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Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Fundamentals of Geographic Information System (GIS)

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Presentation on theme: "Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Fundamentals of Geographic Information System (GIS)"— Presentation transcript:

1 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Fundamentals of Geographic Information System (GIS) WMO/FAO Training Workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the SADC Thelma A. Cinco Senior Weather Specialist,PAGASA Resource Person Philippines

2 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) 1.Overview of GIS Definition of GIS Objectives and Potential of GIS 2.Components of GIS Hardware,Software,Data,Method,Liveware 3.Functions/Task of GIS Data Input:Data Model, Data Management:Relational Database Data quality,Map Scale & Accuracy/Errors Data analysis: Queries & Spatial Analysis 4.Application of GIS Outline

3 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) What is GIS? No consensus on the definition However, there is consensus that GIS includes the following major elements: –hardware/software –database –applications/infrastructure

4 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) What is GIS? A GIS is a specific information system applied to geographic data and ; is mainly referred to as a system of hardware, software, personnel and procedures; designed to support capture, management, manipulation, analysis, modeling and display of spatially referenced data for solving complex planning and management problems [Burroughs, 1986; NCGIA, 1990].

5 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Table 1-1. Alternative Names of GIS Multipurpose geographical data system Multipurpose input land use system Computerized geographical information system System for handling natural resources inventory data Geo-information system Spatial information system Land resource information system Spatial data management and comprehensive analysis system Planning information system Resource information system Natural resource management information system Spatial data handling system Geographically referenced information system Environment information system AGIS - Automated geographical information system Multipurpose cadastre Land information system AM/FM - Automated mapping and facilities management

6 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) GIS OBJECTIVES Maximize the efficiency of planning and decision making Provide efficient means for data distribution and handling Elimination of redundant data base - minimize duplication Capacity to integrate information from many sources Complex analysis/query involving geographical referenced data to generate new information.

7 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Powerful tools for addressing geographical /environmental issues Allows us to arrange information about a location as a set of maps Displaying information about one characteristic of the region Needs a location reference system (such as latitude and longitude) Geographic Information System (GIS)

8 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Potential for GIS Once a GIS is implemented, the following benefits are expected. geospatial data are better maintained in a standard format revision and updating are easier geospatial data and information are easier to search, analyze and represent more value added product geospatial data can be shared and exchanged freely productivity of the staff is improved and more efficient time and money are saved better decisions can be made

9 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) The questions that a GIS is required to answer are mainly as follows : What is at.....? (Locational question ; what exists at a particular location) Where is it.....? (Conditional question ; which locations satisfy certain conditions) How has it changed......? (Trendy question ; identifies geographic occurrence or trends that have changed or in the process of changing) Which data are related.....? (Relational question : analyzes the spatial relationship between objects of geographic features) What if ? (Model based question ; computers and displays an optimum path, a suitable land, risky area against disasters etc. based on model)

10 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Components of GIS

11 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Components of GIS :Hardware Input devices (Keyboard, digitizing tablet, scanner) Central Processing Unit (Pentium, AMD, Sun SPARC) Storage devices (Tape drives, floppy drives, hard disks) Output devices (Monitor, printer, plotter)

12 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

13 Components of GIS:Key software components Tools for the input and manipulation of geographic information A database management system (DBMS) Tools that support geographic query, analysis, and visualization A graphical user interface (GUI) for easy access to tools

14 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) manifold

15 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

16 Components of GIS: GIS Data Base Maps – include streets, highways, boundaries for census, postal, and political areas, rivers and lakes, parks and landmarks; place names Environmental maps - include data related to the environment, weather, environmental risk, satellite imagery, topography, and natural resources. Socio-economic data - include data related to census/demography, health care, real state, telecommunications, emergency preparedness, crime, business establishments, and transportation.

