Abstract

Drastic land use land cover changes (LULC) in forest landscape alters the functional ability of forest ecosystem, evident from decline in quantity and duration of water flow in streams, loss of carbon sequestration potential, etc. affecting the local livelihood, while inducing changes in the climate. This necessitates inventorying, mapping and monitoring of LULC dynamics to evolve appropriate location specific mitigation strategies. The LULC dynamics can be assessed effectively through free and open source software using temporal remote sensing data and modeling and forecasting techniques. The current research work aims to identify spatio-temporal land use changes from 2007-2016 in Kaiga Nuclear Power house (NPH) and its environs with the help of GRASS GIS (a robust free and open source software). Land uses in 2022 is predicted through Cellular Automata–Markov Chain model with the knowledge of land use transitions during the four decades. Validation of the model is done using Kappa indices, which shows good accuracy due to agreement of the predicated and actual land uses. The region has lost forest cover from 23 to 16.81 % (2016-2022) with an increase of built-up are of 42.01 to 46.81 % (2016-2022). The land use modeling, planning and policy by implying ecological science perspectives can improve understanding of ecological processes. The recent study reports of higher instances of cancer in humans with the functioning of NPH during the last decade. Any further expansion of NPH area would have serious ecological implications due to loss of vast tracts of forests, farmlands and would threaten the health and livelihood of local population.

Abstract

The Western Ghats forms an important watershed for the entire peninsular India, being the source of 37 west flowing rivers and three major east flowing rivers and their numerous tributaries. However, deforestation due to large scale land cover changes has affected the water sustenance in the region evident from the quantity and duration of water availability during post monsoon period. Land use Land cover changes accelerated by unplanned anthropogenic activities have been the prime mover of global warming and consequent changes in the climate. This necessitates appropriate resource management with an understanding of drivers. Geo-visualization of landscape transitions considering the influential agents will aid in formulating strategies to mitigate global warming. Uttara Kannada district in the Central Western Ghats has the distinction of having highest forest cover in the country and this region is now experiencing rapid forest cover changes. Factors inducing changes in the land cover are normalized through fuzzyfication, considered for Multi criteria Evaluation using Analytical Hierarchy Process (AHP) under high protection and low protection scenarios. Likely land use transitions by 2022 across zones based on transitions during 2004–2007, 2007–2010, 2010–2013 was done through cellular automata and Markov chain process (MC). The analyses highlight the loss of forest cover by 66.55–56.76% by 2022 in the coastal zone with escalating population density. Similar situation of 65.98–55.62% decline in Sahyadri region is noticed with execution of dams, hydroelectric projects and monoculture plantations. Lower transitions as compared with the second scenario highlights regulatory framework’s role in protection. However, forests in plain region show loss of 27.38–11.09% in both scenarios due to population pressure and market induced land cover changes. This necessitates policy interventions by the federal government to mitigateforest loss towards sustainable development.

Abstract

Generic text-based compression models are simple and fast but there are two issues that needs to be addressed. They cannot leverage the structure that exists in data to achieve better compression and there is an unnecessary decompression step before the user can actually use the data. To address these issues, we came up with GMZ, a lossless compression model aimed at achieving high compression ratios. The decision to design GMZ (Khandelwal and Rajan, 2017) exclusively for GML's Simple Features Profile (SFP) seems fair because of the high use of SFP in WFS and that it facilitates high optimisation of the compression model. This is an extension of our work on GMZ. In a typical server-client model such as Web Feature Service, the server is the primary creator and provider of GML, and therefore, requires compression and query capabilities. On the other hand, the client is the primary consumer of GML, and therefore, requires decompression and visualisation capabilities. In the first part of our work, we demonstrated compression using a python script that can be plugged in a server architecture, and decompression and visualisation in a web browser using a Firefox addon. The focus of this work is to develop the already existing tools to provide query capability to server. Our model provides the ability to decompress individual features in isolation, which is an essential requirement for realising query in compressed state. We construct an R-Tree index for spatial data and a custom index for non-spatial data and store these in a separate index file to prevent alter - ing the compression model. This facilitates independent use of compressed GMZ file where index can be constructed when required. The focus of this work is the bounding-box or range query commonly used in webGIS with provision for other spatial and non-spatial queries. The decrement in compression ratios due to the new index file is in the range of 1-3 percent which is trivial considering the benefits of querying in compressed state. With around 75% average compression of the original data, query support in compressed state and decompression support in the browser, GMZ can be a good alternative to GML for WFS-like services.

Abstract

Geospatial data visualization systems have been predominantly through applications that are installed and run in a desktop environment. Over the last decade, with the advent of web technologies and its adoption by Geospatial community, the server-client model for data handling, data rendering and visualization respectively has been the most prevalent approach in Web-GIS. While the client devices have become functionally more powerful over the recent years, the above model has largely ignored it and is still in a mode of serverdominant computing paradigm. In this paper, an attempt has been made to develop and demonstrate LSIViewer - a simple, easy-to-use and robust online geospatial data visualisation system for the user’s own data that harness the client’s capabilities for data rendering and user-interactive styling, with a reduced load on the server. The developed system can support multiple geospatial vector formats and can be integrated with other web-based systems like WMS, WFS, etc. The technology stack used to build this system is Node.js on the server side and HTML5 Canvas and JavaScript on the client side. Various tests run on a range of vector datasets, upto 35MB, showed that the time taken to render the vector data using LSIViewer is comparable to a desktop GIS application, QGIS, over an identical system.

