eChapters

It was in April-May 1968 that the country witnessed the wonderful spectacle of large arrivals of wheat grain in the mandis of the Punjab like Moga and Khanna. Wheat production in the country rose to nearly 17 million tonnes that year, from the previous best harvest of 12 million tonnes. Indira Gandhi released a special stamp titled “Wheat Revolution” in July 1968, to mark this new phase in our agricultural evolution. The nation rejoiced at our coming out of a “ship to mouth” existence. Later in 1968, Dr. William Gaud of the U.S. referred to the quantum jumps in production brought about by semi-dwarf varieties of wheat and rice as a “Green Revolution.” This term has since come to symbolise a steep rise in productivity and, thereby, of production of major crops.

Wheat production this year may reach a level of 85 million tonnes, in contrast to the 7 million tonnes our farmers harvested at the time of Independence in 1947. I visited several grain mandis in Moga, Khanna, Khananon and other places in the Punjab in April 2011 and experienced concurrently a feeling of ecstasy and agony. It was heart-warming to see the great work done by our farm men and women under difficult circumstances when, often, they had to irrigate the fields at night due to lack of availability of power during the day.

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.

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.

1. Introduction
Crop health valuation plays a vital part in modern agriculture as it positively stimulates food security, economic constancy, and ecological sustainability. This process involves monitoring and evaluating the growth and development of crops, finding possible stressors or diseases, and executing suitable management practices. Judicious and precise evaluation of crop health permits early exposure of diseases, pests, and other irregularities. Detecting these problems at their early growth stages can aid prevent extensive occurrences and decrease the need for unnecessary pesticide use, indorsing justifiable crop management practices. Assessing crop health on a regular basis help in enhancing yields by detecting factors that could inhibit crop productivity. Agricultural systems can be made resource-efficient by incorporating tools, modern technologies and information management systems that come under the precision agriculture concept. Modern scientific improvements, comprising Global Positioning System (GPS), grain yield monitoring system, Variable Rate Technologies (VRT), sensor networks, and Femote Sensing (RS), have assisted farmers to detect in-field heterogeneity of crop and soil and adopt site-specific farm management practices. Varying the application of inputs with VRT can decrease the cost of inputs and labor, increase productivity, and decrease the potential environmental influences from the overuse of farm inputs. However, the success rate of VRT needs precise and consistent information on the location and extent of crop and soil health conditions, such as crop nutrient shortages, soil and crop water deficiency, and injury caused by insect pests, weeds, or pathogens. So far, the dominant remote sensing technologies that the farming sector has used are visible (VIS), Near-Infrared (NIR), and short-wave infrared (SWIR) sensors. The use of thermal sensors in agriculture is to some extent narrow despite its wide use in the areas of medical science (Ring and Ammer, 2012), intelligence/military (Hinz and Stilla, 2006), and the food processing sector (Vadivambal and Jayas, 2011). By providing temperature variations of soil and crop canopy, thermal remote sensing has the prospective to be used for various

Abstract

Being the junction between the continental and the oceanic plates, the coastal zones are always tectonically alert and hence vested with number of aults. The study of LANDSAT ETM FCC imagery revealed one such a major fault along NNE-SSW aligned Tamil Nadu coast from Chennai in the North northeast to Cape Comorin in the South southwest. This Precambrian boundary fault shows signs of repetitive reactivation during Gondwana, Tertiary and Late Holocene periods. While in former two periods it shows indications of faulting, in the latter period, it is manifested in the form of drainage deflections indicating sinistral movements of beds. While such sinistral morphology of the fault seems to be related to the Post collision compression, the seismicities in and in its close proximities indicate the seismic significance of this fault. In the context of natural resources and disasters viz: geothermal springs, flooding and seismicities, this fault demands deeper studies.

