eChapters

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).

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.

Abstract

The Car Nicobar Island is one of the several islands in Indian Ocean which was severally damaged by the Great tsunami on 26 December 2004. The great tsunami event caused massive devastation, in the form of loss of life and property in south and south East Asia. The objectives of the study are to assess the changes in the coastal features of the Car Nicobar Island using various indicators such as the tsunami height, run- up elevation, water flow direction, erosion, sand deposition and coastal subsidence. Andaman &Nicobar Islands are the summit of the submarine mountain range lying along the great tectonic suture zone extending from Eastern Himalaya to Sumatra and Lesser Sunda via Myanmar border and Arakan hills. The IRS P-6 satellite data has been found to be very useful in the present tsunami assessment studies. The digital analysis of the satellite data was done using the image processing software (ERDAS IMAGINE 8.4) and DTM to quantify the tsunami height (15 m), run up elevation (2-19 m), inundation (295-1203 m) and coastal responses. The settlements and coconut plantations have been severely damaged in the island, which were assessed using GIS analysis of the pre and post-tsunami satellite data.

Keywords: Tsunami, Wave Height, Run-up Elevation, Inundation, Coastal Responses, Remote Sensing, GIS.

Abstract

The objective of the study is to build a framework of Geo-spatial Information System for effective decision making by empowering all the stake holders with spatial and non-spatial data. The Interface of this information system is developed as a deployable windows application using Map Objects in Visual Basic programming language with MS Access database. The practical utility of the proposed information system was explained with a case study in Aquaculture. This geo-spatial information system provides required information through maps based on spatial and attribute quarries. These dynamic maps help in taking effective decisions.

1.0 Introduction

Computer based information systems proved to be very efficient and effective tools for managing data and information to aid in decision making . FAO (2000) proposed agricultural knowledge and information system for rural development (AKIS/RD) to link rural people and institutions to promote mutual learning and generate , share and utilize agricultural technology , knowledge and information. This serves as vehicle for sharing ideas and principles with various stake holders addressing the problems and seeking solutions to rural development. Although the rural development planning is top-down prescriptive approach , in reality the decisions are made by the stake holders based on their perspectives, knowledge and priorities. Problems particularly related to environmental impacts are location specific and contextual. Geo-spatial technologies may serve as better vehicle to store and share all kinds of location specific data and information. The state of mapping and management of geospatial data changed during the recent years with the diffusion of modern geospatial information technologies such as GIS(Geographic information system), GPS(Global positioning system) and image analytical system for remote sensing(RS) data. Adinarayana et al., (2008) proposed a geographical information and communication technology (Geo-ICT) based information system called Grama Vikas( rural development) to assist the user in analyzing the rural information for rural development, planning . During the recent years there is a paradigm shift in GIS applications for rural development particularly in developing countries, by way of integrating top-down approach with bottom-up approach by the stake holders who stand to be affected by the decisions based on these technologies. Emerging concepts include “ community - integrated GIS”, “GIS in participatory research” and ‘participatory GIS”. (Abbot et al., 1998)

Abstract

The East Coast of Tamil Nadu exhibits a spectacular network of deltas formed by the easterly flowing river systems. The study of raw and digitally enhanced Landsat TM and IRS data shows, that these deltas were formed due to multivariate dynamic processes of fluvial, marine and aeolian actions. The wide fan shaped delta observed northwest and north of Madras city spanning out over an area of 700 sq.km is a feature, standing testimony to the PaleoCauvery river systems.

The regional slope and the overall arcuate arrangement of the crescent shaped lakes in the area and the tentacles of paleochannels show, that it is overall a fan shaped delta and its successive growth towards the sea might have been due to the constant progradation of lobes by continuous reworking of preceding lobes. The depressions in between successive concentric lobes have become the lakes. The absence of any beach ridges in such palaeo deltaic regime suggests that, it would be a sub aerial delta only. The overall arcuate shape of the shoreline in between Madras city in the south and Pulicat lake in the north is due to this fan delta. This feature once again symbolises the active river dominated delta. The Palar delta is a ‘cuspate’ one. The Ponnaiyar river has developed a linear delta, along a narrow depression found in between Tertiary uplands. While the avulsion phenomenon indicates the ancient shoreline near Villupuram, the east-west trending micro sandy uplands within the delta suggest, that it is a tide dominated delta.

Abstract

Soil quality assessment has been recognized as an important step towards understanding the long term effects of land management practices within agricultural watersheds. Potential soil quality issues include continued high rates of erosion, losses of organic matter, reductions in soil fertility and productivity, as well as chemical and heavy metal contamination. This case study addressed the spatial variability of soil properties and its quality at the watershed level using geostatistical methods. Soil samples from the 0- to 20-cm depth were collected on a 100- by 100-m grid across an 88-ha watershed at Sakaliseripalli village in the Nalgonda District in Andhra Pradesh State, India. Geostatistical analysis showed that most of the soil parameters were moderately spatially dependent. An assessment framework, that included a minimum data set, linear scoring technique, and additive indices, was used to evaluate the soil quality index (SQI). Through principal component analysis, cation exchange capacity, exchangeable Na percentage, DTPA-extractable Zn, available P, available water, and dehydrogenase activity have been emerged as the most important indicators for evaluating soil quality. A kriged map of SQI was prepared for the watershed. The SQI was found to be higher in irrigated systems than under rainfed conditions. In the study, potential soil loss calculated using the Universal Soil Loss Equation and crop yield were identified as the quantifiable management goals; the results indicated that soils having higher soil quality indices were more productive and less erosion prone.

Keyword : Principal Component analysis, SQI, Watershed, Geostatistics, Potential soil loss

Introduction

Accuracy and uncertainty analysis is an important step in preparation of surface map through geostatistical techniques or digital soil mapping approach. It is because that we rely on a map based on how much accurate it is and the degree of uncertainty associated with the estimates while the map was prepared. Any stakeholder for a soil map especially policy makers is interested in accuracy and uncertainty of the map before taking suitable land management decisions involving soil map as an input. Again the level of accuracy and uncertainty depends on the properties for which the map has been developed. If the property of interest is sensitive to environment or human consumption, then the accuracy level should be very high while the uncertainty level should be very low e.g. heavy metal concentration in soil, quality parameters of water etc.

