eChapters

Many times, users have to work with raster datasets of different spatial and radiometric resolutions. So, there is requirement of bringing their resolutions to the same resolution in order to facilitate their processing. Resampling is the process of deriving pixel values from the existing image to the new raster image of different pixel size. Resampling is most common pre-processing step in remote sensing. It is done both at spatial and radiometric scale. At spatial scale, resampling is done either to increase the spatial resolution i.e. decrease pixel size known as Upsampling or to decrease the spatial resolution i.e. increase pixel size known as Downsampling. Radiometric resampling is performed to increase or decrease the radiometric resolution of the image. In R, resampling of raster image is performed by raster package. The details of the resampling methods and their respective R – code with examples are discussed in this chapter.

First of all, we need to import the raster image on which we wish to perform resampling operations by using following R – code. In R, resampling is feasible on one band only. So, we will import single band raster image.

Mosaic is the combination of two or more raster images into single image. Sometimes, single image captured by remote sensing satellite doesn’t completely covers the desired study area mostly. Therefore, users resort for mosaicking two or more images captured by satellite sensors from different paths so that the whole study area is merged into a single raster image to facilitate the image processing and analysis. The pixel size of the raster images should be same for mosaicking.

In most of the cases, there is spatial overlapping among the raster images from which we want to create mosaic. These overlapping areas are handled in multiple ways which is being discussed further using R – code example. For mosaicking raster images in R, mosaic function of the raster package is used.

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

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

Raster sub-setting is done to select the desired area or bands of the raster image on which the user wants to perform analysis. Raster sub-setting is done in two ways:

Spectral subset: Spectral sub-setting is done to extract few desired bands from the multiband raster image which are required for analysis.

Spatial subset: Spatial sub-setting is done to minimize the area or extent of raster image to the desired area or extent for further analysis.

Raster sub-setting in R is done by functions available in the ‘raster’ package. The following packages are required in this chapter:

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.

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

Sensors onboard the satellite records the intensity of emitted or reflected electromagnetic radiations in the form of digital number (DN) for each pixel. These digital numbers can be transformed to more meaningful intensity units like radiance or reflectance. This process of conversion of digital numbers to radiance or reflectance is known as radiometric calibration. Sensor specific information is required to conduct radiometric calibration.

The satellite data is available in raster format. Raster data consists of two – dimensional array of pixels or grids which stores the values representing features. All the pixels are of same size and shape with their particular coordinate location. A raster data format is shown below.

2.1. Open source satellite data

The satellite images have been invaluable information sources for Natural Resource Management. They provide synoptic view of the Earth periodically helping in inventory, surveying, and monitoring in agriculture, hydrology, geology, mineralogy, land cover, land use and environment. Free global satellite image data of spatial resolutions of 1m to 1 km and spectral resolution of single band to hyperspectral are available from an increasing number of sources (Table 2.1).

1.1 R and R studio

R has become the world-wide language for statistics, predictive analytics, and data visualization. R is not limited to statistics; it has a wide range of tools for spatial analysis and digital image analysis and can handle any process needed for image analysis flexibly. It offers a wide range of methodologies for digital image processing, from the most basic to the most complex. As an open source project, it’s freely available for a range of platforms, including Windows, Mac OS X, and Linux. An integrated development environment (IDE) for R, R Studio, allows interactive execution of R functions with user interface. RStudio is available in two formats - RStudio Desktop and RStudio Server - both available in free and fee-based (commercial) editions.

1.1.1. Download and install R

The R software setup files can be downloaded from any of the widely distributed Comprehensive R Archive Network (CRAN). To download R, please choose your preferred CRAN mirror, a location close to you. Follow the following steps:

Abstract

GIS has several products that can be protected in India as copyright. The paper describes major provisions of copyright Act, 1957 that has been amended five times. GIS products qualify to be registered within various sections of the copyright Act. Patenting of software is permitted in some countries but not in India, a comparative account of these two forms of protection has been discussed with practical examples. General public licensing of open source software, translation, exclusive licensing and data sharing & accessibility policy are other areas covered in the present study and analytical learning.

