Monitoring and modelling of urban sprawl using remote sensing and GIS techniques

The concentration of people in densely populated urban areas, especially in developing countries, calls for the use of monitoring systems like remote sensing. Such systems along with spatial analysis techniques like digital image processing and geographical information system (GIS) can be used for the monitoring and planning purposes as these enable the reporting of overall sprawl at a detailed level.     

Remote sensing and GIS application for land quality assessment for coffee growing areas of Karnataka

An assessment of land quality was carried out for coffee-growing areas of Karnataka using satellite image, toposheets and soil studies. The investigation focused on monitoring soil processes that control the land quality using satellite data in order to identify the land qualities that are ideal for coffee-growing; to identify the status of land qualities of coffee-growing areas using satellite imageries, toposheets and soil resource maps and to characterise land quality using soil studies in selected areas. The land quality was characterised using climatic data, terrain analysis and soil attributes. From the study it was observed that coffee growing lands of Gabbugal and Kelagur have best land qualities and coffee-growing areas of Balur and Nellikkad have moderate land qualities. Satellite image and aerial photographs were successfully used for monitoring the land quality and its changes in these areas. For optimum utilization of available natural resources on a sustainable basis, timely and reliable information on soils regarding their nature, extent and spatial distribution along with their potential and limitations is very important. The efficiency and accuracy of data are improved when remote sensing data products such as aerial photographs and satellite image are used.     

Mapping of apple orchards using remote sensing techniques in cold desert of Himachal Pradesh,India

The present study was conducted to map Apple orchards in dry alpine Spiti region of Indian Himalaya using LISS III satellite image. The barren terrain with sparse woody vegetation helped in classification of apple orchards with 91.3 % accuracy. The orchards were found in 154.6 ha of the study area and are anticipated to expand owing to its economic importance.     

Estimation of Snow Cover Area,Snow Physical Properties and Glacier Classification in Parts of Western Himalayas Using C-Band SAR Data

Snow physical properties, snow cover and glacier facies are important parameters which are used to quantify snowpack characteristics, glacier mass balance and seasonal snow and glacier melt. This study has been done using C-band synthetic aperture radar (SAR) data of Indian radar imaging satellite, radar imaging satellite-1 (RISAT)-1, to estimate the seasonal snow cover and retrieve snow physical properties (snow wetness and snow density), and glacier radar zones or facies classification in parts of North West Himalaya (NWH), India. Additional SAR data used are of Radarsat-2 (RS-2) satellite, which was used for glacier facies classification of Smudra Tapu glacier in Himachal Pradesh. RISAT-1 based snow cover area (SCA) mapping, snow wetness and snow density retrieval and glacier facies classification have been done for the first time in NWH region. SAR-based inversion models were used for finding out wet and dry snow dielectric constant, dry and wet SCA, snow wetness and snow density. RISAT-1 medium resolution scan-SAR mode (MRS) in HV polarization was used for first time in NWH for deriving time series of SCA maps in Beas and Bhagirathi river basins for years 2013–2014. The SAR-based inversion models were implemented separately for RISAT-1 quad pol. FRS2, for wet snow and dry snow permittivity retrieval. Masks for layover and shadow were considered in estimating final snow parameters. The overall accuracy in terms of R² value comes out to be 0.74 for snow wetness and 0.72 for snow density based on the limited ground truth data for subset area of Manali sub-basin of Beas River up to Manali for winter of 2014. Accuracy for SCA was estimated to be 95 % when compared with optical remote sensing based SCA maps with error of ±10 %. The time series data of RISAT-1 MRS and hybrid data in RH/RV mode based decompositions were also used for glacier radar zones classification for Gangotri and Samudra Tapu glaciers. The various glaciers radar zones or facies such as debris covered glacier ice, clean or bare glacier ice radar zone, percolation/refreeze radar zone and wet snow, ice wall etc., were identified. The accuracy of classified maps was estimated using ground truth data collected during 2013 and 2014 glacier field work to Samudra Tapu and Gangotri glaciers and overall accuracy was found to be in range of 82–90 %. This information of various glacier radar zones can be utilized in marking firn line of glaciers, which can be helpful for glacier mass balance studies.     

