Remote sensing and geophysical studies for groundwater exploration in Osmania University campus, Hyderabad, India: A case study

With the increasing resolution of satellite sensors, it is possible to fruitfully exploit the special advantages of image analysis for a wide range of geological environments. With this view, a LISS-III and PAN merged image of the 1600 acre (approximately 6.5 sq km) Osmania University (OU) campus taken from IRS-ID in the month of May (a fairly representative month in terms of minimum annual drainage/vegetation cover) was acquired. The image was then digitally processed and visually interpreted for potential groundwater resource regions. Since occurrence of groundwater in crystalline rocks, the host rocks for the entire Hyderabad region, is generally associated with secondary porosity, the accent was on determining and establishing lineaments of considerable surface extent. This was then augmented with maps of subsurface features as obtained from geophysical studies for the southern part of 0 U campus and available bore well/open well information. Subsequently, information from the three sources was integrated for a better understanding of the geological situation and the interrelationship of its various constituents to determine possible locations of groundwater resources. The significant findings comprised the identification of three major dykes, two running E-W and the third running NE-SW. A major N-S linear exposure of granitic rocks, as also several criss-crossing fractures in the southern side of the campus, along with the prevailing drainage pattern for the entire campus area were mapped. Based on these findings and supporting geophysical/hydrogeological data, a geological/lithological map of Osmania University campus was prepared and prospective groundwater zones have been identified.

Spatial distribution of land use concentration and land-use efficiency of the Kakodonga river basin, Assam

The Kakodonga river basin covers an area of about 1,113 km2. The average maximum temperature is 28.50C and the minimum temperature is 18.40C. The average rainfall of the basin is 1766 mm. The present paper is an attempt to analyze spatially land-use concentration and land-use efficiency of the Kakodonga river basin. For the preparation of thematic maps, Survey of India topographic sheets and IRS P6 LISS-III imagery (2011) data are used. The land-use categories data has been collected for three years (2012-2014) at the revenue circle level from the Economic and Statistical Department of Assam. To work out land-use concentration Bhatia’s (1965) method was adopted. The major land-use categories are forest cover, area put under non-agricultural use, barren lands, permanent pastures, and other grazing lands, miscellaneous trees and groves, culturable wasteland, current fallow land, other fallow lands, and the net area sown. The land-use efficiency has been worked out taking five positive variables are net sown area, total irrigated area, area irrigated more than once, the intensity of irrigation, and intensity of crops.

Change Detection of Salt Affected Land Area of Unnao District in Uttar Pradesh using Remote Sensing and GIS

Reasons for excess salt in soil are due to both natural and anthropogenic activities. About 955Mha Sodic soil is present in worldwide out of which about 60% is cultivable area. This has huge impact on economic and agricultural production. Salt affected soil is locally called as reh, thur, chopan and kallar. Traditional methods are time consuming and expensive. This can be only fulfilled by using emerging technologies like remote sensing and GIS which are economical and easy in less time. Remote sensing is a technique using to conquer information without in being touch with that object. Remote sensing is very helpful for in temporal changes and spatial changes. So with the help of remote sensing mapping of salt affected area of Unnao district using satellite data of LISS-III of year 2012 and 2018 to calculate spatial changes in between this year and suggest some methods and techniques for reclamation. Analysis shows that Sodic area in Unnao is decreasing and awareness about reclamation of Sodic soil with new techniques is spreading among farmers. In this study, Unnao district has been taken as the study area for mapping and monitoring the change detection with respect to salt affected lands. Salt affected land covers mapped is 14495.63 ha area in 2012 in the district. But in 2018, the total area of salt affected lands has been decreased by 11054.62 ha. The major areas that have been reported having large salt affected land are Auras, Bichhiya & Mianganj Blocks.

Classification of small scale landforms, its significance: a case study of the middle Ichamati river, India

Landforms are the core concept of geomorphology. The definition of landforms, their characterization and classification are the core subject of geomorphology. But all these become complex when it seems to difficult to identify the landforms, especially when the area is plain land and highly modified by human activities. This paper has examined the characters of the landforms of the middle basin of the Ichamati river, the important distributary in the district of North 24 Parganas, India. It has been primarily taken an attempt to classify the landforms with the help of the satellite image, IRS P6 LISS II and LISS III. The DEM is not enough to identify the micro scale landform. To overcome this difficulty a series of field works have been conducted (2002, 2004, 2012 and 2015). The landforms have been classified according to second order derivative (Wood, 1996) method. Then ANOVA test has been applied to justify the classification. The F-statistics have indicated the effort is satisfying. The changing character of different landforms denote the river is going to be deteriorating from downstream to upward.

Analysis of Land Use and Land Cover Change in Gummidipoondi Using Remote Sensing and GIS Tools

The aim of the study was to evaluate the changes in land use and land cover (LULC) in Gummidipoondi and the surrounding areas in Thiruvallur district, Tamilnadu India.Spatio-temporal variation in the land use and land cover were analysed on a decadal basis for the period from 1990 to 2019 using remote sensing and GIS based tools. The Landsat 5 (TM) and Resource-Sat 2 (LISS-III) data was used for the LULC classificationin the study area. During the study period from 1990 to 2019, built-up area including industrial, urban and rural land use increased by about 147%. Predominant change was also noticed in the mudflat category where more than 95% of it was lost to various other land uses such as agriculture and marsh area. This observation calls for planning and conservation of sensitive ecosystems in the study area that may be lost due to anthropogenic pressures via pollution and undesirable conversion of LULC. The study revealed no significant changes in the extent of other LULC classes such as agriculture, forests, plantations, land with or without scrub, rivers and waterbodies in the study area

