Saumitra Mukherjee

Forest fire risk zone mapping from satellite imagery and GIS

A forest fire can be a real ecological disaster, regardless of whether it is caused by natural forces or human activity. It is impossible to control nature, but it is possible to map forest fire risk zones and thereby minimise the frequency of fire, avert damage, etc. Forest fire risk zones are locations where a fire is likely to start, and from where it can easily spread to other areas. Anticipation of factors influencing the occurrence of fire and understanding the dynamic behaviour of fire are critical aspects of fire management. A precise evaluation of forest fire problems and decisions on solution methods can only be satisfactorily made when a fire risk zone map is available. Satellite data plays a vital role in identifying and mapping forest fires and in recording the frequency at which different vegetation types/zones are affected. A geographic information system (GIS) can be used effectively to combine different forest-fire-causing factors for demarcating the forest fire risk zone map. Gorna Subwatershed, located in Madhya Pradesh, India, was selected for this study because it continually faces a forest fire problem. A colour composite image from the Indian Remote Sensing Satellite (IRS) 1D LISS III was used for vegetation mapping. Slope and other coverages (roads and settlements) were derived from topographic maps and field information. The thematic and topographic information was digitised and ARC/INFO GIS software was used for analysis. Forest fire risk zones were delineated by assigning subjective weights to the classes of all the layers according to their sensitivity to fire or their fire-inducing capability. Four categories of forest fire risk ranging from very high to low were derived automatically. Almost 30% of the study area was predicted to be under very high and high-risk zones. The evolved GIS-based forest fire risk model of the study area was found to be in strong agreement with actual fire-affected sites.

Application of remote sensing technology for land use/land cover change analysis

Land use/land cover changes over a period of 30 years were studied using remote sensing technology in a part of Gohparu block, Shahdol district of Madhya Pradesh. Land use/ land cover maps were prepared by visual interpretation of two period remotely sensed data. Post-classification comparison technique was adopted for this purpose. The loss of vegetation cover was estimated to be 22 percent and 14 percent of the land was found to have been tranformed into wasteland between 1967 and 1996. Overall rate of change was found to be 1.8 percent per year during this period.

SPATIAL-TEMPORAL MONITORING OF GROUNDWATER USING MULTIVARIATE STATISTICAL TECHNIQUES IN BAREILLY DISTRICT OF UTTAR PRADESH, INDIA

Spatial-Temporal Monitoring of Groundwater Using Multivariate Statistical Techniques in Bareilly District of Uttar Pradesh, India Monitoring of groundwater quality in Bareilly district, Uttar Pradesh, India, was performed at 10 different sites during the years 2005-2006. Obtained quality parameters were treated using principal component analysis (PCA) and cluster analysis (CA). The study shows usefulness of multivariate statistical techniques for evaluation and interpretation of groundwater quality data sets. Monitorování Podzemní Vody V Čase A V Prostoru Pomocí Multivariačních Statistických Metod V Oblasti Bareilly, Uttar Pradesh, Indie V letech 2005-2006 byla na deseti odběrných místech v regionu Bareilly, Uttar Pradesh, Indie sledována kvalita podzemní vody. Zjištěné kvalitativní parametry byly zpracovány pomocí analýzy hlavního prvku (PCA) a pomocí shlukové analýzy (CA). Studie dokládá vhodnost multivariačních statistických metod pro vyhodnocení a interpretaci změřených výsledků.

Impact of land-use and land-cover change on groundwater quality in the Lower Shiwalik hills: a remote sensing and GIS based approach

Human activities have exerted small to large scale changes on the hydrological cycle. The current scenario regarding groundwater resources suggests that globally there is a water crisis in terms of quantity (availability) and quality. Therefore there is a great need for the assessment and monitoring of quality and quantity of groundwater resources at local level. This paper presents a case study of the lower Shiwalik hills, in Rupnagar, Punjab, India, to trace land-use and land-cover changes during the past 17 years, with an emphasis on groundwater quality and quantity. This study was performed in alluvial and hilly terrain. The results show that the quantity of groundwater increased with the help of natural and artificial recharge due to change in land-use and land-cover pattern (increased area of fallow land). The quality of groundwater deteriorated due to input of fertilizers for enhancing the short-term soil fertility. Using a Remote Sensing and GIS based approach, we show the final results in map form. In particular we highlight a potential groundwater exploration site, which could be useful for district level planning. Our research shows that the change in land-use and land-cover affects the quantity and quality of groundwater.

Integrated framework for monitoring groundwater pollution using a geographical information system and multivariate analysis

Abstract Appropriate assessment of groundwater is important to ensure sustainable and safe use of this natural resource. However, evaluating overall groundwater quality is difficult due to the spatial variability of multiple contaminants. This research proposes a geographical information system (GIS)-based groundwater quality pollution mapping technique, which synthesizes different available water quality data, normalized with the World Health Organization (WHO) standards. The normalized difference index (NDI) is used to perform the normalization process. This study utilizes a multi-criteria evaluation (MCE) script (MATLAB 10.0), developed to assign weights to each of the analysed water quality parameters. The consistency of judgments of weight assignment is further analysed using the consistency ratio (CR) and consistency index (CI) techniques. The Shuttle Radar Topography Mission (SRTM) C-band radar and Landsat TM satellite image data are used to derive a digital elevation model (DEM) and land-use/land-cover map of the area. A new sensitivity analysis method is introduced to estimate the responsible factors associated with the proposed groundwater pollution zone model (GPZM). Multivariate analysis methods, such as factor analysis (FA), cluster analysis (CA) and principal component analysis (PCA), are used to uncover the latent structure of the data, to understand the correlations across hierarchical levels, and for dimensionality reduction, respectively. Editor D. Koutsoyiannis; Associate editor Chong-yu Xu Citation Srivastava, P. K., Han, D., Gupta, M., and Mukherjee, S., 2012. Integrated framework for monitoring groundwater pollution using a geographical information system and multivariate analysis. Hydrological Sciences Journal, 57 (7), 1453–1472.

