eChapters

Introduction to GIS

QGIS (Quantum GIS) is an open-source Geographic Information System (GIS) software has been used to showcase various exercises pertaining to viewing, editing, analyzing, and printing of the geospatial data. QGIS helps to allow the users to easily access and manage geographic data stored in folders on local disks or relational databases available on the user’s network. QGIS supports raster, vector, and mesh layers. QGIS also allows using data from external sources with the help of web services such as Web Map Service (WMS) and Web Feature Service. QGIS is a constantly developing software as anyone can add new features or update existing tools. Newer versions of QGIS are frequently released on the official website. It is a cross-platform software that can be installed in Windows, Linux, and MacOS.

Introduction

Rainfall is one of the climatic variables that varies very highly spatially and temporally. Understanding this micro-level variation is a crucial indicator for planning future water requirements and preparing for management strategies. Numerous researchers have delved into the distribution, variability, and trends of rainfall across diverse spatial scales, ranging from global to regional and basin levels. Nair et al. (2014) analyzed the rainfall trend and seasonality index for Kerala state in India over the last 100 years. The rainfall trend was found to be decreasing significantly in almost all the regions of the state. Nischitha et al. (2013) provide insights into the spatial and temporal variability of daily monsoon rainfall in the Tunga and Bhadra River basins in Karnataka state. Paradkar et al. (2019) evaluated the pattern of weekly rainfall by using statistical parameters such as mean, standard deviation, skewness, and kurtosis for five stations in Pratapgarh district.

Geographical Information System (GIS) proved to be an effective tool in handling information and analyzing rainfall patterns, providing visual representations, and facilitating the decision-making process (Kadir et al. (2016) and Rao et al. (2017)). Anand et al. (2020) analyzed the long-term spatial and temporal trends of rainfall in the Lower Bhavani basin in Tamil Nadu, India using GIS and statistical methods. Earls et al. (2007) compared different interpolation methods such as Inverse Distance Weighting, Spline, and Kriging (ordinary and universal) to generate a 30 m representation of the rainfall data. Spatial interpolation techniques perform differently and their evaluation should not rely solely on quantitative assessment. Wu & Hung (2016) compared the performance of three spatial interpolation techniques i.e., IDW, kriging, and spline using quantitative assessment, 2D visualization, and 3D visualization. IDW and kriging performed similarly in terms of quantitative assessment, while spline performed worse. IDW and spline performed similarly in 2D visualization, while kriging showed different behavior. Kriging was observed to be an inexact interpolation in 3D visualization, while spline tended to create extreme values along the edges of the study area. Some studies discovered that the accuracy of Kriging in the study area is higher than that of IDW or Thiessen interpolations (Earls et al. 2007; Song et al. 2008; Chen et al. 2010). GIS applications have the capability to integrate spatial data and non-spatial data like rainfall and to visualize, model, and analyze how rainfall distribution varies across space and over time, providing insights into climate change, water resource management, agricultural planning, and disaster management. The present study aims to analyze annual rainfall data pertaining to Kerala state in the GIS environment.

Introduction

Climate change and human activities have emerged as significant factors affecting water resources in diverse regions (Kumar et al. 2022; Haddeland et al. 2014). The escalation in human water consumption for domestic and industrial purposes enhanced by the impacts of climate change has resulted in an overall decline in water availability (Liu et al. 2017; Paul et al. 2021). Rising temperatures and altered precipitation patterns directly influence water availability for ecosystems and agriculture potentially leading to greater evapotranspiration rates, intensified rainfall events, and prolonged droughts (Sharannya et al. 2021; Myers et al. 2023). Furthermore, urbanization, deforestation, and agricultural practices alter landscapes, affecting water resources in these areas.

Water shortages pose threats to ecosystem health, and socio-economic development and disrupt agricultural production (Piao et al. 2010). Thus, ensuring sustainable and efficient water usage necessitates accounting for water usage and quantifying it in a changing environment. To comprehensively assess the significance of water resources in the environment, Falkenmark (1995) introduced the concept of blue water (BW) and green water (GW) water, particularly for managing water resources in water-stressed regions. Green water refers to the portion of precipitation that infiltrates into the unsaturated soil layer and is subsequently utilized by plants through transpiration and soil evaporation. In fact, “blue water” represents surface and groundwater flow in rivers, lakes, and aquifers (Abate et al 2024; Sharannya et al. 2021). While numerous studies have investigated the spatial and temporal variation of BW and GW using the Soil and Water Assessment Tool (SWAT) (Feng et al. 2021; Li et al. 2009; Lyu et al. 2019), fewer publications have focused on assessing these variations and uncertainties at the basin scale under varying climate scenarios (Sharma et al. 2023). Hydrological models such as SWAT, serve as valuable tools for simulating past and future hydrological changes in the watersheds using projected climate data. SWAT specifically can simulate both blue and green flows at the watershed scale.

