The world is undergoing the most significant wave of urban growth in history. It is expected that by 2030 the number of people living in the cities will increase to about 5 billion. The rapid urbanization has led to complex problems, including a reduction in vegetation cover, the formation of the urban heat island effect, environmental pollution, reduced open space, etc. This study intends to explore the spatial patterns of urbanization and its impact on the environment in and around Chandigarh- the first planned city of India. Chandigarh was originally planned for a population of 5 lakh, but the city has expanded rapidly over the last four decades and faces problems common to other growing cities in India, including the proliferation of slums and squatter settlements. The areas adjacent to the city boundary also face similar issues. The study presents the methods and results of an object-based classification and post-classification change detection on multi-temporal Landsat data (1978-2017). The processed data was used as an input for object-based classification using image segmentation algorithm of eCognition Developer software. The results show that maximum urbanization has taken place in the last decade in the southern and north-western directions outside the city as a result of the development of an international airport, new sectors and approach roads on the vegetated areas. As a result, maximum changes could be seen in the class vegetation as it has been rapidly changed to built-up areas. The results of this kind of study may hold immense value for planning the urban sprawl areas where up-to-date information is lacking because of the rapid pace of development.
The relationship between surface temperature and Soil Adjusted Vegetation Index (SAVI) associated with changing land‐use pattern due to intensive mining and mine fires as discussed in Jharia coalfield, India using data collected by the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Optical land imager (OLI) and thermal infrared sensor (TIRS) from 1991 to 2013. Jharia coalfield is under fire since the last century due to unsustainable mining activities. On visual interpretation of the surface temperature and SAVI images, it was observed that the spatial distribution of SAVI is opposite to that of LST for the whole coalfield. A subset of typical mining area known to have mines under fire was taken for further analysis. Profiles were taken along north-south and east-west directions in the subset in order to disclose variance based on the pixel values of surface temperature and SAVI images. The profiles show that peak SAVI values are in areas having dense vegetation and peak surface temperature values correspond to areas under fire. These two show an obvious negative correlation. Areas with water bodies show low temperature as well as low vegetation index values. Thus, it could be concluded that moderate resolution remote sensing data provides a convenient way to evaluate the impact of mine fires on vegetation over a period of time.
The paper outlines comparisons between different methods for the mapping of debris covered glaciers. The supervised classification method like Maximum Likelihood Classifier (MLC) has been tested using different data set for deriving the glacier area. Along with MLC the semi-automated method like Hierarchical Knowledge Based Classifier (HKBC) has also been used here. All the results were tested for accuracy, processing time and complexities. The results were also tested against the manually digitized boundary. The results suggests that the MLC when used with other ancillary data like geo-morphometric parameters and temperature image takes slightly more time than HKBC due to some to higher amount of post processing time but the output is satisfactory (89 % overall accuracy). Results show that the time taken in different classifications is significantly different which ranges from 1-2 hours in MLC to 5-10 hours in manual digitization. Depending on the classification method, some to large amount of post processing is always required to achieve the crisp glacial boundary. Classical classifier like maximum likelihood classification is less time consuming but the time taken in post-processing is higher than HKBC. Another factor which is important for a better accuracy is the prior knowledge of glacier terrain. In knowledge based classification method, it is required initially to establish crisp rules which are later used during classification, without this per-classification exercise the accuracy may significantly decrease. This is a time consuming procedure (2-3 hours in this case) but a minimal amount of post-processing is required. Thermal and geo-morphometric data when used synergistically, classified glacier boundaries are more crisp and accurate.
The LISS-IV sensor aboard Resourcesat-2 is a modern relatively high resolution multispectral sensor having immense potential for generation of good quality land use land cover maps. It generates data in high (10-bit) radiometric resolution and 5.8 m spatial resolution and has three bands in the visible-near infrared region. This is of particular importance to global community as the data are provided at highly competitive prices. However, no literature describing the atmospheric correction of Resourcesat-2-LISS-IV data could be found. Further, without atmospheric correction full radiometric potential of any remote sensing data remains underutilized. The FLAASH and QUAC module of ENVI software are highly used by researchers for atmospheric correction of popular remote sensing data such as Landsat, SPOT, IKONOS, LISS-I, III etc. This article outlines a methodology for atmospheric correction of Resourcesat-2-LISS-IV data. Also, a comparison of reflectance from different atmospheric correction modules (FLAASH and QUAC) with TOA and standard data has been made to determine the best suitable method for reflectance estimation for this sensor.
Jharia coal-field holds unequivocal importance in the Indian context as it is the only source of prime coking coal in the country. The coalfield is also known for its infamous coal mine fires which have been burning since last more than a century. Haphazard mining over a century has led to eco-environmental changes to a large extent such as changes in vegetation distribution and widespread development of surface and subsurface fires. This article includes the spatiotemporal study of remote sensing derived eco-environmental parameters like vegetation index (NDVI), tasseled cap transformation (TCT) and temperature distribution in fire areas. In order to have an estimate of the temporal variations of NDVI over the years, a study has been carried out on two subsets of the Jharia coalfield using Landsat images of 1972 (MSS), 1992 (TM), 1999 (ETM+) and 2013 (OLI). To assess the changes in brightness and greenness over the year s, difference images have been calculated using the 1992 (TM) and 2013 (OLI) images. Radiance images derived from thermal bands have been used to calculate at-sensor brightness temperature over a 23 year period from 1991 to 2013. It has been observed that during the years 1972 to 2013, moderate to dense vegetation has decreased drastically due to the intense mining going on in the area. TCT images show the areas that have undergone changes in both brightness and greenness from 1992 to 2013. Surface temperature data obtained shows a constant increase from 1991 to 2013 apparently due to coal fires. The utility of remote sensing data in such EIA studies has been emphasized.
NON-SPIE: Advanced Remote Sensing
I took lab and occasionally theory classes of PhD students
NON-SPIE: Surveying, Photogrammetry and remote Sensing
I took tutorial classes of b. Tech. Civil Engineering II year students. it involved satellite data processing using ERDAS Imagine, ENVI, and base map preparation and site suitability analysis using ArcGIS software