eChapters

Groundwater exists at many places beneath the surface of the earth. It is normally hidden from view but, on occasion, can be seen flowing from springs and wells or seeping into tunnels. Certain properties of the various geologic materials control the ability of groundwater to enter into, move through, be stored in, or be extracted from the ground. Therefore, an understanding of geology is necessary in order to gain an understanding of groundwater found within a particular area of investigation. A basic part of the study of the geology of an area includes the preparation of a map showing surfacial exposures of each rock type. Interpretation of the map facilitates the study of the underground configuration, characteristics, and physical properties of these materials. This, in turn, enables the evaluation of groundwater storage, movement, and yield, as well as the identification of areas of ground water quality problems, recharge, and extraction.

The fundamental unit of a geologic study is the geologic formation. A formation has been defined as any assemblage of rocks, which have some character in common, whether of origin, age, or composition. In this sense, the term “rock” is defined as any naturally occurring part of the earth’s crust, made up of aggregate of minerals and includes hard, dense granite, lava ash, clay, sand, and even loose-uncohesive soil.

Hypothesis testing provides a statistical framework for making inferences about populations from sample observations obtained through random experiments. This chapter introduces the scientific logic underlying statistical hypotheses and explains the formulation of null and alternative hypotheses. It discusses one-tailed and two-tailed tests, levels of significance, Type I and Type II errors, sampling distributions, test statistics, critical regions, p-values, and decision-making. The chapter also explains statistical significance and the interpretation of test results. Applications of Student’s t-test and related procedures are illustrated through forestry and biological examples, enabling researchers to determine whether observed differences between means can reasonably be attributed to treatments rather than random sampling variation.

The sensors present over remote sensing satellites are designed to collect energy or radiance from the varied surface features having wide range of radiance i.e. from features like water bodies having very low radiance to features like sand or snow having very high radiance. But, most of the time, the study area may not have all these features; therefore, radiance values may remain confined to a narrow range. This reduces the contrast of the image, thereby decreases its visual quality. In this context, image contrast enhancement techniques are used for effective visualization and display of remote sensing image on computer screens or hard copy so that maximum amount of information can be derived through its visual interpretation.

Contrast enhancement of a digital image is a technique to modify the original grey levels of input image to the entire available range of the grey levels. This increases the contrast between various features in the image which improves the visual quality of original image. Contrast enhancement can be done by both linear and non-linear methods. Some of the commonly used contrast enhancement techniques with their implementation in R software are discussed in this chapter.

We need following packages for performing this exercise in R. raster package is required for importing of raster image and for performing contrast enhancement. RStoolbox package is also required for contrast enhancement as well as for visualization of raster data with ggplot2 package. rasterVis package is mainly used in this exercise for plotting histogram of the raster data.

1. Introduction
Global food security threatened by climate change is one of the most important challenges in the 21st century to supply sufficient food for the increasing population on diminishing agricultural land while sustaining the already stressed environment. Global agricultural production is highly vulnerable as key crops such as maize, wheat, rice, and legumes will be negatively affected by climate change. The study showed that each degree Celsius increase in global mean temperature is estimated to reduce the average global yield of cereal crops by 6-15%. Further, predicted changes in climate may affect water availability and key soil processes such as respiration and nitrogen (N) mineralization, thus key ecosystem functions such as carbon (C) storage, nutrients, and water availability. Although crop growth simulation models are being utilized to understand the impact of climate change scenarios on aboveground biomass, the roots and their architecture, which are essential for plant adaptation and productivity, need to be studied more due to the difficulty of observing them during the plant life cycle. The root system and its architecture are highly responsive to external environmental conditions such as soil moisture, nutrients, temperature, pH and microbial communities; therefore, it is high time to study below-ground responses to future climate scenarios. The study of plant roots represents a highly promising yet underexplored area of research related to plant growth. While the aerial parts of plants have received significant attention due to their visibility and accessibility, the underground portions have been largely overlooked due to the challenges in observing and sampling them and the disruption caused when roots are removed from the soil. Plants’ efficiency of water and mineral nutrient uptake largely depends on how effectively new root tips penetrate soil horizons and how these roots eventually die and decompose. The plant’s ability to access soil resources depends on the size of its root system and the activity of its growing root tips. Root growth is influenced by both genetic factors and environmental conditions. For example, fine root dynamics vary with the seasons, with increased vegetative growth and nutrient release occurring during the monsoon months. (Kumar et al., 2020). Further research is needed to assess how different tillage and water management practices affect rooting behaviours, soil water extraction, and nutrient uptake.

