Monitoring and Analysis of Wastelands and its Dynamics using Multiresolution and Temporal Satellite Data in Part of Indian State of Bihar

International Journal of Geomatics and Geosciences, Volume 1, No 3, 2010

Nathawat M.S., Rathore V.S., Pandey A.C., Suraj Kumar Singh, Ravi Shankar G.

“Voluminous increase in population has created an excessive demand for productive land. At the same time land degradation because of desertification, soil salinity, waterlogging, floods/droughts, excessive soil erosion and unscientific agricultural practices has resulted in the creation of vast stretches of wastelands. This has necessitated adoption of scientific measures for increasing land productivity and bringing more areas under cultivation/forests. In the present study, the multi-temporal satellite images of IRS P6 LISS-III were used to map wastelands dynamics over different seasons. An attempt has also been made to evaluate the potential of high spatial resolution LISS IV (5.8 m) data over moderate spatial resolution LISS-III data (23.5 m) from the Indian Remote Sensing Satellite for delineation of wastelands in a portion of the Indo-Gangetic plains of northern India. Visual interpretation based on image characteristics and a prior knowledge of the study area was used to delineate wasteland classes. Using LISS III data, 1372.92 and 605.90 hectares of land areas are identified as affected by seasonal and permanent waterlogged respectively, and using LISS IV, 1113.33 and 105.84 hectares of land areas are identified as affected by seasonal and permanent waterlogged respectively. Wasteland classes such as seasonal and permanent waterlogged could be further separated into wasteland classes such as land with dense scrub, land with open scrub, degraded pastures/grazing lands and degraded land under plantation using higher resolution satellite data.”

Relative Accessibility Deprivation Indicators for Urban Settings: Definitions and Application to Food Deserts in Montreal

Urban Studies, June 2010; vol. 47, 7: pp. 1415-1438., first published on February 19, 2010

Antonio Páez, Ruben Gertes Mercado, Steven Farber, Catherine Morency, and Matthew Roorda

“Accessibility research, within the context of the social exclusion dimensions of transport, has provided valuable tools to understand the potential of people to reach daily life activity locations. In this paper, model-based estimates of distance travelled are used to calculate a cumulative opportunities measure of accessibility. Multivariate, spatially expanded models produce estimates of distance travelled that are specific to both geographical location and type of individual. Opportunity landscapes obtained based on these estimates are used for comparative accessibility analysis by means of what are termed relative accessibility deprivation indicators. The indicators proposed are demonstrated with a case study of food deserts in the city of Montreal, Canada. The results of the analysis illustrate the variations in accessibility between individuals in low-income households and the reference group, and the effect of vehicle ownership for accessibility to food services, thus highlighting the social exclusion implications of these factors.”

Data Management in the Worldwide Sensor Web

IEEE Pervasive Computing, Volume 6, Number 2, Pages 30–40, 2007

Magdalena Balazinska, Amol Deshpande, Michael J. Franklin, Phillip B. Gibbons, Jim Gray, Mark Hansen, Michael Liebhold, Suman Nath, Alexander Szalay, and Vincent Tao

“Harvesting the benefits of a sensor-rich world presents many data management challenges. Recent advances in research and industry aim to address these challenges.

“With the rapidly increasing number of large-scale sensor network deployments, the vision of a worldwide sensor web is close to becoming a reality. Ranging from camera networks that monitor large wildlife reserves to biological sensors implanted in the body to monitor vital signs, these deployments generate tremendous volumes of priceless data. Simply put, data is the raison d’être of any sensing exercise. Most sensor network researchers would probably agree that we have placed too much attention on the networking of distributed sensing and too little on tools to manage, analyze, and understand the data. However, with standards in place for many of the networking and other infrastructure-related issues, or at least an emerging consensus on how to solve them, the demands of a sensor web are largely shaping up to be questions of information management. What, then, are the challenges in working with data in distributed sensing systems; what services might promote good data sharing and analysis practices? Can we design sensor networks with data quality in mind?”