6.1 INTRODUCTION

Besides simple analysis, GIS is useful to do advanced analysis like network analysis, spatial analysis, 3D analysis and modeling. Different softwares are available to perform such analysis and the above mentioned analysis is done in separate modules of the softwares.

6.2 NETWORK ANALYSIS

Network consists of well connected linear features. Roads, railway line, water distribution system, sewer line, streams are some of the examples of networks. These networks consists of nodes and arcs with designated directions and connection with other linear features. Networks are topology based with attributes for the flow of objects like traffic. Road network will be considered for explaining network analysis.

Road map is digitised with nodes and arcs. Signals, accidents are represented as points. Road is represented as line and two or more roads intersect at nodes. Attributes are added to the nodes and lines. Dynamic segmentation model is used for network analysis. It is built on lines of network. In road network analysis, attributes for travel time, one way street, two way street, right turn, impedance are added to the nodes and arcs. A link is the line running between two nodes. Link impedance is the cost of passing through the link. Travel time is different in links in cities due to varying traffic at different locations. Time taken to pass through a known length of link may give the cost but the travel time in a link is not uniform because of varying speed designated in the link as 20 km per hour and 30 km per hour. Hence it is important to consider the length of link and also the travel time. If the speed limit is 20 miles per hour for a length of 2 miles, then the travel time is 6 minutes (2/20x60 minutes). Also tuning in the network is considered. A road may have a maximum of 12 possible turns excluding U turns. There are right turn, left turn, straight way for each road. Also U turn is possible. This turning takes different time depending on the signal. Straight way takes less time but left and right turn may require some more time. If signals is available then signal time should also be taken into account. Fig 6.1 show the possible turns in a road.

1. Introduction
Fish and fisheries remains to be an important sector for Indian economy providing food, nutrition as well as employment to a large share of the population. Initiating with a production of merely 7.7 lakh ton of fishes in 1951-52, the country was on its way of increasing the production steadily and has now attained the phenomenal success of producing 162.5 lakh ton during 2021-22. Of this total amount, 41.3 lakh ton has been obtained from marine fisheries and 121.2 lakh ton from inland resources (Anon, 2022). For the inland fisheries production, the contribution of fish culture sector was much lower than that from capture fisheries for several decades during the initial phases. However, the production of culture fisheries maintained a consistently increasing trend and has now surpassed in magnitude the production from capture fisheries sector (Fig.1). With the potential areas for fish culture being gradually used up for different purposes, maintaining such high production rates in coming days will be possible only through optimum management of various factors which govern the productivity of the ponds. Among such factors, one important aspect is nourishing the quality of pond soil and water.

1. Introduction
Soil salinity is a global threat which affects crop production and poses challenges to sustainable development in various regions worldwide. Worldwide, salinity and sodicity impact 20% of cultivated land and 33% of irrigated land, respectively (Kumar and Sharma, 2020). Global map of salt–affected soil (SAS) indicated that 0.424 billion hectares of upper soil layer (0–0.3 m) and 0.833 billion hectares of subsoil layer (0.30–1 m) are salt-affected (FAO, 2021) and by 2050, salinization will degrade 50% of agricultural land (Wang et al., 2003). In India, SAS currently occupy 2.1% of total geographical area. Among the SAS, 2.96 million ha is saline and 3.77 million ha is sodic (Narjary et al., 2024). Due to salinity and sodicity, yearly 16.8 million megagram of agricultural produce are wasted in India, costing of ? 230.2 billion (Sharma et al., 2015).
Salt–affected soils are a significant concern in several regions of India. These soils contain high level of sodium salts (NaCl, Na2SO4, NaHCO3, Na2CO3) which adversely affect soil fertility and crop productivity. Here are some areas in India known to have salt–affected soils:
1. Indo–Gangetic Plain: The Indo–Gangetic Plain, spanning across states like Punjab, Haryana, Uttar Pradesh, Bihar, and parts of West Bengal, is prone to salinity issues. Over–irrigation, poor drainage, the presence of salt containing parent materials in the lithology and use of saline irrigation have contributed to soil salinity.
2. Coastal Regions: Coastal regions in India, such as parts of Gujarat, Maharashtra, Tamil Nadu, Andhra Pradesh, Karela, Odisha and Andaman and Nicobar Islands experience salinity problems due to sea water intrusion and the presence of coastal saline soils. These soils are affected by high levels of sodium chloride and other salts, making them unsuitable for cultivation of most arable crops.
3. Arid and Semi–Arid Regions: Arid and semi–arid regions, including areas in Rajasthan, Gujarat, and parts of Madhya Pradesh, face the risk of soil salinization due to low rainfall, high evaporation rates, and poor– quality irrigation water. Waterlogging in these regions can also lead to soil salinization.
4. Irrigation Command Areas: Certain irrigation command areas in different states, including Punjab, Haryana, and Uttar Pradesh, have witnessed salinity problems as a result of improper water management, excessive irrigation, and inadequate drainage infrastructure.

Introduction

Although both maps and aerial photos present a “bird’s-eye” view of the earth, aerial photographs are NOT maps. Maps are orthogonal representations of the earth’s surface, meaning that they are directionally and geometrically accurate (at least within the limitations imposed by projecting a 3-dimensional object onto 2 dimensions). Aerial photos, on the other hand, display a high degree of radial distortion. That is, the topography is distorted, and until corrections are made for the distortion, measurements made from a photograph are not accurate. Nevertheless, aerial photographs are a powerful tool for studying the earth’s environment. 

Because most GISs can correct for radial distortion, aerial photographs are an excellent data source for many types of projects, especially those that require spatial data from the same location at periodic intervals over a length of time. Typical applications include land use surveys and habitat analysis. 

This unit discusses benefits of aerial photography, applications, the different types of photography, and the integration of aerial photographs into GISs.

Introduction

Agriculture contributes to 29.93% of India's GDP with a share of 27% of the world's agriculture production. India is ranked 6th in the world. In Rajasthan significant portion of economy of is agrarian. The agriculture sector of the state accounts for 22.5% share of agriculture in GSDP which is estimated to 18.39% in 2009-10. About 80% of the total population resides in rural area and largely dependent on agriculture as the source of their livelihood. The hot arid region of western Rajasthan occupies 61% of the total geographical area and 39.51% population of the state. During the last four decades the burgeoning human and livestock population and increased societal demands for land based products led to  significant expansion and intensification of agriculture by putting even marginal lands under plough, excessive exploitation  of ground water and importing of surface water for irrigation.