Abstract

The major subduction of the Indian plate and the related high magnitude earthquake (>9M) occurred in Sumatra region, triggered a major tsunami on 26, Dec 2004 which caused massive disasters in many coastal regions of South Asian countries including India, killing tens of thousands of life and causing property loss of several million rupees. Duly realizing the credentials of Geospatial / Geomatics technology and the expertise of NRDMS, an all India coordinated programme was conceived by NRDMS on tsunami disaster assessment and got it executed through six specialized institutions working on geomatics technology in the country. In the programme, the tsunami disaster in terms of natural, physical and human resources were assessed using geomatics technology and village wise digital databases were generated which will form a potential baseline information in coastal area.

Keywords: Indian Ocean Tsunami 2004, DST Initiatives, Disaster Assessment, Mitigation.

Introduction

Spatial datasets always exhibit two common features (i) the occurrence of a few very large concentrations (hot-spots) and (ii) the presence of data below the detection limit (black spots). Extreme values can strongly affect the characterization of spatial patterns, and subsequently the prediction. Several approaches exist to handle strongly positively skewed histograms (Saito and Goovaerts 2000). One common approach is to first transform the data (e.g. normal, Box Cox or lognormal), perform the analysis in the transformed space, and back-transform the resulting estimates. Such transform, however, does not solve problems created by the presence of numerous black spot data, since either it yields a spike of similar transformed values or, in the case of the normal-score transform, it requires a necessarily subjective ordering of all equally-valued observations. Moreover, except for the normal score transform (Deutsch and Journel 1998), it does not guarantee the normality of the transformed histogram, which is required to compute confidence intervals for the estimates. Last, the back-transform of estimated moments is not straightforward and can introduce bias if not done properly (Saito and Goovaerts 2000); for example, lognormal kriging estimates cannot simply be exponentiated. Another way to attenuate the impact of extreme values is to use more robust statistics and estimators. The non-parametric approach of indicator kriging (IK) falls within that category (Journel 1983, Goovaerts 2001). The basic idea is to discretize the range of variation of the environmental attribute by a set of thresholds (e.g. deciles of sample histogram, detection limit, regulatory threshold) and to transform each observation into a vector of indicators of non-exceedence of each threshold. Kriging is then applied to the set of indicators and estimated values are assembled to form a conditional cumulative distribution function (ccdf). The mean or median of the probability distribution can be used as an estimate of the concentration of the material in question (Barabas et al. 2001, Cattle et al. 2002, Goovaerts et al. 2005).

Introduction
Following our comprehensive exploration of scientific crop modeling and the rigorous processes of model assessment, we now pivot to an equally vital, yet distinct, realm of understanding weather in agriculture: Indigenous Technical Knowledge (ITK). This chapter, Indigenous Technical Knowledge for Weather Forecasts and Its Validity,” delves into the profound and often overlooked wisdom embedded within local communities worldwide, knowledge systems accumulated over generations that offer unique insights into anticipating future weather patterns.
In the upcoming sections, we will embark on an in-depth examination of the diverse forms that ITK related to weather forecasting takes. This includes detailed discussions on:
• Observational Indicators: How traditional communities keenly observe subtle changes in the natural world, such as specific patterns in animal behavior (e.g., bird migration patterns, insect activity, animal calls), plant phenology (e.g., early or late flowering, unusual fruiting patterns of wild plants), and the behavior of flora and fauna as harbingers of upcoming weather shifts.
• Atmospheric and Astronomical Signs: The interpretation of sky conditions (e.g., cloud formations, specific colors of sunrise/sunset, halos around the sun or moon), wind patterns, atmospheric pressure changes (often perceived through bodily sensations or changes in local sound travel), and celestial phenomena (e.g., star brightness, moon phases) as indicators for short-term or seasonal forecasts.
• Traditional Ecological Calendars and Proverbs: The role of traditional calendars that map out expected seasonal weather based on historical observations, and folk proverbs or sayings that encapsulate generations of empirical weather wisdom in concise, memorable forms, guiding agricultural practices.

Introduction

Groundwater resources are under stress globally due to various reasons including increasing water demand, impending climate change, industrialization, land use/land cover changes, etc. Adequate sustainable development strategies have to be adopted for the management of groundwater resources. Irrespective of the developmental status of countries all over the world, groundwater serves as a crucial resource for meeting fundamental needs. However, increasing population, water demand, surface water contamination, and recent patterns of climate unpredictability have caused increasing reliance on groundwater as reported by several researchers (Adimalla and Taloor 2020; Aladejana et al. 2020; Cuthbert et al. 2019; Green 2016; Hamed et al. 2018; Jasrotia et al. 2019; Khalaj et al. 2019; Li 2016; McGill et al. 2019; Morsy et al. 2017; Taloor et al. 2020).