Key words : copyright, patent, GIS, software, data sharing

1.0 Introduction

The copyright Act 1957 was mainly amended in 1994 and1999 to bring into complete harmony with Trade Related Aspects of Intellectual Property Rights (TRIPs), further major amendment has been done in 2012, that include wide range changes in ownership, term of protection, copyright security, cinematography and broadcasting provisions. In the same year government of India has initiated ‘National Data Sharing and Accessibility Policy (NDSAP)’ which came into operation from February 2012. At present the copyright Act and data sharing policy are the two legal and administrative instruments which facilitate the intellectual property protection and appropriate sharing among stakeholders in the country.

Abstract

Any naturally occurring phenomena varies both in space and time. This variability is a result of various natural process and hence deterministic. Geostatistics is a tool to help us to characterize spatial variability and uncertainty resulting from imperfect characterization of the variability. Geostatistics involves the theory of regionalized variables, which dates back to the early fifties and includes concepts of random function and stationarity. Geostatistical mapping can be defined as analytical production of maps by using field observations, explanatory information, and a computer program that calculates values at locations of interest. There area number of spatial prediction models depending on the amount of statisticsinvolved in the analysis. Kriging is a generic name adopted by the geostatisticians for a family of generalized least- squares regression algorithms and various kriging algorithms viz., punctual kriging, block kriging, co-kriging and universal kriging are explained in this paper. Applications of geostatistics can be found in many varied disciplines ranging from the geology to soil science, hydrology, meteorology, environmental sciences, agriculture and even structural engineering and some of its applications are summarized in this paper.

Keywords :Geostatistics, spatial variability, kriging, model

Abstract

GIS in marine fisheries has taken off much later than its application in land. But the strides made in this field since 1990’s has been quite significant, as many serious mapping studies and modeling initiatives have been recorded. With the third dimension added to the 2D spatial data, the marine GIS have all the ingredients of making it a fertile domain for research for decades to come. Most of the studies and products pertaining to GIS in fishery management had been revolving around mapping of habitats, geo-referencing of catch and effort data and modeling linking oceanographic and biogeochemical factors with living resources. Satellite remotely sensed information like ocean colour can act as a surrogate to fish biomass. Further remotely sensed SST, wind and other parameters can be effectively modeled to predict fish abundance, their habitat and related aspects. This manuscript pours into the various products and outputs based on GIS in marine fisheries around the world and the Indian initiatives.

Key words: GIS, marine resources, oceanography, primary productivity and climate change

1.0 Introduction

Geographic Information Systems (GIS) have been one of the most prolific scientific enhancements in recent past, which by virtue of out of box expansion of traditional database concept have opened up an entirely new vista of knowledge and opportunities. It’s probably one such unique application where information collation and analysis happen hand in hand resulting in palpable mining of data in perspective. A formal introduction to GIS would define it as a collection of computer hardware, software, data and personnel designed to collect, store, update, manipulate, analyze and display geographically referenced information. By virtue of its conceptualizations, there are no single GIS; systems can be assembled in an infinite number of ways. This is one such field, which probably always seeks the frontiers of innovations in the fields of ICT and modeling.

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 social and economic development of any state is interlaced with the manner of its natural resource management. The unplanned use and overexploitation of resources are results in various kinds of land degradation, biomass deterioration and siltation of tanks. In addition, the low per capita availability of land, erratic and uneven distribution of rains, undulating topography, improper management, traditional cropping programmes and recurrence of droughts having cumulative effect leading to low productivity and high risk particularly in dryland farming. Further, developing dry land agriculture through sectoral approach did not bring in the desired results, as the efforts were not integrated. However, in nature all the resources are inter-linked and thus, the integrated developmental approach is the best solution for optimal development of any area. Further, development based on administrative units such as district, taluk, block and village have resulted in imbalance and sometimes, no overall development in some areas. Considering this, natural resources management through Geo- informatics tools like, remote sensing, GIS, GPS and other sensor based techniques are the possible ways for making agriculture sustainable. The paper mainly highlights theimportance of application of latest technologies in agriculture along with some field level case studies.