MAPPING AND CHANGE DETECTION OF MANGROVES AROUND MUMBAI USING REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS (GIS)

The study focuses on the mangroves in two districts namely, Mumbai and Mumbai Suburban. Mumbai, a coastal megacity, is a financial capital of the country with high population density. Mumbai is facing depletion of coastal resources due to land scarcity and large developmental projects. Thus, it is important to monitor these resources accurately and protect the stakeholders’ interest. Cloud-free satellite images of IRS P6 LISS III of 2004 and 2013 were procured from National Remote Sensing Centre (NRSC), Hyderabad. Two bands of visible and one band of NIR were utilized for landcover classification. Supervised Classification with Maximum Likelihood Estimator was used for the classification. The images were classified into various landcovers classes namely, Dense Mangroves, Sparse Mangroves and Others. Two software’s namely, ERDAS Imagine and GRAM++ were used for landcover classification and change detection analysis. It was observed that the total mangrove area in Mumbai in 2004 and 2013 was 50.52 square kilometers and 48.7 square kilometers respectively. In the year 2004 and 2013, contribution of sparse mangroves in the study area was 72.31 % and 87.06% respectively.

Impact of the Spatial Resolution of Satellite’s Data for Mapping Land use Land Cover in Karmala Tehsil, Solapur, India

Land use and land cover (LULC) classification mapping is important for evaluating, monitoring, protecting and planning for land resources. A key factor in extracting desired information from satellite images is choosing the right the spatial resolution. The scale of a pixel on the ground is known as spatial resolution. A pixel is the smallest ‘dot' that makes up an optical satellite image which defines the level of detail as in image. In this paper estimation of the areal extent of water, built up, barren land, vegetation land and fallow land classes with its classification accuracy were reviewed particularly for January 2013 and November 2016 in Karmala tehsil of Solapur district, India. LULC is implied by different spatial resolution images of Advanced Wide Field Sensor (AWiFS), Linear Imaging Self Scanning Sensor (LISS-III), Landsat-8 Operational Land Imager (OLI) and Sentinel-2A imageries in QGIS environment while the classification was carried out using the maximum likelihood algorithm (MLA). The classified maps obtained from AWiFS and LISS-III sensors, as well as Sentinel-2A and Landsat-8 OLI data sets, were compared separately. Spatial analysis depicts that the Kappa coefficient of Sentinal-2A, Landsat-8, LISS III and AWiFS was found 96.96%, 91.64%, 87.30% and 89.36%. Furthermore, overall accuracy of was found to be 99.07%, 94.49%, 89.84% and 94.08% respectively. The accuracy of the classified image with higher spatial resolution (Sentinal-2A) proved more informative than that of lower resolution (AWiFS) sensor. On the response, the finer spatial resolution of Sentinal-2A (10 m) delivered more precise details and enhanced LULC classification accuracy most reliably than the coarser spatial resolution of Landsat-8 (30m), LISS III (23m) and AWiFS (56m) image. A perusal of data revealed that the overall accuracy and Kappa coefficient was found proportionate to spatial resolution of satellite imageries. The higher resolution spatial data also greatly reduces the mixed-pixel problem. The study revealed that the spatial resolution plays an important role and affects classification details and accuracy of LULC level.

NDVI COMPUTATION OF LISS III IMAGES USING QGIS

Feature Selection and Extraction is a very significant and mandatory part in the domain of image processing. After the relevant preprocessing operations, the relevant features have to be extracted using suitable algorithms. In multispectral imagery, the features are identified and extracted based on the applications and objectives of the analysis such as color, texture, brightness, intensity etc. Some of the prominent algorithms used for feature extraction are mean shift algorithm, Principal Component transformation, Wavelet based Transformation, Local Binary Patterns etc. Texture based feature detection and extraction is the most prominent method adopted which involves multispectral images. With respect to hyperspectral images, dimensionality is a critical issue to be dealt appropriately.

ESTIMATIVA DE CONSTITUINTES OPTICAMENTE ATIVOS DA ÁGUA POR SENSORIAMENTO REMOTO ORBITAL

O trabalho teve como objetivo avaliar o uso potencial de imagens LISS-III/Resourcesat-1 na estimativa de constituintes opticamente ativos da água do reservatório Passo Real. Com as imagens de satélite utilizadas para a análise da refletância da água foi possível a extração de informações em pixels com resolução espacial de 23,5 metros. A seleção de imagens foi feita a partir da disponibilidade de cenas sem presença de nuvens em datas mais próximas à realização dos trabalhos de campo. Os processamentos das imagens para correção dos efeitos atmosféricos e transformação dos números digitais em valores de reflectância foram realizados no software ENVI 5.0 As espacializações dos dados limnológicos obtidos in situ foram realizadas por interpolação no software Spring 4.3.3 e as espacializações dos dados estimados por satélite foram gerados por fatiamento da imagem gerada pela inserção das equações obtidas nos modelos. A partir do conjunto de dados disponível foi possível produzir modelos capazes de estimar o total de sólidos em suspensão, a turbidez e a transparência da água com apenas uma única banda espectral, a banda 3 (620 a 680 nm) do sensor LISS-III/Resourcesat-1. Com a aplicação da técnica de razão de bandas espectrais foi possível gerar um modelo para estimativa da concentração de clorofila-a.