Even in India, water scarcity has been reported in many parts, though the country receives relatively higher rainfall. However, the rainfall pattern has changed over the last few years. The country has been experiencing significant spatiotemporal changes in rainfall patterns, leading to fewer rainy days and more extreme rainfall, which many studies have attributed to global climate change and anthropogenic influences. The rainfall variability has substantial impacts on water availability, agricultural productivity, and welfare of the 1.2 billion people in India because agriculture in India feeds about 17.2% of the global population and more than 56% of the agricultural land is rain-fed. Rapid and heavy patterns of rainfall result in the draining of most of the rainfall as surface runoff particularly on higher slopes, and reaches the surface water bodies, thereby reducing the infiltration and subsequent groundwater recharge. These variations in rainfall have ultimately affected the groundwater behavior and availability in many river basins.

Abstract

In the present study an integrated remote sensing and Geographical Information System (GIS) based methodology is adopted for demarcating the groundwater prospective zones and identification of artificial recharge sites in the Peddavanka watershed, Andhra Pradesh. In this study Shuttle Radar Topograhic Mission (SRTM) digital elevation model (DEM) with spatial resolution of 90 m and Indian Remote Sensing Satellite (IRS-1A), Linear Imaging Self Scanner (LISS- III) satellite data along with other field and collateral data on geology and well inventory have been utilized to generating the Drainage, Slope, Lineaments and Hydro geomorphology of the study area, which are an essential prerequisites for demarcating water resources and identification of recharge sites. Spatial data integration and analyses are carried out in GIS environment.

Keywords : DEM, Drainage, Hydrogeomorphology, Groundwater prospective zones, Remote sensing, GIS

1.0 Introduction

Groundwater is an important natural resource in arid and semi- arid regions, because most of the places surface water resources are scarce and some places it’s totally absent particularly in hard rock areas. Because in hard rock terrains availability of groundwater is of limited extent and its occurrence is essentially confined to fractured and weathered zones (Saraf and choudhury 1998, Sankar 2002). In these areas groundwater recharge depends on rainfall only. For understanding rainfall recharge mechanism and groundwater management in the watersheds level, first we have to understand the morphometric parameters in the watershed level.

7.1 Importance of Weather Forecast in Agriculture

In India, agriculture is in reality to be a “gamble activity” with the monsoon rain, that means the productivity and profit from agriculture depend upon the goodness of the season. If any malevolent weather situation that happens during a crop season, whether it is at the beginning of the crop season or at the mid of the crop season or at the terminal of the crop season or at the whole of the crop season, the productivity of the crops raised would be under stake. In practice the irrigated crop area is lesser than the area under unirrigated crop in India in any region except the regions where irrigation is routed through perennial rivers. Normally the production from unirrigated crop is 30 to 40 per cent of the same crop grown under irrigated condition in an environment in the same season.

In India, in a region or in a taluk, if the irrigated area is lesser than 30 per cent of total cultivable area, there is every opportunity for the occurrence of food shortage. That means assured production depends upon irrigation rather than managing the crops with seasonal rainfall alone. This clearly indicates that the higher probability of occurrence of crop production risks falls under rainfed/dry land situation. The crop production risk is also common to irrigated area like unirrigated crop but the intensity gets varied. In this context one example that happened in India can be quoted. During December- January of 1982, with the occurrence of heavy rain/snow as a result of prevalence of western disturbance weather system, the wheat crop stood for harvest at Punjab got damaged with a loss of 124 crore of rupees. If this was anticipated earlier through weather forecast development, (which is common in all developed countries) and communicated to the farmers in advance, then the yield loss would have been reduced by following some tactical decisions to be taken either by the farmers or by the concerned Government. At that time, India did not have medium range weather forecast system and hence Indian farmers met with greater losses.

7.1 INTRODUCTION

Web technology revolutionised information technology and GIS of no exception. Analysis of maps available in internet through remote personal computer is called as web GIS. The term web is GIS synonymous with the term internet GIS. Most of the software vendors in the field of GIS have developed their GIS compatible to internet. The advantage of internet GIS is no need to own a GIS software in the personal computer. Maps available in a web site can be analysed for GIS functions as one could do with GIS in standalone personal computer. Applications of web GIS is not limited. Most of the governmental agencies, business people and others put their data in their server and the data could be analysed spatially for the users requirement. Internet mapping is simple, cost effective and reach vast audience.