Introduction

The coastal regions in general form the junction between the continental and oceanic plates. Hence, the coastal regions are always tectonically active and thus vested with coast parallel, tangential and conjugate faults depending upon the tectonic history of the area. In the Indian subcontinent too, all along the 9000 km long east and west coast margins, number of major faults were mapped by the earlier workers. Significantly, Vardarajan and Ganju (1989) have brought out a comprehensive picture on the lineaments of the entire coastal zone of India using Landsat images. Vemban et al. (1977) earlier inferred faults along certain segments of east coast of Tamil Nadu and observed them to be lithological contacts and also seismogenic in nature. Raiverman et al. (1966) have again inferred earlier a N-S curvilinear fault in Thanjavur deltaic region and attributed it to post drift kinematics of the Indian plate. Ramasamy (1989) has brought out the morphotectonic and morphodynamic model of the east and west coasts of India on the basis of tectonic and geomorphic signatures using Landsat satellite images. Ramasamy (2006) developed a first time comprehensive model on the active tectonics of the Indian Peninsular and in which he brought out detailed information on the varied geomorphic, tectonic and hydrological anomalies and inferred them to be related to the whirling and fracturing of the Indian plate due to the still prevailing N–S to NNE – SSw compressive force which originally drifted the Indian plate to the present position after the breakup of the Gondwana land.

The National Food Security Bill, 2011 designed to make access to food a legal right, is the last chance to convert Gandhiji’s vision of a hunger-free India into reality.

Mahatma Gandhi’s articulation of the role of food in a human being’s life in his speech at Noakhali, now in Bangladesh, in 1946 is the most powerful expression of the importance of making access to food a basic human right.  Gandhiji also wanted that the pathway to ending hunger should be opportunities for everyone to earn their daily bread, since the process of ending hunger should not lead to the erosion of human dignity.  Unfortunately, this message was forgotten after the country became independent in 1947, and government departments started referring to those being provided some form of social support as “beneficiaries”.  The designation “beneficiary” is also being applied to the women and men who toil for 8 hours in sun and rain under the Mahatma Gandhi National Rural Employment Guarantee Programme (MGNREGA).  Sixty-five years after Gandhiji’s Noakhali speech, we find that India is the home for the largest number of under- and malnourished children, women and men in the world.  There are more persons going to bed partially hungry now, than the entire population of India in 1947.

Livestock and livelihoods are intimately linked in our country.  Mixed farming involving crop-livestock as well as sometimes fish integration has been both a way of life and a means to sustainable livelihoods.  Also, the ownership of the livestock is more egalitarian than that of land.  That is why in our ancient culture, livestock has been given a pride of place in the area of spiritual veneration and natural resources conservation.  Maharashtra is rich in its livestock resources and has also a long coastline with a wide range of living aquatic resources.

The crop-livestock integrated farming system provides an opportunity for promoting organic farming and restoration of soil health, and thereby of an ever-green revolution leading to an enhancement of productivity in perpetuity without associated ecological harm.  It also strengthens household nutrition security.  The country as a whole has over 500 million farm animals.  However, grazing land has been reduced to less than 5 per cent of the arable land area.  Village common property resources, which used to provide fodder and shelter to farm animals, have been overexploited.  Consequently, our farm animal productivity is very low.

Transformation of data is useful when the assumptions required for analysis of variance are not adequately satisfied. Forestry experiments may produce observations showing skewness, unequal variances, non-normality, or departures from additivity. This chapter explains the need for transforming observations to an appropriate scale before statistical analysis. It discusses the characteristics of an ideal transformation, including stabilisation of variance, improvement of normality, efficient estimation of means, and achievement of linear and additive effects. Various transformation techniques used in forestry experimental analysis are introduced with suitable examples. The chapter emphasizes that appropriate transformation can improve the validity, sensitivity, and interpretability of statistical tests.

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:

Introduction

The term geographic information systems (GIS) has come to mean, variously, an industry, a product, a technology, and a science. As such, the term invokes different perceptions dependent on whether the viewpoint is that of the software developer, the system marketer, the data provider, the application specialist, or the academic researcher, among others. A newcomer to the field is likely to be bewildered by the multiple uses of the same term, or, seen alternatively, different definitions of the same term. For instance some find it distasteful to use of the conventional acronym GIS and prefer Geospatial Technology. With all the variety of perceptions predicting the future of GIS is a cumbersome business. The most certain inference we can gain from the available trends is, it is growing and is poised for further growth. Geotechnology along with Nanotechnology and Biotechnology is considered as of the three most promising growth industries for the near future.