Abstract

Customized application development can deliver/fulfill the specialized and specific needs of the users as well as it provides user- friendly environment to the users. There have been applications developed in the past but now applications developed and coupled with the advantage of GIS technologies for various data-handling, spatial analyses and visualization under variable inputs and spatial scales is an innovative approach developed during recent years.The present work encompasses Spatial Agricultural Information and Decision Support System having five modules - Agro-socio-economic-information, Bio- Physical Information, Crop’s Information & Management, and Agriculture DSS Module. This system, developed in a GIS framework, allows managers and planners to rapidly assess land attributes and link these attributes with maps and related infrastructure. Hence, it can facilitate efficient use of existing resource information. The primary database of the software has a Database Update feature which can be utilized by the user using the software to integrate own data into the primary database of the software. With this integration, the software adapts to new data entered and will enable this data to be visualized in GIS platform. The software has been designed keeping in view the requirements of the departments like Department of Agriculture, Department of Land Records etc. In comparison with the commercial GIS applications, the commercial applications are not cost effective if the primary requirement is visualization enabling decision making and basic spatial analysis. The most features available on such software remain un-utilized or they cannot be used without having GIS knowledge. This application is suitable in such scenarios, providing a simple customizable platform for GIS visualization. This software application fits in and serves the line departments and in a way promoting the GIS in the mountainous terrain as it requires very basic or no GIS skill set to operate.

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.

Introduction
Having meticulously explored the foundations of crop-weather interactions, the development and validation of crop growth models, the wisdom embedded in Indigenous Technical Knowledge, and the systematic organization of Crop Weather Calendars, we now arrive at the culmination of this knowledge synthesis: the Agroadvisory Based on Weather Forecast. This chapter delves into the critical process of transforming raw meteorological data and scientific understanding into actionable, timely, and location-specific advice for the farming community. An agroadvisory, at its core, is a targeted recommendation provided to farmers, guiding them on various agricultural operations by integrating current weather conditions, short- to medium-range weather forecasts, and the specific needs of their crops at different growth stages. It represents the final, practical output of complex agro-meteorological research and forecasting efforts, designed to empower farmers to make adaptive decisions that can significantly impact their yield, resource efficiency, and resilience against climatic shocks. In the upcoming sections, we will comprehensively examine the multi-faceted nature of weather-based agroadvisories. Our exploration will detail:
• The Conceptual Framework: Understanding the underlying principles and objectives of these advisories, emphasizing their shift from reactive responses to proactive planning.
• Essential Components of an Agroadvisory: Breaking down the key pieces of information typically included, such as weather forecasts (temperature, rainfall, wind, humidity), crop-specific phenological stages, potential risks (pests, diseases, extreme weather), and precise recommendations for farm operations (sowing, irrigation, fertilization, pest control, harvesting).

Abstract

Remotely sensed data have become the primary realistic source for natural resources and biomass estimation, since last 30 decades. A summary of earlier research on remote sensing and GIS based biomass estimation approaches and a discussion of existing issues prompting biomass estimation are very valuable for further improving biomass estimation performance with the integration of geographical information systems. This paper discusses the recently most accurately alternative research methods to integration of remote sensing and GIS applications for identification, characterization and analysis of biophysical characters. The remote sensed data and geographical information systems using the accurate assessment of natural resource and management for the enhancement of biomass production and estimation. There is not any other option for the perfectly biophysical and natural resources estimation in small to large area. The earlier research has demonstrated that biomass estimation remains a challenging task, especially in those study areas with complex forest and mixed traditional cropping systems and environmental conditions. Either optical sensor data or radar data are more suitable for forest sites with relatively simple forest stand structure than the sites with complex biophysical environments. A combination of spectral responses and image textures improves biomass estimation performance. More research expertise is needed to focus on the integration of remote sensing, GIS& GPS, the use of multisource data, and the selection of suitable variables and algorithms for biomass estimation at different scales. Understanding and identifying major uncertainties caused by different stages of the biomass estimation technique and applying strength of mind to reduce these uncertainties are critical.

Keywords : Integrating, Remote Sensing, Bio-physical, Resources, Environment

Analysis of Covariance (ANCOVA) combines features of analysis of variance and regression analysis to account for the influence of a concomitant or covariate variable. In forestry and horticultural experiments, initial measurements or other associated characteristics may influence the response variable and contribute to experimental variation. This chapter explains how treatment effects can be evaluated after adjusting for the linear relationship with a covariate. It presents the ANCOVA framework, calculation of sums of squares, regression coefficients, adjusted error and treatment variation, F-tests, and adjusted treatment means. Examples involving seedling growth and physiological responses demonstrate how covariance adjustment can provide more precise comparisons among treatments.

Analysis of Variance (ANOVA) is an important statistical technique for separating variation attributable to different sources in an experiment. The chapter explains how total variation can be partitioned into controlled and uncontrolled components and how experimental error is used to assess treatment differences. The F-test provides the basis for testing the homogeneity of several treatment means simultaneously. The chapter discusses the assumptions underlying ANOVA, including independence, normality, additivity, and homogeneity of variance. It also introduces fixed- and random-effects models and explains their relevance to experimental analysis. Forestry and agricultural examples demonstrate the application of ANOVA for interpreting treatment and experimental effects.

Key words : Integrated Watershed Management (IWM), GIS, DTM, Watershed Delination, Water Quality Modeling, RS, DSS, Ground Water Information System (GWIS).