Internet GIS is useful in field like civil engineering, government organisations, forestry, environment, goescience, utility planning etc.

Introduction

Famous French postmodern philosopher Jean Baudrillard commented on information once. “Information can tell us everything. It has all the answers. But they are answers to questions we have not asked, and which doubtless don’t even arise.” This seemingly paradox observation fits contemporary Internet age perfectly. Internet is a vast repository of information of all kinds and is bursting on its seams. The seeker needs to search with monumental grit and determination to have a full handle on the range of information available for any particular search. A search string for GIS returns more than 1185000 results, in a mere 0.26 seconds. Recommending a comprehensive resource list is not possible as new material gets continually added by way of additions and updates. However for a beginner the list given below will be handy. The flip side is GIS has innumerable resources and a broad range of content. Software vendors, organizations, individuals, universities and companies have provided a wealth of information. The readers are exhorted to make use of the materials to improve their understanding of the subject.

Interpretation of Drainage Patterns Through Aerial Photographs

Aerial photographs can provide a useful tool for interpreting the drainage patterns of a landscape. Drainage patterns are formed by the natural movement of water across the land and the way in which it flows from high to low areas, creating channels and streams that eventually converge into larger rivers and bodies of water. Different types of drainage patterns can be identified through aerial photographs, which can help to understand the underlying geology and topography of the landscape.

Here are some of the different drainage patterns that can be identified through aerial photographs:

1. Dendritic pattern

This pattern resembles the branches of a tree and is the most common type of drainage pattern. It occurs in areas where the underlying rock or soil is uniform and has no preferred direction of erosion. The streams converge into larger rivers, which may flow in a particular direction.

Rock

A rock is a solid, naturally occurring substance composed of minerals or mineraloids. It is typically formed through geological processes, such as cooling and solidification of molten lava or magma, or by the deposition and compaction of sediment over time. Rocks are classified into three main types based on their origin: igneous, sedimentary, and metamorphic.

Igneous rocks: Formed from the cooling and solidification of molten lava or magma. Examples include granite, basalt, and obsidian.

Sedimentary rocks: Formed from the accumulation and compaction of sediment, such as sand, mud, or organic matter, over long periods. Examples include sandstone, limestone, and shale.

Metamorphic rocks: Formed from the transformation of existing rocks under high temperature and pressure conditions. They undergo changes in mineral composition, texture, and structure. Examples include marble, slate, and gneiss.

Rocks serve as the building blocks of the Earth's crust and are an essential part of the geological processes that shape the planet. They can provide valuable information about Earth's history, including the formation of mountains, the movement of continents, and the presence of ancient life forms. Rocks are also used in various practical applications, such as construction, road building, and the production of minerals and resources.

Structural landforms are landforms that are created due to the underlying geological structures of the earth's crust. These structures can include folds, faults, and other features that have been formed due to the tectonic forces that have shaped the earth's surface over millions of years.

Examples of structural landforms include mountain ranges, plateaus, and rift valleys. Mountain ranges are formed due to the upliftment of the earth's crust along a fault line or due to the folding of the earth's crust. Plateaus are flat-topped landforms that are formed due to the upliftment of a large area of the earth's crust. Rift valleys are formed due to the stretching and pulling apart of the earth's crust along a fault line.

Denudational landforms, on the other hand, are landforms that are created due to the process of erosion and weathering. These landforms are formed due to the gradual wearing down of the earth's surface by natural forces such as wind, water, and ice.

Introduction

Quantum Geographical Information System (QGIS) is an open source GIS environment. The software package is available freely in web https://www.qgis.org/en/site/forusers/download.html) and can be installed in a variety of computer platform e.g. Windows, Linux, Mac OS X, Android etc. Latest information on QGIS is available inhttp://www.qgis.org/en/site/. Basically, the QGIS GUI is divided into five areas: (i) Menu Bar, (ii) Tool Bar (iii) Map Legend, (iv) Map View and (v) Status Bar. QGIS allows users to define a global and project-wise coordinate reference system (CRS) for layers without a pre-defined CRS. It also allows the user to define custom coordinate reference systems and supports on-the-fly (OTF) projection of vector and raster layers. All of these features allow the user to display layers with different CRSs and have them overlay properly. QGIS has support for approximately 2,700 known CRSs. Definitions for each CRS are stored in a SQLite database that is installed with QGIS. The CRSs available in QGIS are based on those defined by the European Petroleum Search Group (EPSG) and the Institute Geographique National de France (IGNF) and are largely abstracted from the spatial reference tables used in Geospatial Data Abstraction Library (GDAL). For Post GIS layers, QGIS use the spatial reference identifier that was specified when the layer was created. For data supported by OGR, QGIS relies on the presence of a recognized means of specifying the CRS. In the following sections, few basic GIS analysis using example spatial data from arid western India is discussed. However, readers are advised to go through different tools and functions available in QGIS after installation of the system in personal computers.