Keywords: Remote sensing, GIS, Watershed, CAPE, Precision agriculture

Abstract

A huge technical improvement in Information Technology (IT) has made new instruments (e.g. WEB-GIS, Open Source platforms, DBMS with geometric data) available. Distributing geospatial information on the Internet is an enforcing factor for information providers. For dissemination of spatial data, Web-GIS technology can be used, which is amalgamation of several information technologies. Open Source is a philosophy of providing software with better quality, more flexibility, and lower cost. This paper explains the use of Open Source Web-GIS which provides a fast and globally reachable way not only to represent the information about natural resources through maps and statistics but also possibility of processing the information interactively on web.

Keywords : Web-GIS, Open Source, PostgreSQL, PostGIS, GeoServer, Wetlands

1.0 Introduction

India is blessed with diverse and abundant resources. India has been witnessing tremendous growth which has impacted practically every aspect of environment and the common man. Natural resource has been tremendously in use due to increase in population growth, thus generation, dissemination and sharing of information regarding natural resources and data amongst all stake holders have become a necessity. The utilization of modern technologies such as remote sensing, GIS which are effective in management of natural resources as user can store, retrieve and model the spatial information. Web media is playing a vital role in making people aware about the importance and conservation of natural resources. We are presenting such efforts with use of Free and Open Source Software (FOSS) tools.

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.

Abstract

Natural resources and agricultural production processes vary spatially. Capturing the spatial variations in data and knowledge of natural resources and production processes has been a major challenge for efficient and sustainable management of agricultural production systems. Geographical Information Systems (GIS) provide an effective platform for storing and mapping spatial data and modeling the relevant processes. Huge volumes of geospatial data on specific themes such as soils, water, climate, and other natural resources, are being collected by public and private agencies. However, currently it is difficult for most users to access the data and integrate them into a GIS platform to derive useful information and knowledge for specific applications. There is a need to create geospatial data and knowledge systems that can provide efficient, on-demand and remote access of spatial data to users to enable development of a variety of GIS applications for agricultural and natural resources management. The design of a prototype geospatial library to provide web-based access to geospatial data, services, and learning and knowledge resources that can support agricultural and natural resources management is presented. The design is scalable and sufficiently general to allow a range of agricultural and natural resources management applications across different spatial scales.

Keywords: Agriculture, GIS, Geospatial library, Knowledge Management, Natural Resources

Abstract

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

Keywords: Remote sensing, weather, agroadvisory, GIS

1.0 Introduction

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

Abstract

Anantapur is the driest district of Andhra Pradesh and hence, agriculture conditions are very often precarious. Groundnut grows where other crops fail and thus it is the predominant crop of Anantapur district. Groundnut is grown in about 7.5 lakh ha in Anantapur district; however the average yields are low at 500 kg ha^-1. Among various available vegetation indices, normalised difference vegetation index (NDVI) is widely used for all reasons, which is a single numerical indicator of presence and condition of green vegetation. NDVI mapping at a regional scale helps to assess the spatial changes in the vigour of green vegetation and thus occurrence of any moisture stress. Climate tools have a great role in understanding the crop performance and estimating the yields. This study was taken up by using freely available MODIS data to understand NDVI in terms of abiotic stresses over Anantapur district and linking with the actual rainfall conditions, groundnut crop acreage and production. Results indicate that low groundnut yields in general could be related to NDVI-based stress measurements and rainfall quantum and distribution in the area however, with a few exceptions. It is hoped that by combining improved practices through IWM with climate-adapted crop varieties, rainfed farmers of Anantapur district can sustain their crop production under present climate variability and become resilient to future climate change.

Keywords: Climate, MODIS, NDVI, Anantapur, Groundnut, Integrated Watershed Management

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

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

The sustainable management of plant genetic resources is of great concern as increasing population and rapid technological strides are putting enormous pressure on this country’s nutritional and food security. Management of plant genetic resources at national level generates and uses enormous data and analysis of these data is crucial to the effectiveness of PGR management process and can add significantly to the value of genetic resources. A GIS is a database management system that can simultaneously handle data representing spatial objects and their attribute data. GIS allows us to view, understand, question, interpret, and visualize data in many ways that reveal relationships, patterns, and trends in the form of maps, globes, reports, and charts. It can play an important role in the management of large and complex PGR datasets. The article analyses the potential uses of GIS in plant genetic resources management in India covering various activities such as germplasm exploration and collection, characterization and evaluation, plant health management, documentation and conservation.

