eChapters

Abstract

The groundwater behaviour in hard rock aquifer systems has always remained enigmatic owing to the heterogeneity of the aquifer systems. This is the reason why there are a lot of failures in groundwater targeting and management in hard rock aquifer systems even after the employment of many modern technologies. For precise groundwater targeting in hard rock aquifer systems various parameters like lithology, the type and the degree of structural deformation, the degree of weathering, geomorphology, etc., will have to be analysed which involves spatial analysis of voluminous data. In the sphere of spatial modelling, GIS plays an important role. Hence, a newer study has been carried out in parts of Madurai, Dindigul and Theni districts, Tamil Nadu, to identify the groundwater provinces and their functions using GIS. In the said study, GIS images were generated on the groundwater targets through GIS integration of transmissivity, permeability, storage co-efficient and water level. Such GIS image showing groundwater maxima was overlaid with GIS image of fracture maxima, subsurface geology maxima and the geomorphology maxima and thus by various GIS based spatial manipulations, aquifers controlled by structure, geomorphology, subsurface geology, and combinations of the above various variables were brought out. This has provided potential method for precise targeting of groundwater.

Key words : Remote Sensing, Ecosystem, Environmental Pollution, Biodiversity Conservation, Coastal Environmental Degradation, Wetland.

Introduction

National prosperity and human resources development are primarily shaped by the availability of natural resources and their use. None the less, the benefits are quite often negated by the resultant environmental degradation concomitant with their utilisation for economic values. The problems of environmental degradation have assumed critical dimensions in many parts of India due to resources exploitation; industrialisation and urbanisation, unchecked population growth and a never ending quest for energy to meet the increasing demands. Moreover, improper utilisation of resources has resulted in such environmental problems as soil erosion, waterlogging, floods, droughts and pollution of various types besides socio-economic disparity among people.

After the 1972 Stockholm conference on Human Environment and Development, there has been a phenomenal increase in the worldwide interest in the environmental issues associated with development. Consequently, the focus of development has shifted to environment friendly and sustainable developmental planning in recent years. The Rio Summit (June 3-14, 1992) further emphasised the fact that earth is an integral and interdependent system, which require sustainable developmental planning. During deliberations of the Rio Summit (Earth Summit), it was emphasised that satellite imagery will be quite helpful in monitoring earth’s environment.

India has a long tradition of environmental protection and has enacted laws during the last three decades to give further impetus to the conservation of natural resources and protection of environment. Some of the important laws are:

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

Spatial data is different from a non-spatial data in the sense that it always has a spatial reference system which is nothing but a pair of coordinate values and a system of reference for these coordinates. For example, the locations of benchmark soil series of India can be defined as pairs of longitude and latitude in decimal degree values with respect to the prime meridian at Greenwich and zero latitude at the equator. The World Geodetic System (WGS84) is a frequently used system of reference on the Earth. The different types of spatial data are: (i) point, which is a single point location, such as a GPS reading or a geocoded address (ii) line, which is defined as a set of ordered points, connected by straight line segments (iii) polygon, an area, marked by one or more enclosing lines, possibly containing holes and (iv) grid, a collection of points or rectangular cells, organised in a regular lattice. Among the above said spatial data, the first three are vector data models and represent entities as exactly as possible, while the fourth data model is a raster data model, representing continuous surfaces by using a regular tessellation. In this chapter, we discuss how a spatial data can be handled in R. There are several packages available in R in which spatial data analysis can be handled e.g. sp, rgdal, raster etc. All the contributed packages for spatial data analysis in R address two broad areas: moving spatial data into and out of R, and analysing spatial data in R. In the following section, the sp package for spatial data handling is briefly discussed.

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

Introduction

The conventional approach of the human person for plant classification is comparing the color and shape of the leaves, but by using Artificial Intelligence analysis can provide quicker and more detailed results by analyzing the morphology of the leaves and provides more details about the leaf properties. Species Recognition has been a very important part of agriculture and there are thousands of species in plants which can be identified by a single person and even if it does so It will take a huge lot of time to do it.