Introduction

The availability of land and water resources has a major effect on the economic and ecological aspects of life everywhere around the world. As a result, the need to develop better tools for efficient land and water resources management has become very vital. For quicker, reliable and precise results, it is necessary to use the geospatial tools and techniques such as Global Positioning Systems (GPS), Geographic Information Systems (GIS) and Remote Sensing (RS) image interpretation and analysis techniques to manage the natural resources. The application of these tools and techniques not only facilitates timely and repetitive analysis of all the topological features, but also assists in development of Decision Support Systems (DSS) to generate alternative scenarios for optimal use of the natural resources in the long term. The optimal use of these resources through integrated management of land water and vegetation leads to enhanced production and productivity of food, fodder, fiber, fuel, fish, flower and forest resources. Moreover, the holistic management of the resources to produce these outputs is plausible and manageable within the watershed systems, which are hydro geological units from which the runoff resulting from precipitation flows past through a single outlet to downstream locations. The systematic management of land, water, vegetation and livestock resources within the watershed systems is termed as Integrated Watershed Management (IWM). Broadly, IWM can be defined as a multidisciplinary, holistic way of protecting and managing a watershed’s natural resources to enhance biomass production in an eco-friendly manner. The watershed is viewed as a hydro-geological complex and dynamic ecosystem in which natural and anthropogenic processes occur and interact resulting in generation of runoff at the watershed outlet.

Abstract

This study was conducted at Directorate of Cashew Research (DCR) Puttur, D.K., Karnataka during 2007-12. In this study, a methodology was standardized to find the area under cashew, barren areas and other areas for expanding cashew cultivation with remote sensing. It was found that the images pertaining to the month of November was best for classifying cashew since the colour of cashew leaves changes during that period. Also, the LISS III images were found to be good for identifying cashew. It was not possible to differentiate between cashew seedling area and cashew graft area using LISS III or LISS IV images. A spatial database of soil moisture was prepared for the irrigation scheduling in cashew and a spatial database of major nutrients was prepared for the site specific nutrient management in cashew gardens. A spatial database of land slope and stream orders was prepared using GIS and suitable soil and water conservation interventions were suggested for the selected area.

Keywords: Cashew, GIS, remote sensing

Abstract

Soil Resource Appraisal helps in characterizing the existing land resources, assessing their suitability for various land uses and then helping the user to prepare a rational land use plan suitable for the integrated development of the area. Application of Remote Sensing (RS) and Geographical Information System (GIS) helps not only in the characterization of the resource base, but also in the generation of various thematic maps and preparation of land use and action plans for effective implementation of various developmental plans at farm level.

Keywords : Soil Resource Appraisal, Land Use Plan, Farm level Developmental Plans

1.0 Introduction

A thorough knowledge and understanding of soils is basic to the development and management of agricultural resources. About 100 years ago, man began systematic study of soils in the field and tried to separate, classify and map their geographic distri­bution. Today we have sufficient knowledge about soils; however the characterisation, classification and mapping of soils is still tedious, time consuming and costly affair. Therefore the need for adopting new and more rapid and accurate methods of mapping soils has resulted in the use of satellite images with image interpretation techniques for soil survey and map­ping.

In India for the country as a whole, soil and other land resources information is available at 1:1 M scale, which was generated from the state level mapping done at 1:250,000 scale under the Soil Resources Mapping Project of India during the period from 1986 to 1995. This information is of general nature and useful for planning on a wide canvas and not particularly useful for farm or watershed level planning in the country. For site-specific needs and for developmental works at watershed level, we need detailed farm level database at 1:10,000 scale. Once this type of information is generated at village/watershed level, this will be useful for identifying site specific constraints, generation of area and site specific agro technologies, land-use options based on soil site suitability assessment, wasteland identification, identification of prime farm lands and farm clusters for zoning and Strategic planning, preparation of farm level development plans by line departments and proactive advice and technology transfer to farmers.

2.1 INTRODUCTION

GIS is a tool that can be used in varied disciplines ranging from civil engineering to business. Remote sensing data is often brought into GIS to have a complete data set in many natural resource application areas. Full capability of GIS is not used widely. Many a times people use GIS only for digitising maps and to do simple query on maps. But it can also be used in modeling, simulation and forecasting, only then one may regard GIS technology is utilised to its full potential. Some GIS softwares handle simple problems for location based services while other complex softwares posses advanced modules to do all types of spatial, network and 3D analysis. Integration of softwares used in natural resources study are being interfaced with GIS to create scenarios.

Remote sensing and GIS technology is useful in the following fields:

Water is a primary source of life and sustains all human activities such as domestic needs, agriculture, industries etc. The allocation and management of water resources is becoming a difficult task due to increasing demands, decreasing supply and diminishing quality. This calls for judicious use of water resources. The geomorphology describes the environment in which the hydrological processes operate. A strong mutual correlation exists between geomorphological variables and hydrological characteristics. Such relationship can be applied to both surface and groundwater regime. Thus the linking of geomorphological parameters with hydrological characteristics of the basin provides a simple way to understand the hydrogeomorphological behaviour of different basins and particularly of un-gauged basins.

During the last few years various applications of hydrogeomorphology with reference to the Geographic Information System (GIS) and various other techniques have emerged as an extensively effective tool for analyzing and prioritizing natural resource management alternatives. Because natural resource management problems are spatial in nature the GIS technology provides a tool for defining the extent of the problem, and facilitates the design and implementation of alternative management strategies. The GIS is used to estimate the hydrogeomorphological parameters of the watershed, which can be used in watershed planning and management at micro and macro levels. The results of the study can be utilized to infer the hydrological features like runoff potential, infiltration and groundwater recharge of the region. Some of the applications of hydrogeomorphological studies are described below:

Abstract

Remote sensing is the art and science of obtaining information about an object or phenomenon without having any contact with the object concerned. Geographical information system is a tool to create, manage, retrieve and query spatial databases. Remote sensing in conjunction with Geographical Information System (GIS) has wide applications in all spheres of life and especially in agriculture. Specifically, this paper reveals some of the applications of remote sensing and GIS in the field of crop condition assessment viz., crop type classification, crop stress detection, crop yield estimation and pest and disease monitoring.

Keywords : Remote Sensing, GIS, Yield Forecasting, Crop Stress, Pest and Disease Monitoring

1.0 Introduction

Remote sensing is the art and science of obtaining information about an object or phenomenon without there being any physical contact. The information is so collected using a device called a sensor. The sensors are mounted on a platform at an advantageous position. Accordingly the remote sensing is called ground based (sensor is hand held or mounted on mast), air borne (sensor is on a balloon or aero plane) or space borne (sensor is mounted on a satellite). The basic principle in remote sensing is that each and every object has unique reflecting property like human thumb impression and when the solar energy is falling on an object a part of it gets reflected, which carries the information about the object. The reflected light is converted in to reflection percentage of incoming solar radiation. This percentage of reflection can be used to work out the vegetation indices which are surrogate measure of crop condition.