Key words : plant genetic resources, germplasm, conservation, documentation, DIVA-GIS,

Abstract

Geographical Information Systems (GIS) application in plant genetic resource management allow capture, manipulate, process and display of georeferenced data integrated with genetic resource database for operations such as query and statistical analysis, visualization and geographic representation. GIS based several spatial tools have been developed that model species’ ecological niches finding a relationship between crop or variety with a set of environmental parameters and useful for planning exploration, evaluation trials, in situ and ex situ conservation sites. GIS technologies have direct practical application in elucidation of genetic, ecological and geographic patterns in the distribution of crops and wild relatives; mapping of richness, diversity, distribution of useful traits and, location of areas with complementary diversity; gene bank information management and strategies for future explorations and gap analysis; selection of suitable accessions/sites for ex situ evaluation; searching germplasm for abiotic stress tolerance and developing core collections. With the increasing availability of high- speed computers and software development in the area of GIS tools specific to PGR management increasing trend in application of these is expected. Apart from ecogeographic survey for locating diversity and germplasm collection and design, management and monitoring of in situ reserves, introduction of new varieties, and development of core collection, locating gene donors for biotic and abiotic stresses are the areas to be benefited with the GIS application.

Keywords: GIS, genetic resource, distribution, diversity, core collection, gap analysis, mapping

Abstract

Agroforestry is an integrated self-sustainable land use management system that is not only capable of producing food from marginal agricultural land but also capable of maintaining and improving the quality of environment. It plays a vital role in achieving integrated rural and urban development. But reliable data on number of trees and area under agroforestry is not available as methodology for estimation of even area under agroforestry is not yet standardized. In this paper, a methodology for estimation of number of trees under agroforestry using high resolution satellite data has been proposed and estimation of number of trees as well as area under agroforestry in Ludhiana district of Punjab State has been done using the proposed methodology. Land use/land cover map of the district has also been generated using ERDAS IMAGINE software. It has been observed from the accuracy assessment that the estimates of number of trees as well as area under agroforestry obtained using the proposed methodology under this study are very reliable. Therefore, the proposed methodology may be replicated for estimating number of trees as well as area under agroforestry in other parts of the country.

Keywords: Agroforestry; Estimation of number of trees, Area estimation; Remote Sensing; GIS

Abstract

Agroforestry is an ancient practice that farmers have adopted throughout the world. There are innumerable examples of traditional land use practices combining production of trees and agriculture crops on the same piece of land. In India, the organized agroforestry research began in 1979 when a Seminar on Agroforestry was organized at Imphal by the ICAR, New Delhi. As a follow up to this, ICAR launched an All India Co-ordinated Research Project on Agroforestry (AICRPAF) was started in 1983. The diagnostic survey and appraisal revealed that there are enumerable agroforestry practices prevalent in different agro- ecological zones of India occupying sizeable areas. However, spatial technologies like Geographical Information System (GIS), Remote Sensing (RS) and Geographical Positioning System (GPS) have yet to be implemented extensively in this field in India. The spatial technologies have shown great potential in natural resource management through out the world, especially in developed countries in the fields like forest cover mapping, agricultural planning, watershed management, land use planning, etc. In India the agroforestry land use occupy large areas but use of these technologies to estimate area has been initiated in year 2007 with DST sponsored project entitled ‘Spatial and Temporal analysis of agroforestry interventions in North-western India using GIS and Remote Sensing’ The area under agroforestry systems has been estimated/ mapped in two districts viz., Yamunanagar (Haryana) and Saharanpur (Uttar Pradesh), where intensive commercial agroforestry was predominant. Besides, the methodology developed as an out come of this study may be replicated for agroforestry systems existing in other parts of the country.

Keywords: Agroforestry, Geo-spatial, Mapping, Natural Resources, Remote Sensing.