In this chapter we will discuss and built a project related to identify species in flower with an image. In this project we will be using Artificial Intelligence different branches like machine learning, deep learning and computer vision to build such an amazing project. So, let’s begin

4.1. Data Set Generation for Modelling
We have generated 54 transfomed spectra by applying different combination of parameter. Now this spectrum is to be tagged with respective soil sample and desired soil properties for which spectral modeling has to be done. Here we are generaring data tagged with CaCO3.
For that we have to use cbind() fuction. Here we are keeping soil properties in first column and spectra after that column. We are storing data in R memeory and naming data in such way that we can identify properties and transfoemed spectra attached with that data. After generating data for modeling, data is divided into calibration and validation data set by using random sampling method or Kedard-
Stone method
# Data set for CaCO3 with raw spectra ####
Data.CaCO3.pc.113=cbind(data10nm_$CaCO3_equivalent_per, First_ derivative10nm113) #case 1st
Here in case 1st, CaCO3 indicating properties and 113 indcating without smoothed 1st derivative, 1st order polynomial 3rd point spectra is tagged with these soil samples. Same was followed with other cases also

For applying spectral data models with other machine learning and regression algorithms we have to follow all steps upto Chapter 4 till the partition of whole datainto calibration and validation data set. After that different algorithms is used to develop others models so that,the best model among the different algorithms can be identified and used for better perdiction of soil properties.
5.1 Principal Component Regression (PCR)
Principal Component Regression (PCR) is a regression technique that combines Principal Component Analysis (PCA) and multiple linear regression. It is particularly useful when dealing with multicollinearity in the predictor variables, as it transforms the predictors into a set of uncorrelated components before performing the regression.
set.seed(123)
pcr.CaCO3.pc.223SGS<- train(CaCO3.pc ~., Train_Data.CaCO3.pc.223SGS,
method =”pcr”, trControl = custom,
tuneLength=25, importance = TRUE)
Explaination for code train(CaCO3.pc ~ ., data = Train_Data.CaCO3.pc.223SGS, ...)
Formula (CaCO3.pc ~ .): Specifies the model formula where CaCO3.pc is the
dependent variable and . indicates that all other columns in Train_Data.CaCO3.
pc.223SGS are independent variables.
Data (data = Train_Data.CaCO3.pc.223SGS): The dataset used for training the model.
method = “pcr”:Specifies that the model to be used is Principal Component Regression (PCR).
trControl = custom:Specifies the training control method. custom should be an object of class trainControl created earlier, defining the resampling method (e.g., cross-validation).This example sets up 10-fold cross-validation. tuneLength = 25:Specifies the number of different values for the number of principal components to consider. The package caret will try 25 different values to find the optimal number of components.

Data pre-processing is a crucial step in data analysis and machine learning that involves preparing raw data for further processing and analysis. Proper preprocessing helps in improving the quality of the data, which in turn enhances the performance of machine learning models. Smoothening VIS-NIR spectra enhances signal quality by reducing noise, crucial for better spectral data interpretation. Key methods include Savitzky-Golay and moving average filters, wavelet transform (WT), and frational derivatives.
Savitzky-Golay Filter: The Savitzky-Golay filter is widely used for smoothing spectral data. It preserves the original shape and features of the signal better than simple averaging filters.Savitzky-Golay Filter fits low-degree polynomials to successive subsets of data points using linear least squares, effectively preserving the signal’s original shape and features, making it ideal for spectroscopy. Moving Average:Moving Average is a simple method that reduces random noise by averaging neighboring data points. While effective, it can sometimes blur sharp features.
Wavelet Transform: Wavelet transform is effective for denoising spectral data, especially when dealing with non-stationary signals.Wavelet Transform (WT) decomposes spectral data into multiple scales, providing both time and frequency information. It is particularly useful for identifying features like peaks and trends. WT includes Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT), and Maximal Overlap Discrete Wavelet Transform (MODWT). Fourier Transform Filtering: Fourier transform can be used to remove highfrequency noise components from the spectra.
Median Filter: Median filtering is useful for removing outliers and preserving edges in the spectral data.
Fractional derivative:Fractional derivative generalizes traditional derivatives to non-integer (fractional) orders, allowing for arbitrary order derivatives. This provides a flexible, nuanced approach to modeling and analyzing complex, nonlinear, or memory-dependent behaviors.

Introduction

Soil is one of the vital resource, and is the basis of the existence of mankind. Management of this resource including its conservation and utilization is of crucial importance. It is a base on which all life depends. In the recent past, with burgeoning populations and the national goals of seeking self-sufficiency in food and fibre production, this resource base is slowly being stripped. While natural systems often adapt to stress in a remarkable fashion, some relationships - once destroyed - can never be restored. Soil resources can be assessed and monitored through its spectral signatures gathered through remote sensing.

The term ‘remote sensing’ is commonly restricted to methods that employ electromagnetic energy (such as lights, heat, micro wave) as means of detecting and measuring target characteristics. Sensors acquire data as various earth surface features reflect or e mit electromagnetic energy (EM energy). Remote sensing is the technology that is now the principal method (tool) by which the earth’s surface and atmosphere (as targets or objects of surveillance) are being observed, measured, and interpreted from vantage points. Remote sensing of the earth traditionally has used reflected energy in the visible and infrared, and emitted energy in the thermal infrared and microwave regions to gather radiation that can be analyzed numerically or used to generate images whose tonal variations represent different intensities of photons associated with a range of wavelengths that are received at the sensor.

The spermosphere represents a short-lived, rapidly changing, and microbiologically dynamic zone of soil surrounding a germinating seed. It is analogous to the rhizosphere, being established largely by the carbon compounds released into the soil once the seed begins to hydrate. These seed exudations drive the microbial activities that take place in the spermosphere, many of which can have long-lasting impacts on plant growth and development as well as on plant health.

Sprinkler irrigation is an advanced method of irrigation in which water is sprayed to air and allowed to fall on the ground similar to rainfall. The spraying of water occurs through nozzle connected to a network of pipes with water under pressure. The rate of application and area of coverage under a sprinkler is regulated by suitable selection of nozzle size and pressure in the system.

History of Sprinkler Irrigation

The sprinkler irrigation has started elsewhere in the world in the early part of twentieth century. Before 1920, sprinkler irrigation was limited to orchard, nurseries and intensive vegetable cultivation. The cost of sprinkler reduced considerably by the development of impact sprinkler and lightweight steel pipe with quick couplers in the 1930s. As a result, sprinkler irrigation began to spread in larger area and to the area of field crops. However, sprinkler irrigation developed mainly after the Second World War with the introduction of lightweight portable aluminum pipe.

Sprinkler irrigation has started in India from mid of 1950s. Due to requirement of high initial investment, the average Indian farmers cannot afford the system and therefore the area under sprinkler irrigation in India was 0.66mha (approx.) out of total irrigated area of 87.80mHa in 1995 (INCID). The sprinkler irrigation was first introduced in India in hilly regions such as Western Ghats in Kerala, Tamil Nadu, Karnataka and in the North Eastern States especially by plantation owners for irrigating tea, coffee and cardamom crops. Since the cost of aluminum pipe is high, to replace it, High Density Polyethylene Pipe (HDPE)/PVC with suitable modification have been introduced in sprinkler system. However, the area under HDPE/PVC piping system is below 20% to the total area under sprinkler irrigation. The remaining area is cultivated under aluminum or steel based sets. The indigenous manufacturing of the system has started in India only 35-40 years ago and there are about fifty numbers of manufacturers till 1996 (INCID). The details of the cultivated area under sprinkler irrigation system in the world and in Indian States are given in Table 8.1 & 8.2 respectively.

Experiment

An experiment is a planned inquiry to obtain new facts or confirm or deny the result of previous experiment. Such inquiry can aid in an administrative decision, such as recommending a variety, a feed, a cultural practices, a fertilizer, a pesticide or a fungicide etc.

Type of Experiments

    1.     Preliminary experiments : Here investigator tries out large number of treatments in order to obtain leads for future work. Here replications are not taken for such treatments tried e.g. number of pesticides for control of diseases - take single observation for each treatment and take decision that some of them worth trying for use.

Rainfall, runoff, evaporation, and other hydrological variables exhibit considerable natural uncertainty and therefore cannot be treated as entirely deterministic phenomena. Statistical and probability methods make it possible to organize observations, describe their variability, identify relationships, estimate extreme events, and predict values associated with selected probabilities. Hydrological data may be analysed daily, weekly, monthly, seasonally, or annually and expressed through averages, maxima, minima, dispersion, skewness, and frequency. This chapter introduces statistical terminology, correlation, regression, probability distributions, plotting positions, goodness-of-fit tests, model-evaluation tools, reliability, standard error, and risk analysis. These techniques are essential for flood-frequency analysis, planning, and hydraulic design.

Data mining is a powerful technology which helps in extracting future trends and behaviour from large databases. Thereby it helps decision makers to make knowledge-driven decisions. One common form of real-world data is time series. The suitable models are required to analyze time series data. This chapter provides a short introduction to stochastic system, uncertainty, basic statistics time series analysis and some stochastic models.

3.1 Introduction

Water used for irrigation can be stored mainly in three sources i.e. soil reservoir, surface reservoir or groundwater reservoir. Accordingly the storage system can be defined as soil reservoir storage system, surface reservoir storage system or groundwater storage system. Each of these storage systems has its own merit and demerit. Some of the storage systems though economical but are not helpful to store the irrigation water for longer periods. On the other hand, some other storage systems are very costly, but the irrigation water can be stored there for a longer period. However, before adopting a particular storage system, economics of the system need to be calculated. The cost of installation of the system and the water stored in the storage system and its use as supplemental irrigation to the crops to increase the production in terms of monetary benefit need to be assessed and then the economics of the system should be computed. The storage system that gives maximum benefit-cost ratio and net present value should be adopted for storage of irrigation water. The different types of storage systems are discussed below.