Geographical Information System (GIS) is software system which enable Collection of large volume of data with spatial relation Integration of data from different source is possible Analyze such data for our requirement Present the data in different formats (maps, table and reports)

Applications of Remote Sensing

The era of satellite remote sensing was open in the 1960s when cameras and electronic sensors were mounted on spacecraft. Nowadays there is a big assortment of satellite systems actively recording information about the Earth. A wide variety of imagery is available from satellites. Both active and passive sensors, operating from the microwave to the ultraviolet regions of the electromagnetic spectrum collect a large amount of information about the earth’s surface every day. Each of the systems vary in terms of their spatial, spectral, radiometic and temporal resolution. Those characteristics play an important role in defining which applications the sensor is best suited for. The main benefits of satellite remote sensing are the following:

Abstract

Any country’s socio-economic development essentially depends on land and water resources. Resource depletion takes place when there is an overstretching due the exploding population, calling for prudent management of the resources. Application of remote sensing and Geographical Information System (GIS) tools in the management of natural resources at different levels including watersheds is quite well known. In addition, applied geoinformatics are also used in research on management of these delicate resources also besides characterization. There is a continuous advent in technology in both hardware and software necessitating updation of application scientist/manager. Once an exclusive tool of elite institutions and government agencies, GISnow became an indispensible part of our day-to-day life. As the urge for information intensifies, new applications of GIS continue to evolve. A concerted effort is needed to develop an information system when vast expanses of natural resources are to be managed and a combination of information tools like remote sensing, global positioning system and GIS always updates the database effectively. However, the cost factor should not hinder the applications at micro level. This necessity calls for attention to popularize free and open sources of software (FOSS) and data (particularly digital elevation models (DEM) and satellite imagery) to put the techniques into use by enthusiasts at village level such that a database at a regional level can be built through participatory GIS. It is in this direc tion the av ailab le li terat ure on appl ied geoinformatics coupled with use of FOSS is reviewed and presented in this article to give an idea as to how an open ended information system could be developed to help the management.

Keywords: Natural resources, management, Remote sensing, GIS, FOSS, DEM

An aquifer is a geologic formation, a part of a formation, or a group of formations that will yield water. The quantity of ground water that an aquifer will yield to wells depends partly on its thickness, extent, continuity, and homogeneity, and partly on its physical properties of permeability and porosity.

Groundwater is stored in subsurface void spaces below the water table. The geologic material that stores, transmits, and yields groundwater to wells and springs are called aquifers. The key word here is yields. To be an aquifer, it must store and transmit water at rates fast enough to supply reasonable amounts to wells and springs. Therefore, not all groundwater is stored in an aquifer. Productive aquifers can be comprised of sand and gravel, sandstone, limestone and dolomite, or basalt flows, whereas fractured igneous or metamorphic rocks are only marginal aquifers.

The classifications of aquifer are given below:

1. Introduction
Studies on soil health and agricultural growth are increasingly using artificial intelligence (AI) and machine learning (ML) techniques. With the help of these technologies, it is possible to analyse vast amounts of data and gain insightful knowledge that will help to boost crop yields and optimise agricultural practises (Neethirajan, 2020). Applications of AI and ML to research crop development and soil health have enormous potential to revolutionise agriculture. In order to increase soil health and crop yield, farmers and researchers can use these technologies to analyse massive datasets, generate precise forecasts, and optimise farming practises. Overall, artificial intelligence (AI) and machine learning (ML) provide useful tools that can support efficient and sustainable agriculture, solving global concerns in food availability and the sustainability of the environment. Unquestionably, technical developments have significantly increased the productivity and efficiency of many industries, including agriculture. The advent of words like “big data,” “data analytics,” “artificial intelligence,” “Internet of Things,” “erosion modelling,” “smart farming,” and “machine learning” are just a few examples of this technological revolution (Almoussawi et al., 2022). Digital soil mapping (DSM) uses computational models to infer regional and temporal shifts of soil types and properties based on soil observations, prior knowledge, and relevant environmental variables in order to create and populate spatial soil information systems. The supply chain for agricultural production is extremely intricate (Lamichhane et al., 2019). The way our food is produced, distributed, and consumed is changing as a result of AI. When planning crop rotations, planting times, water and nutrient management, pest and disease control, optimal harvesting, food marketing, product distribution, food safety, and other agriculture-related tasks throughout every step of the food supply chain, researchers use powered by AI tools to supply advice and expertise. In “Harnessing AI to transform agriculture and inform agricultural research,” Peters et al. (2020) presented a summary of the most recent developments, difficulties, and prospects for AI technology in agriculture. They use the four main elements of the food system—production, distribution, consumption, and uncertainty—to show the possibilities of AI. They come to the conclusion that agricultural businesses are excellent candidates for using AI and other technologies. In “AI down on the farm,” Sudduth et al. (2020), examined a number of case studies in which machine learning (ML) has been used to model various aspects of agricultural production systems and give data that can be utilised to make management decisions at the farm level. These research efforts involve providing data, essential for creating precise and effective irrigation systems and improving tools for suggesting the best nitrogen fertilisation rates for maize. Traditional crop health monitoring techniques need a lot of work and time. Using AI to monitor and detect potential crop problems or nutrient deficits in the soil is an effective method. Applications to analyse plant health trends in agriculture are being created with the aid of deep learning. These AI-powered tools are essential for improving our understanding of soil quality, crop pests, and disease in plants (Virnodkar et al., 2020).