Introduction

A geographic information system or geographical information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of spatial or geographical data. The acronym GIS is sometimes used for geographic information science (GIScience) to refer to the academic discipline that studies geographic information systems and is a large domain within the broader academic discipline of geoinformatics. What goes beyond a GIS is a spatial data infrastructure, a concept that has no such restrictive boundaries. In a general sense, the term describes any information system that integrates stores, edits, analyzes, shares, and displays geographic information. GIS applications are tools that allow users to create interactive queries (user-created searches), analyze spatial information, edit data in maps, and present the results of all these operations. Geographic information science is the science underlying geographic concepts, applications, and systems.

GIS is a broad term that can refer to a number of different technologies, processes, and methods. It is attached to many operations and has many applications related to engineering, planning, management, transport/logistics, insurance, telecommunications, and business. For that reason, GIS and location intelligence applications can be the foundation for many location-enabled services that rely on analysis and visualization. GIS can relate unrelated information by using location as the key index variable. Locations or extents in the Earth space–time may be recorded as dates/times of occurrence, and x, y, and z coordinates representing, longitude, latitude, and elevation, respectively. All Earth-based spatial–temporal location and extent references should, ideally, be relatable to one another and ultimately to a “real” physical location or extent. This key characteristic of GIS has begun to open new avenues of scientific inquiry.

Introduction

R is one of the most popular platforms for data analysis and visualization currently available. It is free, open-source software, with versions for Windows, Mac OS X, and Linux operating systems. R is a language and environment for statistical computing and graphics, similar to the S language originally developed at Bell Labs. It’s an open source solution to data analysis that’s supported by a large and active worldwide research community. But there are many popular statistical and graphing packages available e.g. Microsoft Excel, SAS, IBM SPSS, Stata, and Minitab etc., however R has many interesting features so why it is mostly followed by researchers and academicians. Few of the major features of R are given below:

R has become the world-wide language for statistics, predictive analytics, and data visualization. R is not limited to statistics; it provides a wide range of tools for spectroscopy. It offers a wide range of methodologies for spectral data preprocessing, spectral analysis, multivariate analysis of the spectral data, machine leraning models for prediction of properties, and visualization. As an open source project, it’s freely available for a range of platforms, including Windows, Mac OS X, and Linux. An integrated development environment (IDE) for R, R Studio, allows interactive execution of R functions with user interface. RStudio is available in two formats - RStudio Desktop and RStudio Server - both available in free and fee-based (commercial) editions.
1.1. Download and Install R
The R software setup files can be downloaded from any of the widely distributed Comprehensive R Archive Network (CARN). To download R, go to url: https:// www.r-project.org/ and choose your preferred CRAN mirror, a location close to you and follow the steps to download and installation of R and Rtools.
1.2. Download and Install R Studio
Download and Install the R Studio once the base R is installed. Go to https://www. rstudio.com/products/rstudio/download/ and click on download R Studio Desktop with Open Source license.
1.3. Updating R
To update the R, we need the package “installr” and use function “updateR( )”

Introduction

The term “Remote Sensing” was coined in 1950s by Ms. Evelyn Pruitt, a geographer with the U.S. Office of Naval Research, and the term is now commonly used to describe as the science and art of identifying, observing, and measuring an object on the earth surface, without having any direct contact. This process involves the detection and measurement of radiation of different wavelengths reflected or emitted from distant objects or materials through which they may be identified and categorized by class/type, substance, and spatial distribution (National Aeronautics and Space Administration—NASA). Remote sensing technology has been widely applied in various applications such as water resources, agriculture, geological studies, forestry, land use, resource exploration, etc.

Elements of Remote Sensing

The remote sensing process involves an interaction between the incident radiation and the targets of interest, exemplified by the use of imaging systems in which the following seven elements are involved; however, remote sensing also involves sensing of emitted energy and using non-imaging sensors