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.

Introduction

Watershed management has emerged as an important research area in recent years. Understanding the morphology of the catchment is one of the widely used methods for watershed management. Morphometry is the measurement and mathematical analysis of the configuration of the earth’s surface, shape, and dimension of its landforms (Agarwal 1998; Obi Reddy et al., 2002). The study of watershed is very useful in drainage basin evaluation, silt erosion control, flood frequency analysis, watershed prioritization, natural resources management, and conservation (Waikar and Nilawar, 2014). The significance of morphometric analysis is highlighted in the basins that are either un-gauged or difficult to access. The morphometry is usually analyzed through measurement of linear, aerial, relief, gradient of channel network, and contributing ground slope of the basin (Magesh et al., 2012). The hydrological behavior of a basin can be easily determined through the areal parameters (Mahala, 2020.) The evolutionary history of any basin can be best understood through the implication of different relief morphometric measures of drainage basin (Sharma and Sarma, 2013).

GIS and RS techniques are proven convenient methods for morphometric characterization of sub-watersheds and prioritization of watersheds. As the satellite images provide a synoptic view of a large area, the RS and GIS methods become very useful in the analysis of drainage basin morphometry (Rai et al., 2017). Thus, the morphometric analysis using spatial techniques has emerged to be the fast and efficient method as compared to most of the conventional methods (Rao et al., 2010) such as manual extraction of drainage network and assigning the stream order from a published Survey of India topographic map. The present study aims at using remote sensing and GIS technology to compute various parameters of morphometric characteristics of the Chaliyar River basin.

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

Introduction

Crop models can be broadly classified into two categories: dynamic or process-based crop simulation models (CSMs), and reduced form statistical models. Since simulation models are based on various plant-related processes, they develop or alter in tandem with plant growth (Chetty 2009); as a result, they are inherently nonlinear (Olmstead 2009). To run, calibrate, and validate, they require a significant amount of date input. However, statistical models are helpful in situations where there is a lack of data because they describe the relationships between a subset of variables while keeping the others constant. (Chetty 2009).

Introduction

Nanopesticides are pesticides that use nanomaterials or nanotechnology to improve their effectiveness and safety in agriculture. Nanomaterials are materials that have at least one dimension between 1 and 200 nm. Nanomaterials have different properties than their bulk counterparts, such as higher surface area, reactivity, and mobility. Nanopesticides can be formulated in various ways, such as nanosized active ingredients, molecular active ingredient nanocarrier complexes, or active ingredients encapsulated or coated by nanomaterials (Deka et al., 2021; Xu et al., 2022).

Introduction

Nanofertilizers are products of nanotechnology that are created or modified from conventional fertilizers, bulk fertilizer materials, or plant extracts. They use different methods such as chemical, physical, mechanical, or biological processes to produce nanosized particles that can enhance soil fertility, crop productivity, and quality (KhetiGaadi 2022). Nanofertilizers have a size range of 0.1–100 nm, which is similar to the size of plant cell wall (20 nm), making them easier to be absorbed by plants (Tiwari 2022).

Introduction

Nanotechnology is a scientific and technological field that manipulates matter at the nanoscale to create, design, characterize, and use structures, systems, and devices with novel properties (Sattler 2016). Nanoparticles are one of the most important products of nanotechnology, as they exhibit different features from their bulk counterparts due to their size and shape (Sabir et al., 2014). Nanoparticles have applications in various disciplines, such as materials science, medicine, agriculture, physics, and chemistry.

Introduction

Precision farming, also known as precision agriculture, prescription farming, and site-specific farming, is grounded on the principle of managing fields based on their heterogeneity (Nowak 2021). In the past, crop management decisions were made for the entire field, treating it as a homogenous unit, despite significant variations in soil characteristics, weed infestations, and plant growth within a field (Nowak 2021). Precision agriculture integrates modern technology with past experience to manage farming on a grid-by-grid basis (Nowak 2021).

Introduction

In order to cater to the nutritional needs of a rapidly growing population, it is imperative to increase food grain production on the same land while maintaining the soil’s productive capacity. This necessitates comprehensive research to establish a scientific foundation for enhancing and sustaining food production and soil productivity with minimal environmental impact (Rathore et al., 2010). Balanced nutrition extends beyond the application of nitrogen, phosphorous, and potassium in specific quantities through fertilizers. It entails ensuring that the soil contains the right proportions of available nutrients to meet the crop’s requirements for optimal yield (Rathore et al., 2010).

Introduction

To address the escalating food demands of the global population, a transformation in the agricultural sector is imperative (Velten et al., 2015; Lampridi et al., 2019). The current overdependence on finite resources is leading to their overexploitation, largely due to irresponsible usage (Velten et al., 2015; Lampridi et al., 2019). By adopting a site-specific approach, farmers can apply inputs only where they are necessary in the field (Velten et al., 2015; Lampridi et al., 2019). This method can enhance output, decrease labor costs, and mitigate the environmental damage caused by excessive input use (Velten et al., 2015; Lampridi et al., 2019).