Abstract

Higher temperature and altered rainfall patterns accompanied by extreme weather events are being considered indications of climate change. These factors impact vegetation growth in natural forest, open scrub and in agriculture land. Vegetation index based on satellite data was used to analyse agricultural vulnerability in India to understand impact of rainfall variability. Time-series Normalized Difference Vegetation Index (NDVI) data products obtained from AVHRR (1982 to 2006) and MODIS (2000-2011) were analyzed to assess spatial and temporal variability. Coefficient of variation of maximum NDVI was assessed for the study period and results were corroborated with Standard Precipitation Index (SPI) instead of actual rainfall. Resolution of AVHRR time-series data facilitated study at regional-scale while MODIS datasets with 250m pixel resolution helped in identifying vulnerability at district-level. Study indicated that over 241 Mha areas in the country may not be vulnerable to rainfall variability induced climate change while over 81.3 Mha in arid, semi-arid and dry sub- humid regions may be vulnerable. Study indicated that over 12.1 and 1.81 Mha of Kharif cropland would be mildly and severally vulnerable while 6.86 and 0.5 Mha of Rabi cropland may be adversely affected in a similar manner. Study indicated that length of cropping season was decreasing due to delay in start of season. The paper also presents analysis of variations in SPI and resultant NDVI in typical drought and flood years in the country.

Keywords: MODIS, AVHRR, NDVI, Rainf ed Agricul ture, Vulnerability, Climate Change

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.

Abstract

Land development in urbanized watersheds poses a potential of increasing storm runoff rates, and therefore increasing the risk of flooding in the downstream areas of a watershed. To investigate the impact of change in land use practices on the watershed hydrology, an hydrological analysis was conducted on Little Kitten Creek watershed located near Manhattan city of Riley county, Kansas, USA. Prior to development, land use types in the watershed included woodland, pasture, and fallow land. However, rapid development has occurred in the watershed in the last ten years. Post development land use practices include commercial development, residential housing, and conservation (woodland and grasslands). Data collected and analyzed included digital elevation model (DEM) of study site, stream network and soil data using ArcGIS (ESRI, 2006) and Arc-Hydro Software (Maidment, 2000). Watershed characteristics such as slopes, channel length, circularity ratios, and drainage density were also computed. The delineated watershed boundary, stream segments and soil data layers were over laid to produce soil maps. Time of concentration (Tc) was determined for the pre and post development conditions in the watershed using seven different methods. The time of concentration values computed using Kerby, Federal Aviation Agency, Kinematic Wave and NRCS (SCS) methods found more reasonable compared to the remaining three methods used.

These computed time of concentration values were observed to be shorter for the post-development condition when compared to the pre-development condition of the watershed. The reduced time of concentration in the post development condition is attributed to increase in percentage of impervious areas due to increased residential development in the watershed; therefore increased rates of runoff in the watershed are expected.

Keywords: Rezoning, Time of Concentration, DEM, Kinematic Wave, Drainage density, Circularity ratio.

1. Introduction
Soil hydrothermal regime refers to the distribution and movement of water and heat within the soil. It describes the patterns of soil moisture and temperature variation over time and space. Understanding the hydrothermal regime is important in agriculture, ecology, and engineering, as it affects various soil processes, plant growth, and the behaviour of soil-related structures. The hydrothermal regime of soil is influenced by several factors, including climate, topography, vegetation, soil properties, and land use practices. The movement of water within the soil is driven by precipitation, irrigation, evaporation, and plant uptake (Aggarwal et al., 2009). It can be affected by soil physical properties such as soil texture, structure, porosity, and permeability. Water moves through the soil in response to hydraulic gradients, which are determined by the difference in soil water potential. Soil temperature is influenced by solar radiation, air temperature, and heat fluxes at the soil surface. It varies with depth and can be affected by factors such as soil color, moisture content, vegetation cover, and the thermal properties of the soil. The hydrothermal regime of soil has significant implications for plant growth and development. It affects processes such as seed germination, root growth, nutrient availability, and microbial activity. It also plays a crucial role in soil erosion, groundwater recharge, and the movement of pollutants within the soil profile. Soil’s thermal properties are pivotal in determining the microclimate, which subsequently influences critical stages like seed germination, the growth of seedlings, and the successful established of a crop stand. These properties are vital for modelling the movement of energy, water, and nutrients in soil. Soil thermal regimes are shaped by static properties such as texture and mineralogy and dynamic properties including water content, compaction, organic matter, and porosity. These dynamic properties including water content, compaction, organic matter, and porosity. These dynamic properties, significant to significant change during various farming operations can be adjusted with suitable soil and crop management strategies. Soil moisture content has a significant effect on the soil thermal conductivity. The relationship between soil moisture and thermal conductivity

Abstract

This paper is the result of strong belief that if earth observation and geospatial techniques could be applied to a village to plan its natural resources in sustainable manner, the potential of this technique for common man is harnessed maximum. So analysis of one village natural environment, predicting its impact on humans, identifying certain possible inhabitable conditions due to existing nature & suggesting ways to utilize these conditions for sustainable human's and nature's existence  is the motive behind this work.

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.

Augmented designs are useful when a large number of standard varieties or treatments are available for replication but new or promising materials are available only in limited quantities. This chapter introduces augmented designs as modifications of standard experimental designs in which additional, usually unreplicated, entries are incorporated alongside replicated standard treatments. Applications include testing new varieties, seedlings, medicinal plants, growth regulators, seedling carriers, and progeny under forestry and agricultural conditions. The chapter explains the principles of augmented designs and their applications in experimental research. It also discusses augmented Completely Randomized Design, including its layout, analysis of variance, and comparison of replicated varieties and unreplicated seedlings.

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.

Soil sampling is an integral and essential component of soil fertility evaluation and nutrient management research. The effectiveness of soil sampling is a prerequisite for soil testing to achieve its goals for efficient nutrient management and plant nutrition for improved yield and quality. The underlying basis for soil sampling is that a soil sample taken represents the “population” which may be a plot, field or a watershed. It further implies that nutrient status of the representative soil sample(s) determined in a laboratory would reflect nutrient status of a plot, field or watershed and is of interest for correcting nutrient disorders in the field or watershed.

The most important factor that influences the effectiveness of soil sampling is soil heterogeneity. However, in a relatively homogenous group of fields or plots, a small number of samples may be sufficient to represent the population to a more heterogenous group of fields that would require more number of samples to represent the soil population. Whenever, soil samples to be taken in field following points needs to be kept in mind.

Biodiversity provides the building blocks for sustainable food, health and livelihood security systems. It is the feedstock for both the biotechnology industry and a climate-resilient farming system. Because of its importance for human well-being and survival, a Convention on Biological Diversity (CBD) was adopted at the UN Conference on Environment and Development held at Rio de Janeiro in 1992. The Convention’s triple goals are conservation, sustainable use, and equitable sharing of benefits. CBD defines biological diversity as follows:

Variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems.