17 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Base Map Socio-economic Maps and Data Environmental Maps and Data Other Maps and Data

18 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Components of GIS:Liveware GIS Manager GIS Specialist GIS Technician GIS Analyst Computer Programmer Data Encoder Information Systems Analyst Information Technology Officer Engineer Planning Officer

19 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Components of GIS: Procedures/Methods Well-designed plan Business rules Models and operating practices unique to each organization

20 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Functions/Tasks of GIS Data Input Data Management Data Analysis and Manipulation Data Display/Visualization

21 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Functions/Tasks of GIS:Data Input The procedure of encoding data into a computer-readable form and writing the data to the GIS database. Data input includes three major steps (the latter two steps are also called data preprocessing): –Data capture –Editing and cleaning –Geo-coding

22 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Functions/Tasks of GIS: Data Input Keyboard entry Manual digitizing (e.g., tablet, on- screen) Scanning

23 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

24

25 Geographic Data Organized into Layers

26 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Data Input :Data Sources for GIS maps aerial photos satellite images technical descriptions GPS data

27 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Data Input : Geographic Data Characteristics Geographic data contains four integrated components, namely, location, attribute, spatial relationship and time. Geographic data include those which are spatially referenced. A GIS includes operations which support spatial analysis.

28 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Data Input: Geographic Data Characteristics Spatial data and their attributes are linked (seamless) –By their geographic location –By unique identifiers

29 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Data Input: Kinds of Data GIS Handles Spatial data –usually translated into simple objects: points, lines, areas and grids (pixels). –represented as maps. –Example: a parcel of land Attribute data (Non-spatial or Aspatial Data) –are descriptive information about specified spatial objects. –often have no direct information about the spatial location but can be linked to spatial objects they describe. –Usually organized in tables –Example: the owner of a parcel of land

30 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

31 Data Input: Kinds of Data GIS Handles Identify if spatial(graphical) or non-spatial(textual) –This building –The color of this building –The people within this building –The name of this building –Rizal Park –Botswana Road –Gaborone –Population of Botswana

32 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Data Input: Identifiers Enable both spatial and attribute data to be stored separately but accessed together. Identifiers are –Unique values - usually integers –Stored as part of the spatial data structure - as a numeric value (i.e., system-generated ID) –Stored as part of the attribute data structure - as a field in a table

33 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

34 Data Input: Data Model Conversion of real world geographical variation into discrete objects is done through data models. It represents the linkage between the real world domain of geographic data and computer representation of these features. Two major categories of spatial data representation in GIS: raster and vector. –Raster approach: cells –Vector approach: points, lines, and polygons

35 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Raster Vector Real World

36 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Raster Data Divides the entire study area into a regular grid of cells Each cell contains a single value Is space-filling since every location in the study area corresponds to a cell in the raster. Raster data can be imagined as collection of cells organized like a matrix.

37 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

38 Database/Internal Structure ncols 15 nrows 16 xllcenter.5 yllcenter.5 cellsize Data SummaryVisual Representation RASTER DATA

39 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Vector Data Model Represented by lines, points, and polygons. Fundamental primitive is a point Points are stored as x,y coordinates and represent features as having no dimension. Objects are created by connecting points with straight lines (or arcs) Areas are defined by sets of lines

40 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

41 Database/Internal Structure Points Lines 1 ( ),(3.1,2.7),(3.5, 1.3), (2.7,2.9), Polygons 1 ( ),(3.1,2.7),(3.5, 1.3), ( ) Data Summary Visual Representation Vector Data

42 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Format Issues Most GIS applications can utilize both vector and raster formats, and/or they can convert between the two Converting from vector to raster is easy; Converting from raster to vector is difficult

43 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

44 Industry-Standard Formats Standard vector GIS formats include: –DLG, shape files, TIGER files (preserve topology) Standard raster GIS formats include: –.JPG,.TIF, GeoTIFF (georeferenced TIF image), other digital image formats, and DEMs (which are georeferenced)

45 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Comparison of Raster and Vector Data Models. RASTER MODEL VECTOR MODEL Advantages: Advantages: 1.It is a simple data structure. 1.It provides a more compact data structure than the raster model. 2.Overlay operations are easily and efficiently2.It provides efficient encoding of topology, and as a result, implemented. more efficient implementation of operations that require topological information, such as network analysis 3.High spatial variability is efficiently represented 3.The vector model is better suited to supporting graphics. in a raster format. that closely approximate hand-drawn maps. 4.The raster format is more or less required for efficient manipulation and enhancement of digital images. Disadvantages: Disadvantages: 1.The raster data structure is less compact. 1.It is a more complex data structure than a simple raster. 2.Topological relationships are more difficult to represent. 2.Overlay operations are more difficult to implement. 3.The output of graphics is less aesthetically pleasing3.The representation of high spatial variability is inefficient. because boundaries tend to have a blocky appearance4.Manipulation and enhancement of digital images cannot rather than the smooth lines of hand-drawn maps. This be effectively done in the vector domain. can be overcome by using a very large number of cells, but may result in unacceptably large files. Source: Aronoff, S., Geographical Information Systems: A Management Perspective, WDL Publications, Ottawa.