Introduction

Site-specific nutrient management (SSNM) is the dynamic, field-specific management of nutrients in a particular cropping season to optimize the supply and demand of nutrients according to their differences in cycling throughsoil–plantsystems(Sarkar etal.,2017).Thisnewmethodofnutrient recommendationsisprimarilybasedontheinitialnutrientstatus(INS)from thesoil andthecrop’sneedfornutrients toachieve thedesiredyield.

Introduction

Agriculture is a vital sector in the world economy, as it helps to reduce poverty and promote sustainable development, especially in developing countries. However, agriculture also faces challenges such as increasing productivity and reducing environmental impacts. Therefore, new technologies and methods are needed to improve agricultural practices. One of these methods is precision agriculture (PA), which is based on the principle of sustainable agriculture and healthy food production. PA is a science that uses high technology sensors and analysis tools to improve crop yields and support management decisions. PA is a new concept that aims to increase production, reduce labor time, and optimize the management of fertilizers and irrigation processes.

Introduction

Crop discrimination is the process of using remote-sensing technologies to identify and characterize different types of crops based on their electromagnetic behavior at different wavelengths. Each crop has a unique spectral signature that reflects its response to the incident radiation. However, the spectral signature can also vary depending on the season, the angle of the sensor, crop’s features, illumination intensity, weather phenomenon, and topography among other external factors (Parida et al., 2023).

Introduction

A thematic layer is defined as a layer with a set of logical feature data placed under single theme such as soil type, pH level, salinity, and water table. The features in these layers determine real world objects and are created using geometries such as points, lines and polygons (Tickoo 2017). Thematic layering is the spatial representation of analyzed data of elements that belong to the same type. Thematic maps are used to create these layers, which show how one or more specific data themes vary across different geographic areas.

Introduction

Spectral signature: A spectral signature is the unique pattern of reflection and emission of a target at different wavelengths. This difference helps to classify remote sensing images based on the spectral signatures of different objects.

Electromagnetic (EM) spectrum: The EM spectrum is the range of all types of EM radiation. Some examples are visible light from a lamp, radio waves from a radio station, microwaves, infrared light, ultraviolet light, X-rays, and gamma rays.

Spectral response patterns: A spectral response pattern is the amount of energy that an object reflects or emits at different wavelengths. It shows how the object interacts with the EM radiation.

Introduction

We use our five senses to perceive the world around us. Some senses, like touch and taste, need contact with the objects. But we get most of our information about the environment through sight and hearing, which do not need close contact with the external objects. In scientific terms, we are doing remote- sensing all the time. Remote- sensing is the process of recording/observing/ perceiving (sensing) objects or events from a distance. Remote- sensing is a way of monitoring and detecting the features, usually physical, of a specific area by measuring its reflected and emitted radiation (usually from satellite or aircraft).

Introduction

Global Positioning System (GPS) is a system of satellites and ground stations that provide location and time information for any place on earth, regardless of the weather. GPS is a type of GNSS, which stands for global navigation satellite systems. GPS has many applications in precision farming, such as farm planning, field mapping, soil sampling, tractor guidance, crop guidance, variable- rate applications (VRAs) and yield mapping. GPS helps farmers work in low- visibility conditions such as rain, dust, fog, and darkness. India has its own GPS system called IRNSS or NavIC, which uses about seven satellites. GPS is owned and operated by the U.S. government, specifically the U.S.

Introduction

A geographical information system (GIS) is a kind of database that has software tools for handling, analyzing, and presenting spatial data and descriptions of phenomena that have a location component. This system can also involve human users and support staff, procedures and workflows, a body of knowledge about relevant concepts and methods, and institutional organizations in a wider sense. GIS technology is a key part of spatial data infrastructure, which the White House describes as “the policies, technologies, standards, human resources, and related activities that are necessary to acquire, process, distribute, use, maintain, and preserve spatial data.”

Definition of Disaster

A disaster can be defined as a sudden or catastrophic event that causes significant damage, destruction, and disruption to human life, property, or the environment. Disasters can be natural, such as earthquakes, hurricanes, floods, wildfires, or they can be human-made, including industrial accidents, terrorist attacks, or technological failures.

Disasters often result in the loss of lives, injuries, displacement of people, infrastructure damage, and the interruption of essential services like electricity, water, and communication systems. They can have long-lasting impacts on communities, economies, and the environment.

Governments, organizations, and communities implement disaster preparedness, response, and recovery strategies to mitigate the effects of disasters, protect lives and property, and facilitate the rebuilding and restoration of affected areas.

Remote sensing can be a useful tool in engineering geological investigations, particularly in the identification and characterization of geological features and hazards.