Abstract

Remote sensing and Geographic Information System (GIS) are integrated system in providing precise information on nature, extent and spatial distribution of land resources to assess their potential and limitations for planning, monitoring and management towards sustainable development. The use of remote sensing and GIS provide immense opportunities in the field of large-scale mapping, generate and update of existing geographical databases for decision making in site- specific management and development of land resources. Integration of remote sensing and GIS includes the use of each technology to benefit the other, as well as the application of both the technologies for geospatial modelling and decision support. Remote sensing and GIS integration has clearly benefited from advances in computing and Global Positioning System (GPS) technologies, including innovations in use of decision tree algorithms and neural networks. The generated precise spatial database in GIS on land resources, socio-economic and contemporary technologies could be effectively used to develop GIS based Spatial Decision Support Systems (SDSS) for sustainable management of land resources. In this article an attempt has been made to provide an overview on the remote sensing and GIS technologies as a special decision support tools in inventory, characterization and management of land resources particularly in digital terrain analysis, geomorphological mapping, hydrological applications, soil resource inventory, assessment of soil loss, land degradation and soil fertility, soil-site suitability and watershed prioritization.

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

Abstract

Runoff from rainfall is an important component of the hydrological cycle. Estimation of runoff is critical for the design of hydrological structures and drainage systems in watersheds. Different soils, land use, and water management practices affect runoff differently. In real watersheds, land use, soils and weather conditions vary spatially over the geographical area of the watershed leading to spatial variations in runoff. Also, most watersheds form a part of larger drainage basin or a large watershed consisting of several such subwatersheds. Each subwatershed is hydrologically connected to the other subwatersheds of the basin. A comprehensive GIS based decision support system (DSS) for estimation of runoff that includes the spatial variations in rainfall and natural resources is presented. The DSS is developed as a deployable application by integrating independent GIS layers of watershed features created in ArcGIS with MapObjects in VB, for a case study watershed - the KK3 watershed in Mahbubnagar district of Andhra Pradesh, India. The DSS generates thematic maps of spatial variations in runoff on individual rainy days for the subwatersheds.

Keywords : GIS, GUI, Land use, Rainfall, Remote Sensing, Runoff, Soil, Sub-watershed, Watershed 228 Geospatial Technologies for Natural Resources Management

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

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

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

In the present study an integrated remote sensing and Geographical Information System (GIS) based methodology is adopted for demarcating the groundwater prospective zones and identification of artificial recharge sites in the Peddavanka watershed, Andhra Pradesh. In this study Shuttle Radar Topograhic Mission (SRTM) digital elevation model (DEM) with spatial resolution of 90 m and Indian Remote Sensing Satellite (IRS-1A), Linear Imaging Self Scanner (LISS- III) satellite data along with other field and collateral data on geology and well inventory have been utilized to generating the Drainage, Slope, Lineaments and Hydro geomorphology of the study area, which are an essential prerequisites for demarcating water resources and identification of recharge sites. Spatial data integration and analyses are carried out in GIS environment.

Keywords : DEM, Drainage, Hydrogeomorphology, Groundwater prospective zones, Remote sensing, GIS

1.0 Introduction

Groundwater is an important natural resource in arid and semi- arid regions, because most of the places surface water resources are scarce and some places it’s totally absent particularly in hard rock areas. Because in hard rock terrains availability of groundwater is of limited extent and its occurrence is essentially confined to fractured and weathered zones (Saraf and choudhury 1998, Sankar 2002). In these areas groundwater recharge depends on rainfall only. For understanding rainfall recharge mechanism and groundwater management in the watersheds level, first we have to understand the morphometric parameters in the watershed level.

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

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

Abstract

The rapid development and integration of spatial technologies viz., Geographical Information System (GIS), Remote Sensing (RS) along with Global Positioning Systems (GPS) provide functional alternatives to revolutionize the conventional entomological approaches like surveillance, spatial/temporal distribution of insect populations, pest distribution mapping and predictive modeling. Application of these technologies for insect problems is well established in the fields of forest, range land, medical and agricultural entomology elsewhere. Nevertheless, in India its field relevance in agricultural entomology is still limited owing to lack of proven field prototype studies and collaborative efforts among stakeholders. It’s time to gain real momentum to integrate these progressive technologies to formulate sustainable integrated pest management programs.