Demographic explosion, environment pollution, habitat destruction, enlarging ecological footprint, widespread hunger and unsustainable lifestyles, and potential adverse changes in climate all threaten the future of human food, water, health and livelihood security systems. 2010 appears to mark the beginning of uncertain weather patterns and extreme climate behaviour. Events like temperature rise, drought, flood, coastal storms and rise in sea level are likely to present new challenges to the public, professionals and policy makers. Biodiversity has so far served as the feedstock for sustainable food and health security and can play a similar role in the development of climate-resilient farming and livelihood systems. Biodiversity is also the feedstock for the biotechnology industry. Unfortunately, genetic erosion and species extinction are now occurring at an accelerated pace due to habitat destruction, alien species invasion and spread of agricultural systems characterised by genetic homogeneity. Genetic homogeneity increases genetic vulnerability to biotic and abiotic stresses. To generate widespread interest in biodiversity conservation, the UN General Assembly has declared 2010 as the International Year of Biodiversity.

The country is at long last becoming sensitive to the serious consequences of the growing rural-urban divide in terms of investment, infrastructure and opportunities for income and employment. The rural-urban divide also leads to an expanding rich-poor divide. Since crop and animal husbandry, fisheries, forestry, and agro-processing are the main sources of rural livelihoods, the current agrarian crisis is adding to the problems of hunger, poverty and unemployment. According to the Union Planning Commission, we are off-track in achieving most of the UN Millennium Development Goals. A major cause for the growing rich-poor divide both between and within nations is unequal access to modern technology. Technology helps to achieve a paradigm shift from unskilled to skilled work and thereby move large numbers of the rural poor from the primary to the secondary and tertiary sectors of economic activity. If technology has been a major factor in promoting economic and social divides in the past, the challenge now lies in enlisting technology as an ally in the movement for economic, gender and social equity.

How can we develop a sustainable water security system in an era of climate change and global warming ?  In my view, every country should develop a sustainable water security system with the following four major components.

Supply augmentation

The major sources of irrigation water are from rainfall, rivers, tanks, reservoirs and other surface water resources, groundwater, industrial and domestic effluents, and seawater.  Seawater comprises almost 97 per cent of the global water resource and is an important social asset.  With the melting of Arctic and Antarctic ice, sea level will go up with disastrous consequences. The tsunami of December 2004 gave a glimpse of what could happen in the future.  To augment supplies, we must harvest rainwater and store it carefully, both above and below ground.  Also, we should ensure that all waste water which emerges from industry and domestic use is purified and recycled.  Rainwater harvesting should become a way of life, so that it becomes everybody’s business.

Introduction 
Having explored the fundamental principles and various types of elementary crop growth models in the previous chapter, we now arrive at a pivotal juncture in their journey from theoretical constructs to practical, dependable tools. It‘s one thing to conceptualize and build a mathematical representation of crop growth; it‘s quite another to ensure that this model accurately reflects realworld plant behavior and can be reliably used for predicting outcomes and informing critical agricultural decisions. This chapter, Calibration, Validation, Verification, and Sensitivity of Elementary Crop Models,” will guide you through the indispensable processes that establish a model‘s credibility and utility. We will delve into the systematic steps involved in calibration, where we meticulously fine-tune a model‘s parameters to accurately reflect a specific crop‘s response within a particular environment, effectively adapting the general model to local conditions. Following this, we will explore validation, a rigorous process of independently testing the model‘s predictive power against datasets not used during calibration, ensuring its robustness and generalizability. Beyond accuracy, we will also cover verification, which confirms the model‘s logical consistency and computational correctness, ensuring that its internal workings are sound. Finally, we will examine sensitivity analysis, a crucial technique for understanding how changes in various input factors—especially weather parameters—or internal model coefficients influence the model‘s outputs, thereby revealing the model‘s critical drivers and potential vulnerabilities. These four processes are far more than mere technical formalities; they form the bedrock upon which the utility, accuracy, and trustworthiness of any crop model, no matter how elementary, are firmly built. By engaging with these essential steps, we can transform theoretical frameworks into powerful instruments capable of supporting sustainable agricultural planning, risk management, and adaptive strategies in a dynamic climate.

Abstract

Traditionally remote sensing technology has been widely used in the mapping and change detection of vegetation features. Of late research has shown that remote sensing technology can be successfully employed for retrieval of biophysical parameters of vegetation, either by statistical techniques or by employing radiative transfer models (RTMs). This paper gives an introduction to leaf and canopy RTMs, especially about PROSAIL, which is the most commonly used model. It reviews different techniques for inverting RTM models, their merits and performance comparison. The main techniques briefly described are iterative optimization, look-up table, artificial neural network, genetic algorithm and support vector machine. At the end, it gives examples of applications which use canopy biophysical parameters retrieved by inverting RTM.

Keywords: Radiative transfer, PROSAIL, Inversion, Vegetation

1.0 Introduction

Accurate quantitative estimation of vegetation biophysical variables is useful for a large variety of agricultural, ecological, and meteorological applications (Houborg et al., 2007). The spatial and temporal distribution of these variables are important inputs into models quantifying the exchange of energy and matter between the land surface and the atmosphere, developing better yield prediction models and detecting abiotic stresses at regional scales. Among the many vegetation characteristics leaf area index, leaf chlorophyll content and leaf water content are of prime importance (Bacour et al., 2006; Houborg et al., 2007).

Abstract

The drainage pattern and the architecture are the reflections of not only the tectono fabric of a region, but also the active tectonic processes. From amongst many drainage patterns and anomalies, the eyed drainages, deflected drainages and the compressed meanders interpreted from satellite images for Pudukkottai region of Southern Tamil Nadu infer to reflect the active N-S block faults and NE-SW sinistral and NW-SE dextral faults related to post collision tectonic phenomena.