Some of the ways that remote sensing can be used in engineering geological investigations include:

Geological mapping: Remote sensing data can be used to create detailed maps of the geological features in an area, including rock formations, faults, and folds. This information can be used to identify potential hazards, such as landslides and rockfalls, and to plan engineering projects accordingly.

Landslide detection and monitoring: Remote sensing data, such as satellite imagery, can be used to detect landslides and monitor their movement over time. This information can help engineers to design and implement appropriate stabilization measures.

Ground deformation monitoring: Remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR), can be used to detect ground deformation caused by geological processes such as subsidence, uplift, and faulting. This information can be used to identify potential hazards and plan engineering projects accordingly.

Remote Sensing in Oil Exploration

Remote sensing plays a significant role in the oil exploration industry. It refers to the use of sensors and instruments to gather information about the Earth's surface and its environment from a distance. This information can be used to locate and evaluate oil and gas deposits.

There are different remote sensing techniques used in oil exploration, including

Seismic surveys: This technique involves creating sound waves underground and recording their reflection to determine the subsurface structure. It is commonly used in oil exploration to locate potential oil and gas deposits.

Magnetic surveys: This technique uses a magnetometer to measure the magnetic field of the Earth's surface. Magnetic anomalies can indicate the presence of underground geological structures that may contain oil or gas deposits.

Gravity surveys: This technique involves measuring variations in the Earth's gravitational field, which can indicate the presence of underground geological structures.

Remote Sensing in Mineral Exploration

Remote sensing is a powerful tool used in mineral exploration to study and identify minerals and mineral deposits from a distance. This technology allows geologists to gather information about the Earth's surface and subsurface without having to physically access the site. Here are some ways remote sensing is used in mineral exploration:

Mapping Geology: Remote sensing can be used to map geology by identifying surface features associated with mineral deposits. Geologists use images collected by remote sensing satellites and aircraft to create detailed maps of the geology and topography of a region. These maps can help identify areas that are likely to contain mineral deposits.

Mineral Identification: Remote sensing can help identify specific minerals by analyzing the reflectance or absorption patterns of light. Different minerals have unique spectral signatures that can be detected by remote sensing instruments. By analyzing these signatures, geologists can identify the type and abundance of minerals present in an area.

Geomorphological mapping is the process of creating detailed maps that illustrate the physical features of the Earth's surface, including landforms, soil types, vegetation, and water resources. These maps can be used to better understand the underlying geology and topography of a region, and can be used for a variety of purposes, such as land-use planning, environmental management, and geological hazard assessment.

Geomorphological maps are typically created using a combination of field observations, aerial photography, and remote sensing data. Field observations involve physically visiting a site and making detailed observations of the landforms, soil types, and vegetation. Aerial photography involves taking high-resolution photographs of the Earth's surface from an airplane or satellite, which can be used to create detailed maps of the area. Remote sensing data can also be used to create geomorphological maps, including data from satellites, radar, and LiDAR (Light Detection and Ranging) systems. These technologies allow scientists to gather data about the Earth's surface without physically visiting the site, and can provide highly accurate information about the topography, geology, and vegetation of an area. The resulting maps can be used for a variety of purposes, including land-use planning, natural resource management, and hazard assessment. For example, geomorphological maps can be used to identify areas of high risk for landslides, floods, or earthquakes, and can be used to develop strategies for managing these hazards.

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.

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.

India is a geologically diverse country with a wide variety of rock types. The major rock types found in India can be broadly classified into the following categories:

Igneous Rocks: These rocks are formed from the solidification of molten magma. They can be further divided into two types:

1. Plutonic (Intrusive) Igneous Rocks: Examples include granite, diorite, and gabbro. These rocks are formed deep within the Earth's crust and have a coarse-grained texture.
2. Volcanic (Extrusive) Igneous Rocks: Examples include basalt, andesite, and rhyolite. These rocks are formed on the Earth's surface from volcanic eruptions and have a fine-grained texture.

Sedimentary Rocks: These rocks are formed by the deposition and consolidation of sediments over time. They can be further divided into various types based on their composition and mode of formation:

1. Clastic Sedimentary Rocks: Examples include sandstone, shale, and conglomerate. These rocks are formed from the accumulation and lithification of fragments of pre-existing rocks.
2. Chemical Sedimentary Rocks: Examples include limestone, dolomite, and rock salt. These rocks are formed from the precipitation of minerals from water bodies.

Lithological interpretation of igneous rocks involves the identification and description of the different types of rocks that form as a result of the cooling and solidification of magma or lava. Igneous rocks can be classified based on their mineral composition, texture, and the way they formed. Here are some of the common types of igneous rocks and their characteristics:

1. Basalt

Basalt is a dark-colored, fine-grained rock that forms from the rapid cooling of lava. It is typically composed of minerals such as pyroxene, plagioclase feldspar, and olivine.