Key words: GIS, Remote Sensing, Insect Populations, Field Applications

Abstract

Carrying out effective and sustainable agriculture has become an important issue in recent years. A key to this is the usage of modern technologies for precision agriculture. Recent technological developments allowed envisioning sensor devices with distributed ambient sensory network, which could be a potential technology for monitoring various natural phenomena (weather parameters, soil moisture, etc.) at micro level. As more and more agricultural data are virtually being harvested along with the crops and are being collected/ stored in databases, the same can be used in productive decision making if appropriate data mining techniques are developed/applied. As a case study for this, an experiment was conducted with four consecutive agricultural (Kharif and Rabi) seasons in a semi-arid region of India to understand the crop-weather-environment-pest/diseases relations using wireless sensory and field-level surveillance data on closely related and interdependent pest/disease dynamics of groundnut crop. Association rule and multivariate regression mining techniques/algorithms were propose developed /tailor-made to turn the data into useful information/ knowledge/relations/trends to know crop-weather- environment-pest/disease continuum. These findings have been used for development of prediction models (cumulative and non-cumulative) followed by a web based pest/disease decision support system.

Keywords : Data Mining, Pest/Disease Management, Precession Protection, Wireless Sensor Networks

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)

Abstract

The present study discusses the cropping pattern changes in Andhra Pradesh agriculture will leads to crop diversification and crop concentration using with Geographical Information System (GIS). Crop data were compiled for the triennium ending period 1980-81, 1990-91, 2000-01 and 2004-05 from various statistical abstracts of Andhra Pradesh. For measuring the crop diversification indices, we have used the Herfindahle index and for crop specialization is measured by using the Location quotient method. The level of crop diversification in the some districts of the state in the eighties is very high. However with the advent of green revolution, there had been a moderate crop unification or crop concentration in the eighties. This trend of crop unification is found to be reversed after 1990-91, though the magnitude of the change is not very striking. The crop diversification is observed to differ across the districts. The level of crop diversification is high in the agricultural backward regions of Rayalaseema and Telangana districts and low in the agriculturally progressive Coastal Andhra districts. During the period 1990-91 to 2004-05, the major trends in crop concentration are increase in the concentration of rice in the districts where it is popularly grown and decline in the concentration of coarse cereal crops, pulses and oilseeds. The concentration of spice crops and fruits and vegetables are also on an increasing trend, which they are popularly grown in their respective districts.

Keywords : Cropping Pattern, Diversification, Location Quotient, Andhra Pradesh, GIS

Abstract

Food security to the increasing population on one side and the increasing export of food grains on the other side has lead to the immediate attention on to raise the food production in our country. The decreasing land availability for agriculture due to the increased degraded lands and use of agricultural lands for non agricultural purposes has still worsened the situation. In order to match with the food requirement from decreasing agricultural lands, Indian agriculture has been pushed towards indiscriminate use of farm inputs exploiting the non renewable natural resources without any concern to the environmental ill effects leading to further degrading of the situation. Even though the concept of sustainability is age old, the importance is felt recently. Sustainable use of resources at farm level can magnify the effect of conserving non renewable resources and reducing ill effects on the environment at state level and to the country. Sustained farm production is a key to manage our resources and environment. Adoption of new information technologies with the conventional agronomic practices to increase farm production is made inevitable due to our past experience. The advancement in the technology tools like Remote Sensing, Geographic Information System, Global Positioning System and Decision Support Systems have paved way for better and effective management of crop production at farm level. 46 Geospatial Technologies for Natural Resources Management

A systematic approach of management involving advanced technologies and conventional agronomic practices through understanding the factors like soil variability, weather parameters and crops that contribute for the sustainable farm production is need of the hour. The spatial and temporal variability of the farm production parameters are to be studied and mapped in relation to historical data of the farm for successful site specific management. Site specific management tools may improve decisions related to site conditions, thereby reducing the aspect of uncertainty in the management system. This approach of farm management using advanced technologies viz., Remote Sensing, Geographic Information System, Global Positioning System, Variable rate technology, crop production modeling and Decision Support System can enable sustainable farm production with beneficial effect on the environment in future.

Keywords : Remote sensing, GIS, Sustainable Farm production, Site Specific Management

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

The ever increasing population in India and the world, impeding threats from the climate change scenario and the increasing demand for land resources from the industrial sector calls for improved resource efficiency. The production strategies are changing from input intensive to integrated resource management strategies. The shift is towards adopting the precision farming technologies like addressing the site specific variability and crop needs. Remote sensing technology coupled with GIS has been recognized as reliable tool in agriculture to guide application of fertilizer, pesticides and other farm inputs. With precision farming, technologies such as grid soil sampling, remote sensing, on- the-go sensing, and electronic yield monitoring help farmers gather very detailed information about the heterogeneous makeup of a farm field. Site specific applications means application of required input only where it is required rather than entire fields or adjusting application rates accordingly. This chapter discussed the input management based on the time of intervention - Input management prior to crop sowing, in season crop management and end of the season.