Abstract

The recent tsunami occurred on 26^thDecember 2004, caused great devastation along the east coast of Tamil Nadu, India. As the said tsunami has caused colossal damages, the impact assessment has become essential for tsunami mitigation measures. In this context, the extent of tsunami inundation, loss of lives, property loss and damages to physical and natural resources have been assessed in the tsunami affected area between Ennore and Mahabalipuram along the east coast of Tamil Nadu.

IRS LISS III PAN merged satellite data and IKONOS data were used for mapping the pre-tsunami geomorphology and land cover conditions and IRS-P6 PAN merged satellite data were used to map the post – tsunami terrain conditions including the inundations. From the study it is inferred that wherever buildings and population are denser the loss is more. Hence, vertical expansion may be needed as an alternative or to supplement horizontal expansion. Bio-shield along coastal areas, involving raising of mangrove forests, planting of casurina, salicornia, palms, bamboo and other tree species and halophytes which can grow near the sea will serve as speed-breakers for the up rising waves.

Keywords: Tsunami, Damage Assessment, Mitigation Measures, Chennai region.

Abstract

Ennore is an industrial region having many industries on chemicals, fertilizers, refineries etc., discharging their effluents into the Buckingham canal. Present study focuses on the impact of tsunami on water quality of Buckingham Canal segment found to the north of Chennai city, Tamil Nadu, India, in addition to the human interferences. Water samples were collected from surface and near bottom at five locations covering about 20km stretch of the Buckingham Canal during high tide on the spring tide day before (December 2004) and after (January 2005) the mega tsunami. Various water quality parameters measured were pH, Temperature, Salinity, Dissolved Oxygen, Suspended Particulate Matter, Nutrients and Chlorophyll. The maximum depth observed in the canal was 2.5m in December, whereas a shallow depth of less than 0.5m was recorded in January. The water quality analyses confirmed the incursion of saline water (upto 32ppt) into the Canal. Not much variations were observed in Dissolved Oxygen and pH. Because of the organic pollution in the canal, the water was rich in nutrients before tsunami, whereas due to the large quantity of seawater invasion, dilution had occurred after tsunami. High turbidity was observed in January 2005 indicating the tsunami induced disturbance and resuspension of the sediments from sea bed.

Keywords: Tsunami, Ennore, Tamil Nadu, Buckingham Canal.

Abstract

During natural disasters, mostly panic and curiosity lead to greater causalities. But the tsunami is such a disaster which no one have witnessed during their life time nor heard of from their ancestors. On 26, December 2004, tsunami has struck all of a sudden many South Asian coasts including the east coast of Tamil Nadu and Andhra Pradesh causing greater devastation and causalities. This historical event has taught us many lessons and the paper deals with some aspects related to tsunami from the citizen’s view point.

Keywords: Natural calamities, Tsunami mechanisms, Mitigation strategies, Citizen’s perspective.

INTRODUCTION

The tsunami that ravaged the coasts of South India had left tell tale signs that affirm the need for early warning systems as well as well-planned mitigation measures for the benefit of the affected. Over the last 12 months, since the tsunami struck the east coast of India on 26 Dec. 2004, numerous seminars, discussions, programmes and project proposals have been deliberated on various aspects that deserve attention. Government agencies, self-help groups and non-governmental organizations (NGOs) have done commendable post-tsunami work. All sections of the society have responded both emotionally and rationally to the event in an unprecedented manner. In retrospect, to the informed citizen – one who has heard about the event, read about it and discussed it with others, though not have had the ‘experience’ of the natural disaster by himself / herself - what do all these mean ? the tragedy and its aftermath. This paper documents the issues that a citizen would like to ponder over so as to understand the various dimensions of the post-tsunami activities and draw a few lessons that the tragedy had taught us. The major issues pertain to (i) the mechanisms (ii) the coastal vulnerability and the need for assessment (iii) mitigation strategies (iv) media exposition (v) communication networking and (vi) educational facilitation.