Basalt is a common extrusive igneous rock formed from the rapid cooling of basaltic lava. It is typically dark in color, ranging from black to dark gray, and has a fine-grained texture. Basalt is composed mainly of plagioclase and pyroxene minerals, with smaller amounts of olivine, magnetite, and other minerals.

Basalt is found in many parts of the world, including the oceanic crust, volcanic islands, and some continental regions. It is also used as a construction material, such as in road paving, because of its durability and resistance to weathering. Additionally, basalt fiber is used in various applications, including reinforcing materials in composites, insulation, and other industrial uses.

Basalt in India: Basalt is a type of igneous rock that is commonly found in India. It is a dark-colored, fine-grained rock that is rich in iron and magnesium minerals.

Traditionally, remote sensing encompasses a wide array of technologies and methods used to gather information about objects, areas, or phenomena without direct physical contact. This includes devices like seismographs, fathometers, radar systems, and more. These tools enable us to observe and measure various aspects of the environment from a distance.

However, in contemporary contexts, when we talk about remote sensing in a more specialized sense, we often refer to the use of satellite and aerial-based systems to capture and analyze electromagnetic radiation reflected or emitted from the Earth's surface. This form of remote sensing has become incredibly valuable in fields such as environmental monitoring, urban planning, agriculture, forestry, and disaster management, among others.

Modern remote sensing techniques leverage sensors capable of detecting specific wavelengths of electromagnetic radiation, such as visible light, infrared, and microwave radiation. By analyzing the patterns and intensity of this radiation, researchers can derive valuable information about the Earth's surface features, including vegetation cover, soil moisture, land use, and changes over time.

Astro-meteorology is considered as one of the branches of Astronomy along with Astrophysics, Astrobiology and Astrometry. Astronomy is the science that deals with the material universe beyond the earth’s atmosphere. It studies the nature and constitution of Celestial (heavenly) bodies and the laws which govern them including the effect they produce upon one another. It seeks to unravel the probable past history of heavenly bodies and their development. There exists a confusion between the branches of Astronomy viz., Astrophysics and Astro-meteorology with the term Astrology. Astrophysics uses principles and laws of physics to explain how Celestial bodies form and function or in other words it will help to ascertain the nature of the astronomical objects, rather than their positions or motions in space. However, the Astrology and Astro-meteorology are the subjects deal in future prediction and often considered as pseudo-science which uses position and movement of stars and planets for that purpose. Astrology links these to predict the human behaviour, while that of Astro-meteorology links the same to predict the future weather.

From ancient days, these predictions are based on one’s wisdom and knowledge in astronomy and often these astrologers are astronomers who keenly watch the stars and planets movement and guide their rulers and farmers with their guesstimates. Till date it is being lore, mainly passed to followers of this group and the methodology is not documented clearly and properly. There are many incidences of falsified predictions of astrology as reported by many reviewers elsewhere and we are not discussing the same here, owing to our interest in weather prediction i.e., astro-meteorology only. However, we have to recognize the works of the ancient astrologers who helped in developing the astronomical science.

Basically, there are two approaches in astrology viz., geocentric and heliocentric as used by the practitioner of astrology worldwide. As the name implies the Geocentric astrology is based on the calculation of planetary position as seen by the observer from the earth and thus earth is centre, while Heliocentric astrology bases its interpretation upon position within solar system with reference to the sun as centre. Though in true sense, the sun is the centre, the effect of motion as manifest on the earth is the basis of most astrological interpretation. Hence, vast majority of astrologers employs the geocentric calculations of the planetary positions for their predictions.

Livestock population meet the protein requirement of humans in addition to meeting from plants. The food chain starts from fodder crops to animals and from animals to humans. In human physiology, digestion of plant protein is much easier than animal protein, but however the digestion depends on climate (temperate climate, sub-tropical climate, tropical climate etc.,) wherein human kind live. Similar to crops, there is strong interaction between livestock and meteorological weather elements (temperature, relative humidity, wind etc.). But detailed study has not been published elaborately in scientific journals in the past. The weather elements have greater influence on livestock productivity in terms of milk, meat, skin, wool etc., pest and disease infestation and their housing needs. In this chapter how weather elements influence shed management, pest and disease problem and animals’ productivity and are discussed. In majority of the Veterinary and Animal Science University in India, they have one division on animal climatology, wherein both research and extension aspects are being attended. However, in depth research is the need of the present decade, since little works have been done in the past on animal climatology.

