Three’ date yields the highest overall accuracy for all accuracy assessment techniques. In all cases, the ‘Three’ date combination corresponds to the three scenes pertaining to different phenological activity of the MDF. This ‘Three’ date combination, along with the soil-adjusted vegetation index (SAVI), yielded the highest overall accuracy values, namely 94.88% and 94.84% for MIN-LEAST and MIN-PROD, respectively.

Evaluation of indicators for desertification risk assessment in part of Thar Desert Region of Rajasthan using geospatial techniques

Desertification has emerged as a major economic, social and environmental problem in the western part of India. The best way of dealing with desertification is to take appropriate measures to arrest land degradation, especially in areas prone to desertification. This requires an early warning system for desertification based on scientific inputs. Hence, in the present study, an attempt has been made to develop a comprehensive model for the assessment of desertification risk in the Jodhpur district of Rajasthan, India, using 23 desertification indicators. Indicators including soil, climate, vegetation and socio-economic parameters were integrated into a GIS environment to get environmental sensitive areas (ESAs) to desertification. Desertification risk index (DRI) was calculated based on ESAs to desertification, the degree of land degradation and significant desertification indicators obtained from the stepwise multiple regression model. DRI was validated by using independent indicators such as soil organic matter content and cation exchange capacity. Multiple regression analysis shows that 16 indicators out of 23 were found to be significant for assessing desertification risk at a 99% confidence interval with

Improving Cartosat-1 DEM accuracy using synthetic stereo pair and triplet

R^{2}=0.83