Remote Sensing and GIS Based Analysis of Geomorphic Evidences and Morphometry of Active Faults in Kachchh Area

Kachchh basin is a Mesozoic rift basin under the influence of many active faults. This in turn gives rise to marked structural complexity and associated seismicity. Remote Sensing study of geomorphic evidences of these faults has been carried out using satellite images and is validated using morphometric analysis and digital elevation model data. Satellite images not only help in identifying expression of active faults and active tectonics on a macroscopic scale, but also provide the image characteristics of active faults directly. A few faults along with nature of lateral displacement could be identified from the Kachchh area. Morphometric analysis viz., sinuosity, asymmetry factor and hypsometry indicated affected streams and drainage basins due to fault activity.     

Mapping of Coal Fire in Jharia Coalfield,India: a Remote Sensing Based Approach

In India, Jharia Coalfield (JCF) has one of the densest congregations of surface-subsurface coal fires known worldwide. Systematic investigation and quantification of actual scenario of coal fires in JCF is always necessary to plan sustainable mining, industrial growth and environmental remediation on a long term basis. The present approach involves evaluation and mapping of coal fire using ASTER (Advanced Spaceborne Thermal Emission and Reflection) data. Mapping reveals that the area located around western, eastern and south-eastern parts of JCF covering territories of Shatabdi opencast, Barora; Sijua opencast; Godhar colliery; Kusunda; Bokapahari; Kujama and Lodna are under intense fire with cumulative coverage of 6.23 km². The ASTER derived Land Surface Temperature (LST) of the anomalous areas have been subsequently validated by the field observations, carried out in JCF in February, 2010. The methodology adopted in the present study would provide precise evaluation and monitoring of coal fire in Jharia.     

An effective ensemble classification framework using random forests and a correlation based feature selection technique

Accurate classification of heterogeneous land surfaces with homogeneous land cover classes is a challenging task as satellite images are characterized by a large number of features in the spectral and spatial domains. The identifying relevance of a feature or feature set is an important task for designing an effective classification scheme. Here, an ensemble of random forests (RF) classifiers is realized on the basis of relevance of features. Correlation‐based Feature Selection (CFS) was utilized to assess the relevance of a subset of features by studying the individual predictive ability of each feature along with the degree of redundancy between them. Predictability of RF was greatly improved by random selection of the relevant features in each of the splits. An investigation was carried out on different types of images from the Landsat Enhanced Thematic Mapper Plus (Landsat ETM+) and QuickBird sensors. It has been observed that the performance of the RF classifier was significantly improved while using the optimal set of relevant features compared with a few of the most advanced supervised classifiers such as maximum likelihood classifier (MLC), Navie Bayes, multi‐layer perception (MLP), support vector machine (SVM) and bagging.     

Trend analysis of precipitation in Jharkhand State,India

Jharkhand is one of the eastern states of India which has an agriculture-based economy. Uncertain and erratic distribution of precipitation as well as a lack of state water resources planning is the major limitation to crop growth in the region. In this study, the spatial and temporal variability in precipitation in the state was examined using a monthly precipitation time series of 111 years (1901–2011) from 18 meteorological stations. Autocorrelation and Mann–Kendall/modified Mann–Kendall tests were utilized to detect possible trends, and the Theil and Sen slope estimator test was used to determine the magnitude of change over the entire time series. The most probable change year (change point) was detected using the Pettitt–Mann–Whitney test, and the entire time series was sub-divided into two parts: before and after the change point. Arc-Map 9.3 software was utilized to assess the spatial patterns of the trends over the entire state. Annual precipitation exhibited a decreasing trend in 5 out of 18 stations during the whole period. For annual, monsoon and winter periods of precipitation, the slope test indicated a decreasing trend for all stations during 1901–2011. The highest variability was observed in post-monsoon precipitation (77.87 %) and the lowest variability was observed in the annual series (15.76 %) over the 111 years. An increasing trend in precipitation in the state was found during the period 1901–1949, which was reversed during the subsequent period (1950–2011).     

Rice-acreage estimation for Orrisa using remotely sensed data

Rice is one of the most important foodgrains grown in India. Attempts have been made to estimate kharif rice acreage of Orissa state since 1986 using digital remote sensing data from Landsat MSS/TM and/or IRS-1A. Accuracies of the estimates obtained have been evaluated against BES (Bureau of Economics and Statistics) estimate. This paper describes the methodology adopted for rice acreage estimation of Orissa state, the results obtained for three years, i.e. 1986–87, 1988–89 and 1989–90, and their accuracy.