Irrespective of weather elements, the heat stress due to the combination of temperature and relative humidity plays important role in affecting the productivity of animals. There is a range of thermal conditions within which animals are able to maintain a relatively stable body temperature by means of behavioral and physiological means. Heat stress results from the animal’s inability to dissipate sufficient heat to maintain homeothermy (Sejian et al., 2017). High ambient temperature, relative humidity and radiant energy compromise the ability of the animals to dissipate heat. As a result, there is an increase in body temperature, which in turn initiates compensatory and adaptive mechanisms to re-establish homeothermy and homeostasis. Homeostasis refers to the tendency to maintain a balanced or constant internal state that is optimal for functioning. Animals have a specific balanced or normal body temperature. When there is a problem with the internal functioning of animal body, this temperature may increase, signaling imbalance. As a result, body attempts to solve the problem and restore homeostasis for normal body temperature. These readjustments generally referred as adaptations and this is essential for survival of the animals.

8.1 Climate Change

Climate change is a broad term of global negative phenomena as created predominantly by burning of fossil fuels, which add heat-trapping gases to earth’s atmosphere. The over accumulation of greenhouse gases in the atmosphere lead to increased temperature and this is called as global warming. As a result, sea-level rise; ice mass loss in Greenland, Antarctica, Arctic and mountain glaciers worldwide; shifts in flowering/ plant blooming; and extreme weather events occur.

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.

Under normal weather situation, agriculture depends on timely occurrence of rainfall for crop management activities irrespective of different farming situations namely, irrigated and unirrigated agriculture. Changes in temperature and rainfall pattern together with frequent occurrence of extreme weather events are major anticipated threats from the climate change that occurs now around the world. This threat in long run, if not addressed properly, would affect food security of a country. Pratap Narain et al., (2000) observed an increase in moderate and severe drought occurrence numbers in the arid zone of India during last decade (1991-2000) as compared to the earlier decades of the last century. Similarly, Pasupalak (2008) reported daily maximum rainfall of 400.3 mm in the last decade against earlier record of 256.4 mm (1969–78) in a location at Odisha State. Further the inter- annual variation within the decade did increase and also the number of rainy days with very heavy rainfall > 125 mm also found increased in the location against the daily mean rainfall of 20 mm/day in India.

A day may be with heavy rainfall or with bright sunshine or windy or in combination of these. When heavy rainfall continues for more than a week with some system like cyclone, floods may be the result. Similarly, if dry spell continues during a cropping season for more than 15 days, drought would initiate in that region. This is again depending upon the nature of the soil water holding capacity and nature of soils, topography and green cover of that region. In between these two extremes, we have normal season, where we get normal seasonal rainfall during a cropping season. Hence like dress code, in agro-climatology, there are three weather codes, namely floods code, drought code and normal code. The jargon weather code was developed by Dr.M. S Swaminathan in India. Across the countries of the World, if these weather codes occur, the impact would be universal. The strength of adaptation capacity to maneuver these extreme events by the farming community rests with their past experience made for similar situations, financial support and also the technology options that are available from the scientific institutions.

5.1 Introduction

A Crop Simulation Model is a mathematical model which describes crop growth and development processes as a function of environment, soil conditions, and crop management. Simulation model is a schematic representation which works on set of underlying assumptions, equations and algorithms, closely replicating the behaviour of the agricultural systems. A crop growth simulation model predicts the harvestable yield. Apart from this final state of crop production, the model also provides a host of quantitative information about major processes involved in the various stages of crop development. The information covers each component of the crop such as leaves, stems, roots and harvestable products as they change over time, including the changes in soil moisture and nutrient status.

An increase in the population, demands an increase in agricultural production with available resources. Efficient management of available resources under varying weather conditions is important for increasing agricultural productivity. Besides that, the emphasis of agricultural production changes from quantity to quality and sustainability (Aggarwal et al., 2000). Solution of these new challenges lies in understanding the interaction of the numerous elements affecting the plant growth and generating a crop growth model. This extremely complicated information needs to be processed and interpreted on a spatial scale. Information technology which has unleashed new frontiers in data analysis and automation is the driving force behind this model generation. These powerful computer programmes simulate the crop growth or yield of crops under various management regimes and provide near real time information to help farmers to make strategic decision in the management of their crops.

4.1. Introduction

Many countries have set up a network of agrometeorological observatories to regularly record important weather parameters such as clouds, rainfall, surface temperature, humidity, wind and wind direction, solar radiation, soil moisture, temperature and humidity profile of the atmosphere, etc. The observation process is performed either manually or through Automatic Weather Stations (AWS).

This recorded information is used for weather forecasting, natural resources management and for tracking agricultural production. However, these networks, which handle data of high spatial climate variability, are typically less robust than they should be. Moreover, the agrometeorological data collected from different ground stations is not transmitted in real time to a central collection point which limits the use of information.

Satellite remote sensing technology is an alternative system that is gaining popularity in the recent times which has allowed us to obtain frequent and precise measurements of vital agrometeorological parameters such as rainfall, solar radiation, temperature, evapotranspiration, albedo etc. Satellite data has many advantages over traditional ground stations, such as high spatial resolution (50 m–5 km), wide spectral bandwidth range (0.4–3.75 um) and high temporal frequency (even half an hour) and the prospect of collecting data from remote areas. In addition to routine repeated observations, satellites often provide synoptic views of the earth’s surface from space on a wide scale and have archive of information to create baseline data.