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.

Abstract

The weather or climate is a natural resource and is an important basic input in agricultural planning and strategies. Every plant process, related with growth, development and yield of a crop and each of inseason and off-season farm operations (according to World Meteorological Organization) is more than 10 per cent and can reach up to 50 per cent in smaller areas and not a surprise even if it reaches to 100 per cent on micro scale. Amongst the various weather elements, temperatures, radiation and rainfall play major role in deciding the crop growth and yield levels. Precipitation is one of the important weather factors being responsible for atmospheric and soil moisture and therefore has more agricultural importance, especially in rainfed agriculture.

Keywords: Remote sensing, weather, agroadvisory, GIS

1.0 Introduction

India receives more than 70 per cent of its annual rainfall during South West monsoon season. Indian summer monsoon rainfall shows large intra - seasonal and intra-annual variability (Parthasarathy, 1960; Alexander et al., 1978; Mooley and Parthasarathy, 1984) and also changes in space and time, intensity and distribution. Borgaonkar and Pant (2001) have reported that Indian monsoon rainfall has shown trend less nature since 17^th century based on proxy data with large inter-annual fluctuations. IPCC (2001) has reported increase in rainfall over tropical and higher latitudes and decrease in rainfall over subtropics and mid latitudes.

1. Introduction
In the realm of modern agriculture, achieving sustainability is paramount. The judicious use of resources and the maintenance of soil and crop health are vital components of this endeavor. Remote sensing is a powerful technique used to gather information about the Earth’s atmosphere and surface without direct physical contact (Lillesand et al., 2015). It involves the acquisition, measurement, and analysis of data from sensors aboard platforms such as satellites, airplanes, drones, or ground-based instruments. These sensors record electromagnetic radiation, providing valuable insights into various aspects of the Earth’s environment. Remote sensing technology has emerged as an invaluable tool for achieving these objectives, offering a range of benefits for sustainable agriculture (Jensen, 2009). With the advent of modern technologies such as hyperspectral remote sensing (HRS) that collects information in narrow contiguous bands, many new avenues of applications have come up. This detailed spectral information enables the precise identification and classification of objects based on their unique spectral signatures. Further, the narrow spectral reflectance provides significant information regarding the biophysical, biochemical, and physiological parameters of crops. These characteristics of HRS provides opportunities for improved feature identification, anomaly detection, and quantification. Often HRS data also complement to the remote sensing data, such as LiDAR or SAR (Synthetic Aperture Radar), allowing for a more comprehensive understanding of the Earth’s surface and its features. All these features of HRS enables precision agriculture, allowing the management practices as per the specific needs of the fields. By assessing soil and crop conditions remotely, one can apply resources like water, fertilizers, and pesticides precisely where and when they are needed. This minimizes waste and reduces the environmental impact of agricultural operations. Hyperspectral remote sensing can also assess critical soil parameters such as moisture content, pH levels, and nutrient concentrations. Monitoring these factors over time helps farmers make informed decisions about soil management and the choice of suitable crops. It also aids in preventing soil degradation and erosion. Early detection of stressors like pests, diseases, and nutrient deficiencies is crucial for maintaining crop health and maximizing yields. Remote sensing provides a timely and accurate means of monitoring crop conditions across large areas, enabling rapid responses to threats and reducing yield losses. In regions with water scarcity, efficient water management is essential. Remote sensing can track soil moisture levels and assess irrigation needs. By optimizing irrigation practices, farmers can conserve water resources while maintaining crop productivity. Remote sensing, particularly hyperspectral remote sensing can detect changes in