Keywords : Management zones, soil variability and Remote Sensing

1.0 Overview

Natural resources sustain life and are critical for economic development of nations. In developing countries like India where more than half the population is rural, economic development is inextricably linked with agricultural development. In these countries, the food and livelihoods security of the poor are critically associated with access to the services provided by the natural resources - land, soil, water, biodiversity and climate resources. The natural resources are finite, and with increasing population, economic growth, urbanization, and demands for food and other agricultural commodities, these are being increasingly strained. As a result, the productive capacity of the natural resource base is shrinking and deteriorating. Sustaining the productive capacity of natural resources for future generations, while meeting the food and other demands of the growing populations, is the major development challenge of the twenty first century.

In India, during the last forty years the net sown area under agriculture has remained nearly constant at 141 million ha. In the same period, the population has more than doubled (from 541 million in 1970 to 1186 million in 2010) and per capita income grew by more than 3.5 times (from Rs 14824 in 1970 to Rs 53331 in 2010). As available land resources for agriculture are finite, increases in agricultural production to meet the growing demands of the population have to come from intensification of agriculture on the same lands. As a result, the quality of soils on these lands is gradually deteriorating both at the farm and ecosystem levels. The major threats to soil quality come from loss of organic carbon, erosion, nutrient imbalance, compaction, salinization, water-logging, decline in soil bio-diversity, urbanization, and contamination with heavy metals. More than 100 million ha of the total cultivated land of 141 million ha is stated to be under some form of soil degradation.

Similarly, utilizable fresh water resources of India are finite at about 1123 billion cubic meters (BSM), of which about 690 BCM are from surface water resources and 433 BCM are from groundwater. This supply is shared among multiple uses: human and animal consumption, agriculture, urban uses, industry and for provision of environmental services. The total water demand in 2010 is estimated at 710 BCM and is projected to increase to 1180 BCM by 2050. Several parts of India are already facing severe water shortages as a result of the growing demand for water. The quality of water resources is also impacted by agricultural intensification and development, and is increasingly a matter of concern. By the year 2050, when population is expected to stabilize, the shortages will only intensify. The country as whole can be on the verge of becoming a water scarce country, with nearly all the presently estimated utilizable water resources being put to use.

Biodiversity natural resources refer to the abundance of different species and ecosystems in nature. These resources are the primary sources of present and future productive agriculture and human well being, as they provide the basic resources for use in breeding crops and livestock, soil formation and structure, soil fertility, nutrient cycling and the provision of water services. India is among the 12 mega diversity regions of the world. It is known to have more than 18,000 species of higher plants including, 160 major and minor crop species and 325 of their wild relatives. Increasing pressure from human population growth, agricultural intensification, urbanization, pollution and degradation of land, soil and water resources are having a negative impact on the plant, animal and microbial biodiversity resources of the country and their productivity.

8.1 INTRODUCTION

To implement a good GIS project, a well structured project design and management are essential. Well planned project will give fruitful result within the stipulated time. Project not completed in time will not incur more cost and also the time delay will add negative value to the project. Hence all the elements in project planning and implementation should be considered to have a desired result. Some of the issues are also present in GIS and they will be rectified in the next decade.

8.2 PROBLEM IDENTIFICATION

Any project is coming into reality due to necessity. If water scarcity is more in a hard rock region with sufficient rainfall, it is important to augment groundwater resource using rain water harvesting structures. It is important to build water harvesting structures like check dam, percolation pond in suitable locations so that groundwater recharge will be more. Hence a problem is identified and the project work may be carried out by a government or private organisation and the result is given to the government to implement the project. Likewise many area may require a project in GIS environment for planning. Once a project is created any development taking place can be added and GIS database is updated.