Introduction

The Geoscientists have long back recognized that the drainages are the reflections of the surface and subsurface lithology, structure and tectano fabric and also the active tectonic movements especially of the Quaternary period (Thornbury, 1985). Hence, the Geoscientists have all along been showing greater interest in mapping the drainage patterns. However, the mapping of the drainage patterns and detection of tectonic features there from have gained greater momentum only after the advent of modern Remote Sensing technology. Since the airborne and satellite borne spectral images have the credentials of showing the drainage pattern, the drainage anomalies and the tectonic features related to them all of which were studied in conjunction to bring out information on the geological history of the Quaternary period by many workers (Vaidyanadhan, 1971; Murthy and Sastri, 1981; Agarwal and Mitra, 1991; Ramasamy, 1991; Saravanavel and Ramasamy, 2014).

Abstract

The strenuous field trekking, inaccessibility, perception of geology only within the visibility range of the eyes etc. have remained matters of major constraints in the past in field based geological mapping. The remote sensing technology not only eradicated these but also provided lot many newer vistas in the field of geology. In addition, the application of such unique technology has brought out a hierarchy of newer concepts in the field of geology. The paper narrates the newer knowledge gathered on the geology of South India in the post remote sensing Era.

Abstract

As the available surface water is too inadequate to fulfill the man’s requirements he was forced to locate newer groundwater reservoirs on one side and to control the fast depleting water table on the other side. As the ancient techniques have failed to give precise models on groundwater targeting, he was to invent and employ newer technologies. Under this scenario the recently invented spatial technology has emerged as a very powerful tool in groundwater targeting and management. The vistas and virtues of such spatial technology in groundwater management have been discussed in this overview paper by citing some case studies from Tamil Nadu, India.

Abstract

The coastal zones are very fragile owing to the reason that these form the junction between the continents and the oceans. Further the multivariate tectonic, fluvial, marine and aeolian processes, which vary independently in their degrees and duration in space and time, act in different permutations and combinations with each other and keep on constructing and destroying the landforms along the coasts. Again, as coastal zones are heavily populated; it adds further pressure on the coastal systems. The paper deals about the hierarchy of issues faced by the coastal zones like tectonic emergence and subsidence and it‘s impacts and the ways, means and needs for measuring the rate of such movements; marine regression and the need for the time series models on the land-ocean boundaries for drawing the sea level curve for India, the Quaternary deltas and the essentiality for understanding the mixture of fluvial and marine forces for the possible future forecasting models; back water modifications, the related issues and the demands for the sediment-chemo dynamics modelling; disaster vulnerabilities and mapping methodologies etc; all using the unexplored vistas of Geomatics technology. The concept has been explained by taking Tamilnadu coast as the reference region.

Introduction

The coastal zones are always fragile as these are formed by the tectonic, fluvial, marine and aeolian processes, which act independently in varying degrees and duration and occur cumulatively in different permutations and combinations along the different parts of the coasts. In addition to such inherent fragility, the coastal zones stand vulnerably exposed to various natural and anthropogenic disasters like seismicity and earth movements, tsunamis, floods, sea level rise, cyclones, coastal erosion etc. Further, owing to the enriched land and water resources, enjoyable eco systems and avenues for intra and intercontinental trades, more than 70% of the global population and the related infrastructure are clustered in the coastal zones. Hence the scientists from all over the world have started showing more attention towards the coastal regions (Ahmed, 1972). The advent of Geomatics technology comprising multi spectral and hyper spectral remote sensing, GIS, GPS, Interferometry, etc. have opened up exhaustive newer vistas in unfurling the hidden complexities of the coastal zones and to provide solutions. From amongst many, the remote sensing based studies carried out by Shailesh Nayak and his team (2004) have brought out many first time information to light about the Indian coasts. But still we will have to go a long way, as the global coasts in general and the Indian coasts in particular are confronted with a hierarchy of challenges and the Geomatics technology can provide answers for these challenging issues owing to its unique virtues. The paper narrates the unique issues of the Indian coasts and how effectively the geomatics technology can be used in solving them. This has been done by taking Tamil Nadu coast as the test area.

The principle of common but differentiated responsibilities is the core of the many climate agreements arrived at so far, including the Kyoto Protocol (1997) and the Bali Plan of Action (2007).  The differentiated responsibilities aim to meet the special needs of developing countries for accelerated and equitable economic development.  Both at Copenhagen and Cancun, the industrialised countries proposed limiting the rise in mean temperature to 2ºC above normal.  Even this seems to be unattainable in the context of the present rate of emission of greenhouse gases (GHG).  Hence, the principle of common but differentiated impact of the 2º change in mean temperature is essential for prioritising climate victims. For example, small islands like Tuvalu in the Pacific Ocean, Maldives, Lakshadweep, and Andaman and Nicobar, as well as the Sunderbans in West Bengal, Kuttanad in Kerala and many other coastal locations will all face the prospect of submergence.  Floods will become more serious and frequent in the Indo-Gangetic plains.  Drought-induced food and water scarcity will become more acute. South Asia, sub-Saharan Africa and the small islands will be the worst victims.  In contrast, countries in the northern latitudes will benefit due to longer growing seasons and higher yields.

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.

Coordinate Reference System (CRS) is a framework of coordinate based local, regional or global reference system used to represent the locations of geographic features. Coordinate Reference Systems (CRS) provide a standardized way to define the real-world geographic locations which further enable us to use common locations of geographic datasets for integration. The datasets from different sources and CRS are transformed to a common CRS in order to perform various integrated analytical operations like overlaying of data layers. Coordinate Reference Systems are mainly of two types:

1. Geographic Coordinate System: This is global or spherical coordinate system in which locations of features on earth are defined in terms of latitude and longitude values. Latitude and longitude are angles based on a point at the centre of the earth. In this system, locations are defined by degrees, therefore, distances cannot be measured accurately. The most common geographic coordinate system is the World Geodetic System 84 (WGS84).
2. Projected Coordinate System: In this system, locations of features are defined using cartesian x, y coordinates on a flat, two – dimensional surface. This system is based on sphere or ellipsoid projected on flat surface. Projected Coordinate System are generally referred as projec- tion. It is required for preparing maps on paper or computer screen. Un- like Geographic Coordinate System, projected coordinate system has ac- curate measurements of distance, angles and areas across the two dimen- sions. However, there is alwaystrade-offs between area, direction, shape, and distance in this system due to distortions created while representing ellipsoid or spheroid on a flat map. So, the projection type is selected based on its application. Most common projections are Universal Trans- verse Mercator (UTM), Lambert Conformal Conic and Albers Equal Area.