The preparation, planning and use of agricultural technologies for agriculture, needs application of agricultural meteorology. The role of agrometeorology in the last three decades has increased in terms of increasing food and nutritional security for the burgeoning population and natural resource management particularly in the context of global climate change. Agrometeorology can help to minimize inputs, thus clarifying the contribution of ecosystems and agriculture to the carbon budget in the event of global change. Agrometeorological information needs to be disseminated to users in time and space quite accurately, as agricultural activities are linked to the very local situation where the outputs of modern tools such as local area models, climate models, and dynamic crop simulation models are reduced to finer grid resolution and combined using GIS to extract practical suggestions. This chapter describes the recent applications of remote sensing in Agrometeorology.

The subject on micrometeorology deals with small scale meteorology of near ground surface with its related elements of both physical and biological processes. When this micrometeorology is integrated for crop production purposes, then it becomes crop micrometeorology, which is very important in the science of agricultural climatology. This crop micrometeorology gets varied with the architecture of the crops grown, available soil moisture, agronomical practices followed, soil texture, topography of the area, the nature of climate and weather that prevails etc. The soil surface heat, exerts a profound frictional effect on wind movements and hence it is different from the air layers at higher levels. These special features constitute the microclimate, which is modified compared to a flat bare surface by factors like topography, nature of soil surface, presence of vegetation and type of vegetative cover. The presence of crop canopy leads to distinct differences in the thermal and humidity regimes of the soil, the air layers immediately above it and the distribution of radiation and wind profiles inside the canopy. The study on the physical exchanges of the transfer of heat, momentum and moisture in the microclimatic region is called as micrometeorology (Venkataraman and Krishnan, 1992).

Radha Krishna Murthy (2002) reported that the micrometeorology is the micro scale of both the physical and meteorological factors that are taking place within the ecosphere. The ecosphere means the area between just above the crop canopy and just below the root zone of a crop. Further he was on the opinion that the microclimate of valley is important as compared to the entire climate of the mountain for the bio-life of valley. For the crops purpose, the microclimate is the climate of the ground surface to top of the crop canopy or otherwise it can be called as crop’s climate, a modified one from the Stevenson’s Screen climate/weather observation.

The climate, the past weather event that existed over larger region and covering long years of more than 30 years is very important for the emergence and successful sustenance of an organism including human kind. Normally scientists talk about environment and this includes not only the soils but also the climate of the particular region. Based on this, during 1989, the Planning Commission of India divided the entire India in to 15 agro-climatic zones based on physiography and climate. This division is only for planning purposes. Subsequently under NARP, the Indian Council of Agricultural Research (ICAR), New Delhi in consultation with the Planning Commission, requested all State Agricultural Universities (SAU) to divide their State in to different sub climatic zones considering soils, temperature and other weather parameters preciously with in the major agroclimatic zone of India in which the concerned State falls. Accordingly, each SAU has divided their State into different agro-climatic sub-zones and this resulted in 129 agro-climatic sub zones in India and limited to 127 and these all fall within 15 agro climatic zones of India as done by the Planning Commission of India.

The National Bureau of Soil Survey and Land Use Planning (NBSS&LUP), Nagpur not satisfied with this division, considering soils, temperature, rainfall, vegetation and all factors responsible for ecosystem (bio-climate, length of growing period, moisture index) divided the whole India in to 20 agro-eco regions by 1992 and revised preciously again by 2015 (Mandal et al., 2016) and the revised approach is being considered as scientific tool and taken for micro-level planning in India now and those are discussed at this chapter.

1.1 Weather Parameters

The atmosphere is a gaseous component surrounding the earth as an integral part and its interaction with ocean (water bodies), other planets of the solar system and land (Earth) generates one physical product namely weather elements (temperature, pressure, wind, humidity, precipitation, etc.,) and these form the weather and climate of the Earth. The process of generation of weather and climate of the region is purely natural and cannot be modified by the human being. But they can do adaptation to the existing climate and weather for their sustainable life. The weather can be defined as day to day change in the atmosphere in terms of heat and moisture exchange, while the climate is average of long period weather covering larger area or a region.

The particular climate of a region dictates/permits the genesis of a particular bio-organism in that domain and this selected organism’s growth and multiplication are triggered by the prevailing weather of that particular climate. Hence weather parameters of the atmosphere are very important to human being and other organisms of the Earth. The details on weather elements are given in question and answer section of this book (Annexure-I)

Maps make it easier to interpret and communicate the results of any GIS analysis. R has a large number of packages for making thematic maps. Static mapping is straightforward with plot()methods provided by core spatial packages sf and raster. The use of plot() function have been discussed in the previous chapters. Another package for mapping isspwithspplot() having enough options for producing good quality thematic maps.It is also possible to create advanced maps using ggplot2. This chapter discusses, separately, preparing thematic maps from vectors and rasters with the above mentioned packages. A dedicated map-making package tmaphas been given special focus in this chapter.