46 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Vector Representation of Real World Objects The representation of real world objects on a map as points, lines and polygons is known as abstraction. In abstraction, the data are structured to be amenable to computer storage/retrieval and manipulation. Abstraction is done based on the requirements of a specific application.

47 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Vector Representation of Real World Objects Building –Represented by polygon in 1:500 cadastral map –Represented by point in 1:50,000 topographic map Road –Represented by polygon in 1:500 cadastral map –Represented by line in 1:50,000 topographic map

48 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Layers and coverages

49 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Geodetic Control Network Digital Elevation Model Orthorectified Image Roads Water Features Political Boundaries

50 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

51 Data Management A collection of non-redundant data which can be shared by different application systems is known as a database. Several layers of geographic data covering the same location are considered a database. When data volumes become large, it is often best to use a database management system (DBMS) to help store, organize, and manage data

52 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Data Management A DBMS is nothing more than computer software for managing a database. There are many different designs of DBMSs, but in GIS the relational design has been the most useful. In the relational design, data are stored conceptually as a collection of tables.

53 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) A DBMS contains Data definition language Data dictionary Data-entry module Data update module Report generator Query language

54 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Data structure Flat file (tabular) - data in a single table (no link between tables). Hierarchical - keys for data retrieval are clearly defined (one-to-many relationship). Network - constructed based on pointers and links between data records (many-to-many relationship). Relational - normalized tables with common redundant fields for relational link (many-to-many relationship).

55 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Hierarchical Data Structure Network System Relational Structure Database Structure

56 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Relational Model Widely used in GIS Commonly used relational DBMS are as follows: –INFO - used in ARC/INFO –EMPRESS - used in System/9 –ORACLE - used in ARC/INFO, GeoVision, etc. –SQL & ACCESS – used in PC-based GIS - Arcview –dBASE - used in pcARC/INFO and other PC-based GIS ARCVIEW.

57 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Relational Model Each record has a set of attributes (fields or items). The range of possible values (domain) is defined for each attribute. Records of each type form a table or relation. In a table, each row is a record or tuple and each column is an attribute or field.

58 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Relational Model

59 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Relation The degree of a relation is the number of attributes in the table. A one-attribute table is a unary relation. A two-attribute table is a binary relation. A n-attribute table is an n-ary relation.

60 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) The degree of a relation.

61 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Key A key of a relation is a subset of attributes with the following properties: –unique identification: the value of a key is unique for each tuple. –non-redundancy: no attribute in the key can be discarded without destroying the key's uniqueness –A primary key is a combination of attributes (fields) whose values uniquely address each record in a relation. –A foreign key is an attribute in one relation (table) which can serve as a primary key into another table.

62 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Key

63 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Foreign KeyPrimary Key

64 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Normalization It is a step-by-step process for converting data structures into a standard form (relational tables) This standard form satisfies the ff. constraints: –Each entry in a table represents one data item (no repeating groups). –All items within each column are of the same kind. –Each column has a unique name. –All rows are unique (no duplicates). –The order of viewing the rows and columns does not affect the semantics of any function using the table.

65 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Normalization Elimination of anomalies in data structure –Update anomalies –Deletion anomalies –Insertion anomalies

66 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Non-normalized relation. Repeating Group What if this group is repeated in 10,000 records?