1. Introduction
Global food insecurity is a prominent concern today, and this concern continues to haunt with exponential population growth, coupled with significant climate changes. The global population has increased at a rapid rate to around 8 billion in number which has led to a corresponding rise in food demand worldwide (UNFPA, 2023). Consequently, ensuring ample crop production has emerged as a primary objective for the entire world. According to World Bank (2023), an important strategy to bring down extreme poverty levels, fostering shared prosperity and providing sustainability for the expected population rise of 9.7 billion is agricultural development by 2050. Nevertheless, optimizing the productivity of a crop is never an easy task. Crop plants continuously face exposure to different abiotic and/or biotic factors, which retard growth and development processes ensuing their reduced productivity and diminished crop quality. Agricultural crops are vulnerable to biotic stresses such as bacteria, nematodes, fungi and herbivores. In addition to biotic factors, these challenges are further exacerbated by abiotic stresses like flooding/waterlogging, drought, temperature variations, soil salinity/alkalinity, heavy metals, nutrient deficiencies and CO2. They have a detrimental impact on crop at different stages of plant growth, ranging from entire plant to tissue and sub-cellular structures. Moreover, the abiotic stressors cause a direct impact on crops by inducing protein denaturation and aggregation, while also increasing the fluidity of membrane lipids (Kumari, 2022). According to Oshunsanya et al. (2019) major abiotic stresses i.e. extreme temperatures, drought, and nutrient deficiency/toxicity, contributes to significant annual crop yield losses ranging from 51-82% worldwide. On the other hand, yield penalties due to waterlogging merely is supposed to increase from 3–11% today to around 10–20% by 2080 (Liu et al., 2023). Daryanto et al. (2016) reviewed the published literatures from 1980-2015 and reported global yield reductions due to drought estimating around 21 % and 40%, respectively in wheat and maize.

Introduction

Soil is a dynamic natural body, characterized by high degree of spatial variability due to combined effect of physical, chemical or biological processes that operate with different intensities at different scales (Goovaerts 1998). Reports have shown that there is large variability in soil properties, crop yield, disease, weed etc., not only in large-sized fields (Godwin and Miller 2003,Vrindts et al. 2005), but also in small-sized fields (Bhattacharya et al. 2008). Therefore, knowledge of hydro-physical properties of soils is crucial for management practices for all kinds of agricultural practices for efficient utilization of input resources. Plant growth and development is highly related to soil hydro-physical properties, which not only depend on soil intrinsic textural and chemical characteristics (Hamza and Anderson 2005) but also on soil management.

Soil physical properties which change with the variation in soil structure include bulk density, total porosity, pore geometry (size distribution, shape, continuity and tortuosity), penetration resistance and aggregate stability, consequently affecting soil hydraulic properties such as water retention characteristics, plant available water capacity, infiltration capacity and saturated hydraulic conductivity. These soil properties are dependent on seasonal climatic conditions, management practices, crop development and biological activity (Reynolds et al. 2007). The hydro-physical properties of soils, i.e., water retention and water permeability in both saturated and unsaturated zones not only shape soil water balance but also decide the conditions for plant growth, development and yield. They also determine water availability for the plant root system and the transfer of water with chemical compounds dissolved in it into deeper layers.

Vector data analysis is performed to convert raw input vector data to output that provides new and meaningful information which ultimately supports decision making and disclose hidden patterns. There are various techniques to analyse vector data like buffering, intersect, union, and so on. Some of the most commonly used techniques with their R-code examples are explained in this chapter.

First of all, you need to import all the packages which are required for performing this exercise. These packages include rgdal and sp for importing and re-projecting vector data as well as raster and rgeos for performing vector analysis operations.

Vector data consists of points, lines and polygons with x, y coordinates known as vertices representing the geographic location i.e. latitude and longitude. Vector data depicts the shape of the spatial feature. Points consist of single x, y locations. Examples of points are tower location, sampling location, etc. Lines represent 1 – dimensional features like roads, streams, etc., and are composed of many or at least two connected points or vertices. Polygons denote 2 – dimensional features like boundaries with three or more vertices that are connected and closed. The vector data are represented in the following figure (fig. 5.1).