7.1 INTRODUCTION

Web technology revolutionised information technology and GIS of no exception. Analysis of maps available in internet through remote personal computer is called as web GIS. The term web is GIS synonymous with the term internet GIS. Most of the software vendors in the field of GIS have developed their GIS compatible to internet. The advantage of internet GIS is no need to own a GIS software in the personal computer. Maps available in a web site can be analysed for GIS functions as one could do with GIS in standalone personal computer. Applications of web GIS is not limited. Most of the governmental agencies, business people and others put their data in their server and the data could be analysed spatially for the users requirement. Internet mapping is simple, cost effective and reach vast audience.

Internet GIS is useful in field like civil engineering, government organisations, forestry, environment, goescience, utility planning 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.

5.1 INTRODUCTION

A GIS database without single error is reliable for decision making. There are various possible sources of errors in GIS. If an error is not removed during scanning, the final output will also have that error. Besides whatever the work has been carried on wrongly scanned map it will add to errors because the source map itself's erroneous. This is like garbage in garbage out proverb. If wrong data is taken for analysis it will only result in wrong output. Hence it is important to remove error at every stage of GIS work. It is important to locate the sources of errors so that due care may be taken while handling a project.

5.2 ORIGINAL SOURCES OF ERRORS

All the equipments which give attributed data and spatial data will give errors. Before going to field or before collecting data all the related instruments should be calibrated properly. Also the instrument should not cross the permissible error. Besides these while using data from diversified sources errors pertaining to those instruments are mixed to give a different result. When very good accuracy is desired data should be collected from high end equipments. Even if one adds the data from a comparbly less accurate instrument to the data of many accurate instrument is then the result obtained finally will not reliable enough. Global Positioning System (GPS) is available with mm accuracy to meters accuracy. Also surveying with suitable map projection and datum is important; otherwise the result will be wrong. Survey instruments posses some inaccuracy and when a person is using it he should have the accuracy over the instrumental like rain gauge, water level recorders which give attributed data which should be checked for instrumental inaccuracies and method of data collection before using it. Likewise satellite and aerial sensors miss some data and some error are inherent. Such errors should be removed before moving ahead. Errors are resulted due to map reduction, enlargement, data editing and computation method adopted. At the same mapping instruments also possess inaccuracies. When after certain interval a person is going for survey, the registered object may have been changed. Many new features might have come up. Hence the established control points are changed and if the wrongly placed control points are taken for survey then the result itself will be wrong. If proper resolution is not available in the instruments, then the instruments may not capture the minute details which are required.

4.1 INTRODUCTION

Database management system is a computer program to store, search, manipulate retrieve and give the result. Vast amount of spatial data are available and it is necessary to arrange them, store them for retrieval and analysis. Attribute data adds intelligent to GIS. Without database, a GIS will be just a Cad software. Cad softwares do not have database. GIS softwares have their own databases besides it has the potential to link with external database like Oracle, MS Access, MS Excel, Informix, INGRESS, Sybase and dbase. The data available in paper has to be digitally stored in a proper order. Previously the data were stored in a a flat file format where the data was stored without any structure. But to search and retrieve and analyse the database, database structures were developed. There are of 4 types of database structures namely Hierarchical, Network, Relational database and Object orientation.

Tables are made of rows and columns. Rows represent a map feature. Columns represents characteristic of map feature. Row is called a record or a tupule column is a field or an item. Table 4.1 is a table where rows 1, 2 and 3 represent the locations with the soil types. A column soil type represents various soil types.

3.1 INTRODUCTION

Geographic Information System handles large volumes of data. It is important to know the nature of data, instruments from which the data is collected and represented in computers. GIS data is either spatial or aspatial. Data is an important component in GIS. Digital data are available for some projects. but for many projects, input data may not be available. Now-a-days, data is supplied by many government, private organizations and also available on internet. If data for a particular project is not available, then the user has to collect the data from satellite imageries, GPS and other instruments and maps from various sources.

Spatial Data

All locations in the earth surface are spatial data. Location of a city, location of a river, location of a lake are represented in earth by their latitude or longitude in global coordinate system or as x,y coordinate in Cartesian coordinate system. Spatial data consists of location, shape and size in earth. A village boundary is represented as a polygon in map. The village is having certain shape of irregular polygon with definite area. As all these are mappable , they are called as spatial data. But name of the village, revenue of a village and description about a village and called as non-spatial or aspatial data.

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: