eChapters

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

Introduction

Natural Disasters are inevitable, and Indian subcontinent is prone to all type of Natural Disaster either it is flood, drought, cyclone, earthquakes, tsunami or forest fires etc. Disasters can be classified in several ways. A possible sub-division of disaster is: (i) Natural Disasters; (ii) Human made disasters; (iii) Human induced disasters. Another sub-division is related to the main controlling factors leading to a disaster. These may be meteorological, geomorphological/geological, ecological, technological, global environmental and extra terrestrial. Another useful distinction that can be made between disasters is regarding their duration of impact and the time of forewarning. Some disaster strikes within a short period with devastating outcomes; others have a slow onset period with equally or more serious repercussions. According to the available statistics 60% of the total area of Indian subcontinent is venerable to seismic activity of varying intensities, 16% of the country’s total area is drought prone. In India 68% of total sown area of the country is drought prone. Coastal areas of India is exposed to tropical cyclones, tsunami in recent years and among all the disaster occur in the country, River floods are the most frequent and often the most devastating.

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.

ABSTRACT

Petroleum exploration is the art of searching hydrocarbon reserves in unknown regions by geological, geophysical and geochemical methods. The selection of effective exploration targets is an important step to achieve success in oil exploration and its selection also dependents on study of basic petroleum geological conditions. Petroleum geologists generally consider basins to be a basic geological unit for petroleum exploration and their main task is to find and determine various sedimentary basins. The remote sensing images have characteristics of really and macroscopic to provide accurate and visualization data for directly determining geometric shapes of sedimentary basins. Remote sensing techniques are more effective in understanding the study of basins in an out-of-the-way about mountains and remote deserts.

Key words: Petroleum Exploration, Sedimentary basin, geology, basin, geometry.

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:

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

This chapter introduces approaches for understanding, assessing, and reducing the vulnerability of forest ecosystems to climate change. It examines the impacts of changing climatic conditions on forests and considers factors such as forest fragmentation, biodiversity, tree density, disturbance, and community dependence on forest resources. The chapter discusses methods and models for vulnerability assessment, including indicator-based and spatial approaches, and explains how vulnerability can be mapped and classified. It also considers strategies for minimizing climate impacts and enhancing forest resilience through appropriate management and conservation interventions.

In the world of academic publishing, Article Processing Charges (APCs) have emerged as a significant barrier, particularly for researchers from countries with limited resources like India. These charges, often levied by open-access journals to cover the costs of peer review, editing, and dissemination, have created a financial divide that impedes equitable access to scholarly communication. This chapter delves into the multifaceted challenges posed by APCs and outlines potential solutions to address the inequities they perpetuate (Fig. 6.1).
6.1. The Burden of Article Processing Charges
The financial burden of APCs is disproportionately felt by researchers in developing nations. Many prestigious open-access journals charge APCs ranging from $1,000 to $5,000 or more per article. For researchers in high-income countries, these charges might be manageable, supported by robust institutional funding or grants. However, for their counterparts in countries like India, where research funding is limited and often tied to modest institutional budgets, such costs are prohibitive. The disparity in income and currency exchange rates further amplifies this challenge. A charge of $2,000, for instance, could equate to several months’ salary for a researcher in India, compared to a fraction of that amount for a researcher in the United States or Europe.

This chapter integrates case studies, scientific examples, and policy perspectives to illustrate how AI and ML can transform agricultural weather intelligence. The discussion spans from advanced predictive models in developed nations to emerging applications in technology-limited regions, while also addressing the urgent need for resilient crisis management systems in the face of natural and human-caused conditions. Advanced scientific techniques can not only optimize food production and resource management but also play a critical role in recovery efforts during times of crisis. Agriculture is intrinsically linked to the natural world—a domain defined by fluctuating weather patterns, seasonal shifts, and unpredictable environmental factors. Today, Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing how we analyze and predict weather patterns, and their applications in agriculture are far-reaching (Fig. 5.1). From forecasting crop yields and fishery conditions to managing plantations and monitoring livestock health, these technologies are creating a paradigm shift in agricultural weather intelligence. Scientific organizations worldwide are leveraging AI and ML to refine predictive models, enable multi-variate analysis, and provide advanced warning systems for extreme events such as droughts, tsunamis, earthquakes, and even crises caused by human-made conflicts. Emerging nations with rapidly growing technological capabilities stand to benefit immensely from these innovations, tailoring solutions to local challenges while contributing to global food security and crisis resilience.

The artificial intelligence (AI) world is quickly growing as it reaches several different industries. You currently will see AI used for many different purposes from agriculture to automotive; with the passage of time you will see even more of it. Agriculture is one of the most important branches of the IT industry. Agriculture is an important industry and is a big part of our economy’s base. In terms of annual sales, the agricultural industry contributes to our economy almost 17%.AI is becoming a technical advancement that boost and protects crop yield, as climate change and populations rise. Here in this chapter basics of Artificial Intelligence have been discussed how it can be related to agriculture.

1. Introduction

Artificial Intelligence has become most prominent in current generation. Every field is utilizing Artificial Intelligence for their better results and which also result in saving a lot of time. As Artificial intelligence is boosting different sectors to increase the productivity and efficiency and its utilization across different sectors like in the field of CIVIL Artificial Intelligence can be used to analyze the structures of buildings, dams etc., and Artificial Intelligence solutions are assisting in so many ways to overcome the traditional challenges in every field.

I. What is Artificial Intelligence

Artificial Intelligence is one of the most prominent field of Computer Science Engineering which attempts to redefine the tasks which are carried out by humans in their daily life with better results and maximum accuracy. Artificial Intelligence is actually meant to ease the work of human where the word artificial states the work done without any human interference and intelligence states the capabilities like human brain to perform tasks. The field artificial Intelligence has given so many definitions from time to time which can be typically stated as

Artificial Intelligence (AI) is technology which can store, retrieve, manipulate, manufacture, transmit or receive, analyze any digital information and can apply machine vision for increasing efficiency and workability of the system. The AI permits systematically scope of ‘3S’ i.e. Safety, Security and Surveillance, which is also known as manmade intelligence. In fact, it needs more reliable and accurate data or information for processing and correction for predicting performance of any system and devices.

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.

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.