eChapters

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.

1.1 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)

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.

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.

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.

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.

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.

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.

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.

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.

Introduction

A system is a simplified version of reality and is defined as a collection of interrelated parts that work together. Most systems share certain common characteristics. These common characteristics include the following:
Systems have a structure that is defined by its parts and processes. Systems are generalizations of reality.
Systems tend to function in the same way as nature. This involves inputs getting changed in a process and realization of new outputs.
The various parts of a system have functional as well as structural relationships between each other.
Owing to the functional relationships that exist between the parts there is a flow and transfer of some type of energy and/or matter. In case of information systems there is a flow of information in the structure.
The parts that make up a system show certain degree of integration. In other words the parts work well together.

Introduction

In a technology that consistently maintains a fantastic development curve with capabilities doubling up year-to-year, freezing the hardware specification is not easy or very meaningful. GIS (not geography) owes to its genesis and progress to the developments in information technology front. It may be asserted that any and all upgrade in the hardware industry will only add to its own ability to effectively process more and more data and handle more complex routines. The personal computers of today are seen to be adequate to run typical GIS software. A reference hardware specification is discussed here. GIS software development is an involved process but then the software developers have made good use of hardware capabilities available and have released upgrades consistently. Hardware developments are such that they defy a static spcification. However a reference specification as on 2009. An overview of various software packages used in the past as well as present is attempted. ESRI released a new version in 2002 and claimed that their ArcGIS is an “integrated collection of GIS software products for building a complete GIS that enable users to deploy GIS functionality wherever it is needed, in desktops, servers, or custom applications; over the Web; or in the field.” The details are discussed below. While a number of software overviews are available, this text is not aimed at providing a tutorial to any of the software packages nor do the comparisons made are to promote any particular software. Similarly, it is presumed that the reader is familiar with the hardware elements and functionality. Lot of information about the software packages are available on their respective web sites and the reader is exhorted to look for current, additional and in depth information required in the pages. A brief discussion on Liveware or human resource can be found at the end of the chapter.

Introduction

All spatial data stored in a GIS are associated with a map projection, either implicitly (so-called “unprojected” data stored with raw longitude, latitude coordinates) or explicitly (where the data have been transformed into a known map projection). Map projections are a particularly important feature of spatial data because they introduce error and distortion, since a sphere cannot be transformed to a plane without some stretching and twisting of the sphere’s surface.

The typical GIS package supports anywhere from 5 to 50 map projection types, and most projections may be modified or customized by varying their parameters. This flexibility is important for two reasons First, in order to use two or more layers together in a GIS for visualization or analysis, all data must be projected in the same way. Second, certain types of analyses, such as the measurement of area, are only valid when using certain types of projections (and conversely, are completely wrong when using an inappropriate projection).

Introduction

Data are the most critical element of a GIS. It is reported that more than two thirds of the time and expenditure to develop a GIS is spent on data collection and organization. Understanding and appreciating the criticality of data is of paramount significance to practice GIS. This chapter is complementary to database concepts chapter discussed in Level II and deals with the fundamentals to start with and the concepts of building an information organization.

Data - A Definition and Certain Points to Ponder

“Data” are defined as a body of facts or figures, which have been gathered systematically for one or more specific purposes.”

1.0 Introduction

Population in dry lands of the world is increasing; more fresh water is required in order to meet the growing demands. To meet this challenging situation developing new technologies for identification of potential sites for rainwater harvesting (RWH) is an important step towards maximizing water availability in semi-arid areas.

Abstract

The anomalies observed in the reverine systems have been demonstrated to be the potential tool in deciphering the tectonically active zones. Such various drainage anomalies viz: radial drainages, anomalous compressed meanders, eyed drainage and the youthful floodplains observed in Annamali – Palani - Varushanad hill ranges of the Western Ghats of Tamil Nadu show that the N-S and NE-SW trending faults are tectonically active in the recent years.

Abstract

The Remote Sensing and the followed up field based investigations show that the Tamil Nadu coast, India, is facing series of geoenvironmental problems due to tectonic movements and morphodynamic processes. Such problems pose a challenge to the engineering geologists. The paper discusses such challenging engineering geological problems and plausible remedial measures.

Introduction

The concept of database is the approach to information organization in computer-based data processing. (Date, 1995 and Everest, 1986).

A Geographic Information System is as good as the data contained in it. Accurate and efficiently organized data are critical to the durability of the system. Data collection, capture and organization will occupy much of the time in building a GIS solution. The analysis potential of a GIS will depend on the database. It is essential to understand database concepts to practice GIS. It is erroneous to view these GIS databases as mere stores of information. The database is used to abstract very specific sorts of information about reality and organize it in a way that will prove useful during subsequent analysis. The database should be viewed as a representation or model of the world developed for a very specific application.

Conversion of real world geographical variation into discrete objects is done through data models. They represent the linkage between the real world domain of geographic data and computer representation of these features. Data models discussed here are for representing the spatial information.

Introduction

Data input or capture is the operation of encoding the data and incorporating in the database. The creation of a clean error free digital database is a complex and critical task determining the very usefulness of a GIS. GIS data represent not only where the graphic or cartographic features occur, but also the associated attributes of them.

Data input to a geographical information system involves

(a) Entering the spatial data (digitizing);

(b) Entering the non-spatial, associated attributes and

(c) Linking the spatial to the non-spatial data.

At each stage necessary verification and checking procedures shall be deployed to ensure an error free database.

Introduction

The GIS database is a model of real world. It goes without saying that there is an inherent discrepancy between GIS database and the real world it represents. This is so because all models are fraught with cognitive issues and remain as approximations. To realize the objectives of a GIS, geographical data collected, entered and processed are to be sufficiently error free and reliable.

Errors can be statistical errors like mistakes, wrong inputs or faulty field surveys or system errors like excessive tolerances or high RMS error. The spatial database has further problems in the sense that the real world phenomena are not always intrinsically discrete. For instance where is the bank of a shallow river? Where is the boundary between two soil types? These are but a few questions revealing the fuzzy nature of geographic data. Many soil scientists, geographers have noticed that carefully drawn boundaries or iso-lines on maps in general give misrepresentations of change that are often gradual, vague or fuzzy. Traditional cartography as a medium of spatial data handling had a solution to such problems. But now when digital data are handled, they need to be stored with certain precision. Moreover, the spatial variation of natural phenomena is not just a local noise function or inaccuracy that can be removed by collecting more data or by increasing the precision measurement but is often a fundamental aspect of nature that occurs at all scales. (Goodchild, 1980; Mark and Aronson, 1984). This means residual unmapped variation is a remarkable problem in geographical information processing. It is therefore a prerequisite to fully understand how errors arise or propagated in GIS and what might be the effects of errors on output.