Introduction

According to the Forest Survey of India (FSI) “all lands, more than one hectare in area, with a tree canopy density of more than 10% is defined as Forest” (FSI 2009). The status of forests and forest management systems contribute to the vulnerability of forests to climate change. Indian forests are extremely diverse and heterogeneous. Forests and Climate Change are intimately intertwined. Forests have regulated the climate, rain, groundwater, soil of the earth over millennia. Their transpiration act as a regulator of the balance of oxygen and carbon-di-oxide: the world’s forests and forest soils currently store more than one trillion tons of carbon – twice the amount found floating free in the atmosphere. The forest and soils are home of biological diversity.

Application of the principal of geomorphology provides information, which will be of value in predicating the geometry of aquifers. On weathering and erosion, many geological formations develop landforms that are distinctive in respect of slope continuity of outcrops and symmetry of valley flanks. The surface topographic features of bedrocks can some times be extrapolated to reasonable depths to predict the thickness of alluvium or aeolian sands occurring as valley fill deposits, by treating slope profile as mathematical curves for which equations similar to regression equations can be found (Doornkamp and King, 1971).

The influence of tectonic movements and resultant structural features on landform is of paramount importance for hydrogeomorphologists. The control of structure on landforms and hydrogeological processes are being discussed here. The structural geology concerns with the features such as uniclinal or inclined beds, folds, faults and joints. Of these the structural features like faults, fractures, joints etc appear as linear feature.

Linear features on the surface of the earth have attracted the attention of geologists for over one hundred years. This interest has grown most rapidly since the introduction of aerial photographs into geological studies. Geologists have recently proven that various structural features perceived in remotely sensed images are reliable indicators of geologic resources (Caran et al, 1982). These structures have been used in many applications: petroleum and mineral exploration (Blanchet, 1957); nuclear energy facility siting (Seay, 1979); geothermal assessments (Woodruff et al, 1982); and water resource investigations (Lattman and Parizek, 1964). Lattman and Parizek (1964) established a relationship between the occurrence of groundwater and fracture traces for carbonate aquifers, and in particular zones of localized weathering and increased permeability and porosity underlying these structures.

Abstract

Groundwater is one of the major sources of exploitation in arid and semi -arid regions of Rajasthan. To protect groundwater, its data on spatial distribution are important along with quantity. Analysis of quality of groundwater is also very important, because the physical and chemical characteristics of groundwater determine its suitability for agricultural, industrial and domestic usages. The importance ground water increases many fold for arid tracts like Western Rajasthan. Here quality of water is also matter of serious concern. Geostatistics methods are most advanced techniques for interpolation of Groundwater quality. The present study appraises the groundwater quality of western Rajasthan for agricultural purpose with GIS technology. Kriging Raster Interpolation technique of spatial analyst module in Arc GIS software has been used to generate the spatial distribution map of ground water for agricultural purpose. In western Rajasthan only 28% (58109 km2) of ground water falls under the category of good and is suitable for agricultural use. 

Abstract

Conventional wisdom holds that variability in rainfall represents a significant variability of agriculture production. But urbanization of the cities on one hand and climate change on theother hand areaffecting the agriculture. Surprisingly, there have been very few analyses exploring the link between rainfall variability and urbanization on agriculture production. Therefore an attempt is made in this paper to know the affect of rainfall variability and urbanisation on the cropping area. For this purposeNDVI analysis is carried out in the catchments of Osmansagar and Himayathsagar Reservoirs by using the medium to high spatial resolution multi spectral data provided by remote sensing satellites, such as Landsat TM, IRS-1C/1D LISS-III and IRS-P6 LISS-III has been used for the years1989, 1994, 1998, 2002 and 2008 for Kharif season, 1975, 1990, 1994, 1998, 2003 and 2008 for rabi season which is collected from NRSC (National Remote Sensing Centre). From this analysis it is found that the increment is more in rabi season compared to kharif season due to the increment of non-monsoon rainfall intensities and ground water withdrawal structures. The increased utilization of the groundwater is causing the reduction of inflows to the reservoirs in the recent years because whatever the rainfall that occurs in the catchment, first utilised for meeting the depleted soil moisture and ground water consequently reducing the surface runoff.

Keywords : Climate change, Urbanisation, Inflows, Cropping area, NDVI