Milap Punia

Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data

Research highlights? We explored the potential of multi-temporal IRS P6 (Resourcesat) Advanced Wide Field Sensor (AWiFS) data for mapping of LULC for Delhi, India. ? A decision tree classification of seasonal composite (three seasons) temporal data set with a good definition of training sites was presented. ? We mapped eight different LULC classes with overall classification accuracy 89%. ? The database is first of its kind being a product of moderate to high resolution (=56m) multi-temporal satellite data. ? We recommend the use of AWiFS data for LULC mapping for regional level assessment and monitoring. In this study we explored the potential of multi-temporal IRS P6 (Resourcesat) Advanced Wide Field Sensor (AWiFS) data for mapping of LULC for Delhi, India. The study presents the result of a decision tree classification of seasonal composite data (three seasons). The study has identified 13 classes with description of cropping pattern namely, double crops, kharif, rabi and zaid from 56m spatial resolution AWiFS data. Delhi has a diverse range of land use predominantly mosaic of built-up. More than half of the area is urban settlement. Results indicate that the temporal data set with a good definition of training sites can result in good overall accuracy (=91.81) as well as individual classification accuracies (producers accuracy ?76.92 and users accuracy ?60). It is evident that AWiFS data can be used to provide timely and detailed LULC maps with limited ancillary data. The AWiFS derived maps could be very useful as input to biogeochemical models that require timely estimation of LULC patterns.

The application of GIS-based bivariate statistical methods for landslide hazards assessment in the upper Tons river valley, Western Himalaya, India

Landslides are one of the most frequent and common natural hazards in many parts of Himalaya. To reduce the potential risk, the landslide susceptibility maps are one of the first and most important steps in the landslide hazard mitigation. Earth observation satellite and geographical information system-based techniques have been used to derive and analyse various geo-environmental parameters significant to landslide hazards. In this study, a bivariate statistics method was used for spatial modelling of landslide susceptibility zones. For this purpose, thematic layers including landslide inventory, geology, slope angle, slope aspect, geomorphology, slope morphology, drainage density, lineament and land use/land cover were used. A large number of landslide occurrences have been observed in the upper Tons river valley area of Western Himalaya. The result has been used to spatially classify the study area into zones of very high, high, moderate, low and very low landslide susceptibility zones. About 72% of active landslides have been observed to occur in very high and high hazard zones. The result of the analysis was verified using the landslide location data. The validation result shows significant agreement between the susceptibility map and landslide location. The result can be used to reduce landslide hazards by proper planning.

3d landscape modelling using java 3d/vrml

Interactive visualization has been an integral part of landscape simulation to facilitate understanding of environmental processes. In most cases, however, interaction with the data is limited to viewing only. Thus to support more 3D interaction, in this study an attempt has been made to apply multi-view approach of the model for orientation, navigation and selection as visual metaphors on the landscape. This paper further describes how JAVA 3D/VRML can be used to model a landscape for better user interaction and exploration. Data used for generation of landscape model is SRTM-3 pertaining to a part of Alwar district in Rajasthan. This study emphasizes the importance of user interaction with 3D landscape for visualizing future design implications, landuse change and visual exploration.

Comparison of MODIS derived land use and land cover with Ministry of Agriculture reported statistics for India

Abstract The purpose of the study is to evaluate the suitability of moderate-resolution imaging spectroradiometer (MODIS) data to study the land use land cover over India. The study is based on secondary data sets pertaining to forest, cropland, pasture, and barrenland obtained from Directorate of Economics and Statistics (DES) and MODIS (Terra) global land use land cover data yearly composite from 2002 to 2005. A family of statistical and mathematical techniques is adopted here in order to compare the MODIS data with DES statistics. The comparison at the country level shows estimated forest cover has least uncertainty compared to pasture and barrenland. Comparison at the state level, on the other hand, shows high degree of association between the data sets in cropland ( R 2 = 0.9 ), followed by forest cover and pastureland. Barrenland shows weakest association between DES and MODIS. The computed average accuracy in cropland shows a level of 84% and has been chosen as the best fitted land cover category among all land cover classes selected for the study. Hierarchical clustering of the MODIS cropland at the state level based on the estimated accuracy shows that, except for Andhra Pradesh, Tamilnadu, Haryana, West Bengal, Chhattisgarh, and Orissa, which are far off from the true estimate, the rest of the states are in closer correspondence of the cropland statistics reported by Ministry of Agriculture.

Comparison of Peripheral Metropolitanisation in Haryana and Rajasthan, India

While this growth is important in terms of absolute numbers, India’s urban growth rate is not very high. Some consider that the urbanisation process is not rapid enough despite continuous economic growth, especially since 1991, when new liberal policies based on foreign investments, free trade zones and special economic zones, to ensure integration in global value chains, were adopted. Others, on the contrary, argue that there is no absolute correlation between size and growth. What is obvious from the last census is the role Census Towns have played in this growth. There are, in fact, multiple urbanisation processes with their distinctive characteristics, which go well beyond the simple agglomeration model, and it is imperative to understand the location and emergence of these new CTs and further, to understand their location in different states. The emergence of new urban settlements in Haryana is located around second tier cities; thanks to better connectivity, clustering of enterprises and a strong inclusion within the larger economy of the National Capital Region (Delhi). On the contrary, in Rajasthan, the emergence of new towns is not necessarily related to their proximity to million plus or class I towns. The comparison indicates that there are diverse processes at work and that regional and historical paths matter in the development trajectories of small towns.

Revealing the Vulnerabilities of Population and Places at Risk in Delhi

India is witnessing upsurge in events of natural hazards, risk and increase in extreme climatic events almost every year. This may be accounted due to change in global climate, however, increasing population pressure on land and lack of regulations related to construction activities on river bed exaggerated the quantum of loss and damage. Thus, this study is an attempt to focus on the assessment of population at risk and to study how effectively assessment can be made about the spatial locations, which can help to reduce the impact and mobilize the resources at the earliest in adversity of hazard. As per 2011 census, 16.7 million people residing in Delhi NCT, out of which 1.49 million (11.64 %) are living in those areas which are highly prone to floods. An attempt has been made at disaggregated municipal ward level to assess the population at risk during 2010 floods adjoining to Yamuna river bed. Adopted methodology is based on remote sensing and geographic information system (GIS) based approaches to extract information for absolute population and density using LandScan data from Oak Ridge National Laboratory (ORNL). These estimated figures are validated with information from census for their further applicability. Results indicate that there is need for noticeable change in policy, with emphasis on loss and damage reduction through mitigation and preparedness. There is also strong need to study further spatial interactions with vulnerable populations in terms of their socio-economic constraints.