Identification and analysis of groundwater potential zones in Ken–Betwa river linking area using remote sensing and geographic information system

The use of remote sensing data with other ancillary data in a geographic information system (GIS) environment is useful to delineate groundwater potential zonation map of Ken–Betwa river linking area of Bundelkhand. Various themes of information such as geomorphology, land use/land cover, lineament extracted from digital processing of Landsat (ETM+) satellite data of the year 2005 and drainage map were extracted from survey of India topographic sheets, and elevation, slope data were generated from shuttle radar topography mission (SRTM) digital elevation model (DEM). These themes were overlaid to generate groundwater potential zonation (GWPZ) map of the area. The final map of the area shows different zones of groundwater prospects, viz., good (5.22% of the area), moderate (65.83% of the area) poor (15.31% of the area) and very poor (13.64% of area).

Geochemical characterization and heavy metal contamination of groundwater in Satluj River Basin

Groundwater, a renewable and finite natural resource, vital for man’s life, social and economic development and a valuable component of the ecosystem, is vulnerable to natural and human impacts. The aim of present study is to evaluate hydrogeochemical parameters and heavy metals in groundwater and to study their spatial distribution in the Rupnagar District of Punjab. The spatial distribution of physico-chemical parameters were studied using Arc GIS 9.2. It was observed that the concentration of parameters, such as NO3, Cd, Cr, Mn and Pb was above permissible limit (World Health Organization, WHO) in southern part of the study area. The heavy metal pollution index (HPI) was calculated for all sampling locations and it was found much above the critical limit of pollution. Geochemical reaction models of selected water groups were constructed using Phreeqc. Geochemical modeling suggests that sodium has source other than halite-albite and calcium has alternate source other than gypsum-carbonate or silicates. It also suggests that evaporites, ion exchange, dissolution along with anthropogenic activities are controlling the hydro-geochemistry of groundwater in the region. Various indices, such as heavy metal pollution index, permeability index, sodium adsorption ratio, were studied to verify suitability of groundwater for drinking and irrigation.

Modeling mineral phase change chemistry of groundwater in a rural-urban fringe

This research paper aims to determine the genetic origin of the chemical elements in groundwater. It deals with the results of physicochemical parameters, to evaluate the hydro-geochemistry of groundwater in rural-urban fringe of district Bareilly, India. Pre- and post-monsoon sampling has been carried out, which reveals inter-seasonal variability effect on the hydro-geochemical processes. Geochemical modeling especially computation of saturation index was undertaken using the WATEQ4F model. Majority of samples fall in the category of undersaturation, which further suggests that groundwater still has potential to dissolve more minerals. Chemical categorizations of groundwater samples were performed with the help of the Aquachem model. Grouping of groundwater on the Piper diagram reveals a common composition and origin. In most of the area, water facies is of Ca(2+)-HCO(3)(-) type in both the seasons. It also indicates that in pre-monsoon, ion exchange is the dominant process, whereas in post-monsoon, both ion exchanges as well as reverse ion exchanges are reported in the groundwater of the study area.

Geochemical modelling, ionic ratio and GIS based mapping of groundwater salinity and assessment of governing processes in Northern Gujarat, India

In semi-arid/arid regions, groundwater is the major source of irrigation, drinking and industrial requirements, water salinity and shortage are major problems of concern. North Gujarat, India, is one such area where highly saline groundwater is generally ascribed to rapid increase of population, agriculture and industries induced decline in water table by unplanned abstraction of groundwater. However, no effort has been made to discriminate the natural and anthropogenic influences on groundwater salinity. In this brief background, the present study attempts to identify the factors and processes controlling the groundwater salinity in the area, based on ionic ratios in integration with various graphical methods, saturation indices and geographical information system. Na+/Ca2+ > 1 indicates the deficiency of Ca2+ possibly due to CaCO3 precipitation or ion exchange process. Na+/Cl− > 1 and \( {\text{SO}}_{4}{}^{2 - } /{\text{Cl}}^{ - } \gg 0.05 \) suggest salinization is mainly due to wastewater infiltration and/or due to irrigation water return flow. Sea water intrusion in coastal parts, vertical and lateral mixing of water and anthropogenic inputs are also responsible for salinization of groundwater. USSL diagram, Na%, sodium adsorption ratio, residual sodium carbonate and magnesium hazard indicate unsuitability of groundwater for irrigation purposes. To prevent groundwater salinization, appropriate measures need to be taken to control further indiscriminate exploitation of groundwater for irrigation.

Computational Intelligence Techniques in Earth and Environmental Sciences

Computational intelligence is a group of computational models and tools that encompass elements of learning, adaptation, and/or heuristic optimization. It is used to help study problems that are difficult to solve using conventional computational algorithms. Neural networks, evolutionary computation, and fuzzy systems are the three main pillars of computational intelligence. More recently, emerging areas such as swarm intelligence, artificial immune systems (AIS), support vector machines, rough sets, chaotic systems, and others have been added to the range of computational intelligence techniques. This chapter aims to present an overview of computational intelligence techniques and their applications, focusing on five representative techniques, including neural networks, evolutionary computation, fuzzy systems, swarm intelligence, and AIS.