Abstract

Geography markup language (GML) is an XML specification for expressing geographical features. Defined by Open Geospatial Consortium (OGC), it is widely used for storage and transmission of maps over the Internet. XML schemas provide the convenience to define custom features profiles in GML for specific needs as seen in widely popular cityGML, simple features profile, coverage, etc. Simple features profile (SFP) is a simpler subset of GML profile with support for point, line and polygon geometries. SFP has been constructed to make sure it covers most commonly used GML geometries. Web Feature Service (WFS) serves query results in SFP by default. But it falls short of being an ideal choice due to its high verbosity and size-heavy nature, which provides immense scope for compression. GMZ is a lossless compression model developed to work for SFP compliant GML files. Our experiments indicate GMZ achieves reasonably good compression ratios and can be useful in WebGIS based applications.

Abstract

Land use land cover (LULC) information at temporal scale has become necessary in the analysis of environmental progression, resource management and planning. Uncontrolled expansion of human activities are playing an important role in global land use land cover change affecting ecology, climate change, soil erosion and hydrological regime. Visualizing and quantifying future changes of land use is also essential in high forested regions such as Uttara Kannada to ensure appropriate management practices and policies for maintaining the ecosystem services provided by forests. The landscape transition has accounted across five forest jurisdictions from 2004 to 2013 and visaulised further by non-agent based modeling for 2026. Cellular automata (CA), Markov chain model approaches has evolved as a promising tools of visualisation helps in complicated decision making process to understand the neighboring effects of land use activities. The Haliyal division has lost major cover from 2004 (55.48%) to 2013 (45.29%) due to increase in population pressure, unplanned developmental activities and conversion of forest area into

Abstract

There have been various ways in which the indoor space of a building has been defined. In most of the cases the models have specific purpose on which they focus such as facility management, visualisation or navigation. The focus of our work is to define semantics of a model which can incorporate different aspects of the space within a building without losing any information provided by the data model. In this paper we have suggested a model which defines indoor space in terms of semantic and syntactic features. Each feature belongs to a particular class and based on the class, has a set of properties associated with it. The purpose is to capture properties like geometry, topology and semantic information like name, function and capacity of the space from a real world data model. The features which define the space are determined using the geometric information and the classes are assigned based on the relationships like connectivity, openings and function of the space. The ontology of the classes of the feature set defined will be discussed in the paper.

Abstract

LANDSAT images are used to identify organic contaminants in water bodies, but, there is no enough evidence in present literature that LANDSAT is also good in identifying a mixture of organic and mineral contaminants such as agricultural waste. The focus of this paper is to evaluate the effectiveness of LANDSAT imagery to identify organic and mineral contamination (OMC) and to identify spread extent variations of pollution over the season/year in the Nagarjuna Sagar (NS) reservoir using only satellite images. A new band combination is proposed in order to detect OMC, because existing formulae based on band ratio proved to be inadequate in detecting the contamination in NS. Difference in reflectance values of Red and Green channel of an image helps clearly distinguish clear water from OMC water. This procedure was applied over LANDSAT data of the calendar years 2008, 2014 and 2015 to understand the contamination spread pattern through the reservoir. Results show that contamination is following a similar pattern over these calendar years. In January contamination starts at inlets and by May contamination spreads to almost 90% of the reservoir when the total area of water spread is also reduced by half. Contamination spread is low during the monsoonal period of June to September due to heavy inflow and heavy outflow of waters from NS reservoir. Post monsoon NS is contaminated again because of heavy inflow of runoffs from neighboring land use and limited water outflow. This contamination spread pattern matches the agricultural seasons and fertilizer application pattern in this region, indicating that agricultural use of fertilizers could be one of the primary causes of contamination for this waterbody.

Abstract

Relational databases have been around for a long time, and Spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling too. Moreover, this is gaining ground in the context of increased shift towards Geospatial Web Services on both the Web and mobile platforms especially in the user-centric services, where there is a need to improve the query response time. This paper attempts to evaluate the performance of an existing NoSQL database ‘MongoDB’ with its inbuilt spatial functions with that of an SQL database with spatial extension ‘PostGIS’ for two fundamental spatial problems—line intersection and point containment problem, across a range of datasets, with varying feature counts. Given these results, NoSQL databases may be better suited for simultaneous multiple-user query systems including Web-GIS and mobile-GIS. Further studies are required to understand the full potential of NoSQL databases across various geometries and spatial query types.

Abstract

The traditional pixel-based classification totally relies on spectral information and neglects the spatial information. These methods when applied on very high-resolution imagery get confused because of the increased variability implicit within the data and thus leads to lower classification accuracies.The object-based image analysis (OBIA) is advantageous to deal with objects that are composed of homogeneous pixels.This paper aims at automatically extracting buildings from very high-resolution satellite imagery using Object Based Image Analysis(OBIA).The algorithm uses an active contour model called chan-vese segmentation to create objects from the image. Objects representing vegetation or trees are removed by subtracting NDVI mask from the segmented output. The detected objects are further filtered based on regional properties like minimum area, width of object etc. The results are promising with 74-77%of the buildings getting detected as objects