Elevation or height data of land surface above a defined vertical datum is stored in the form of raster grids which is known as Digital Elevation Model (DEM). The DEM data can be processed to derive many useful parameters like slope, aspect, curvature, roughness, etc. This process of quantitatively describing terrain obtained from DEM data through computation of many variables is known as Digital Terrain Analysis. Digital terrain analysis has applications in the various fields like hydrology, ecology, geomorphology, etc. DEM data for digital terrain analysis are available from the satellites like ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), Cartosat, ALOS (Advanced Land Observation Satellite), and SRTM (Shuttle Radar Topography Mission).

In this exercise, we are going to discuss the derivation of some primary and secondary terrain attributes from DEM data in R software. For performing digital terrain analysis in R, following packages are required.

Supervised classification is the technique to classify raster image in which prior information about the classes in the form of training sites is provided by the user. Training site represents a homogeneous area in a particular class. The spectral response pattern of training sites is used to classify the raster image.The pixels of raster imagewhich have spectral response pattern similar to that of training sites of a particular class are grouped in that class. As there is some variability in the spectral response pattern of a particular class, therefore, multiple training sites per class are taken for supervised classification. Supervised classification of raster images can be performed by various methods. Conventional methods include maximum likelihood classifier, minimum distance to mean classifier, and parallelepiped classifier. Now a days, machine learning algorithms are also gaining importance in this context. These classifying algorithms are first trained or calibrated using the data of training sites.This trained model is then validated on the independent datasets for accuracy assessment. If the accuracy of the developed model is of acceptable level, then that model is used for the classification of raster data.

In this chapter, we are going to discuss the execution of maximum likelihood classifier and few machine learning techniques for supervised classification of raster image in R software. The training sites used in this exercise are in the form of points shapefile. The raster image is classified into six classesi.e. built- up, fallow soil, riverine sand, salt-affected soil, vegetation, and water. For performing this exercise, first of all,set the working directory and import the required packages.

Unsupervised classification is the technique of classification of raster data in which no prior information is provided by the user. Raster data is classified based on their spectral characteristics such that the pixels having similar spectral properties in the multispectral feature space are grouped together in one class. Unsupervised classification is mainly performed by clustering algorithms. The purpose of these algorithms is to minimize within cluster variability and maximize between clusters variability. The user must interpret and label the classes after performing unsupervised classification. This method of classification requires minimum data which makes it easy and quick, but user has toput efforts in identification of the classes. This method could be useful when user doesn’t have any prior knowledge about the study area. This method removes subjectivity and enables us to get the unique classes which might be unrecognized by the user. K-means is the most commonly used method for performing unsupervised classification. In this chapter, we have discussed K- means, CLARA and unsupervised random forest method for classification of raster data in R.

Raster transformations involve mathematical manipulation of raster layers to generate new raster images. These transformed raster images either contain the information of interest or highlight the features which are not discernible in the original image. Transformations are also performed to preserve the essential information in reduced number of dimensions or layers. In this chapter, we are going to discuss few most commonly used transformation tools listed below:

Remote sensing images have an important characteristic known as spatial frequency. Spatial frequency is defined as the number of changes in pixel value per unit distance. Few changes in pixel or brightness value over an area in an image are known as low spatial frequency area like agricultural fields, forests, water bodies, etc. If the changes are large, then it is known as high frequency area like roads, built up areas, etc. Spatial filtering is an approach for extracting quantitative spatial information by taking into account the neighbouring pixel values along with the independent pixel value. Therefore, filtering is a technique for modifying or enhancing an image. Filtering is done to improve the interpretability of raster image or to extract certain information from them. It is a neighbourhood operation because the value of any pixel in the output image is determined by applying certain algorithm on the neighbourhood pixels of the corresponding input pixel. The nature of algorithm to be applied depends upon the information which user wants to derive from the raster image. Filtering operations are mainly done for smoothening, sharpening, noise removal and edge enhancement. A filter is a kernel of small array which is applied to input pixel and its neighbouring pixels in the raster. There are many types of filters which can be applied to a raster image in R. These types with their respective R – code examples are explained further.

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.

The calculation of raster data statistics is very essential before performing some geoprocessing operations. Raster data statistics involve knowing the distribution of pixel values in a band, comparing the pixel values of two bands, extracting values of all bands for a particular geographical location or point, and computing statistics like mean, median, standard deviation, etc. at pixel level across all bands of raster dataset.

In R, codes to perform raster data statistics are present in raster package. The visualisation of raster data statistics can be done by rasterVis package. So, we need to import these packages in R in order to perform this exercise.