67 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Non-normalized relation. Changing the erodibility of Loamy Sand from 0.10 to What if there are 10,000 records with Loamy Sand? Update Anomaly

68 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Non-normalized relation. Deleting all records with Loamy Sand. Should the corresponding entries under Land System be also deleted? Deletion Anomaly

69 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Non-normalized relation. Adding the Soil Type Clayey. What if there are 10,000 records having this Soil Type? Insertion Anomaly

70 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Normalized relations. SQL SQL – Standard Query Language

71 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Normalization Major Steps –remove repeating groups of information and move each group out into its own table –look at the information in terms of dependencies (separating out information that is not dependent on the table's Primary Key)

72 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Relational join (using Standard Query Language or SQL)

73 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Degree of Relationship One-to-one relationship –A division has at most one chief and that a chief is a head of at most one division One-to-many relationship –A division has many staff and a staff belongs to at most one division Many-to-many relationship –An employee may be assigned to many projects and a project may have many employees assigned to it

74 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) One-to-one Relationship 1 1

75 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) One-to-many Relationship 1 N

76 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Determine if the following tables are normalized or not. HF_CodeHealth Facility NameClass H0001Marinduque Provincial Hospital HOSPITAL H0001Sta. Cruz Provincial Hospital HOSPITAL R0001Brgy. Morales Brgy. ClinicRHU A. Health_Facility (HF_Code, Health Facility Name, Class) HF_CodeHealth Facility NameEQUIPMENT_CODEEQUIPMENT H0001Marinduque Provincial HospitalE1X-Ray Machine H0001Sta. Cruz Provincial HospitalE2Computer H0001Marinduque Provincial HospitalE2Computer R0001Brgy. Morales Brgy. ClinicE3Air Conditioner B. Health_Facility (HF_Code, Health Facility Name, Equipment_Code, Equipment)

77 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Determine if the following tables are normalized or not. HF_CodeHealth Facility NameCatchment Population H0001Marinduque Provincial Hospital13, H0001Sta. Cruz Provincial Hospital17, R0001Brgy. Morales Brgy. Clinic3,459 C. Health_Facility (HF_Code, Health Facility Name, Catchment Population) HF_CodeHealth Facility NameHW_IDType_of_HW H0001Marinduque Provincial Hospital Doctor H0001Sta. Cruz Provincial Hospital Nurse R0001Brgy. Morales Brgy. Clinic Medical Technologist D. Available_HW (HF_Code, Health Facility Name, HealthWorker_ID, Type_of_HW)

78 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Topology The mathematical procedure for explicitly defining relationships between spatial objects. Topology expresses different types of spatial relationships: (3 major topological concepts) –arcs connect to each other at nodes (connectivity) –arcs that connect to surround an area define a polygon (area definition) –arcs have direction and left and right sides (contiguity)

79 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) An arc in vector GIS.

80 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Example of "built" topology (ARC/INFO)

81 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Data Manipulation GIS data need to undergo transformation before they can be integrated, displayed or analyzed. –same scale, coordinate system, format, etc. A temporary transformation for display purposes or a permanent one required for analysis

82 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) GIS Data Quality As a result of increased mapping capabilities using GIS, two new and very significant mapping issues arose. Map Scale Accuracy/Error

83 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Map scale The major concern in collecting/using spatial data any mapping purpose is scale The ratio of distance on a map over the corresponding distance on the ground. Scale is represented as 1: M or 1/M, where M is called the scale denominator. The Larger the scale, the more the detail described by the map and with higher accuracy. Most of the available GIS data are collected at the common scale such as 1:50000 or 1: It is possible to change the scale in GIS but not advised (important detail are missing) Use of multi scale/multi resolution data US geological survey provides two dataset at two resolution(NIMA-100 m and USGS-30 m).Using above two sources result in different maps.

84 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Map Scale

85 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Data Quality: Accuracy/Error The closeness of measurements or estimates by computation to true values. Accuracy is generally represented by standard deviation of errors, that is difference between measurements and the true value. xi: error in measurements n : number of measurements In GIS, errors result from the map itself, map digitizing and coordinate transformation, which will sum up to about 0.5 mm on the map. Error associated with spatial information can be user errors, measurement error and processing error.

86 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Error: User error Age of data - Reliability decreases with age. Map scale - Non-availability of data on a proper scale or Use of data at different scales Density of observation - Sparsely distributed data set is less reliable Relevance of data - results from indirect or derived data layers as input into GIS Data inaccuracy - Positional, elevation, minimum, mapable unit etc. Inaccuracy of contents - Attributes are erroneously attached Accessibility Military restrictions, inter-agency rivalry, privacy laws, and economic factors may restrict data availability or the level of accuracy in the data

87 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Error: Measurement Error Error associated with variations in data set. Error due to limitations and/or quality of instrument. Error due to limitation in collecting data in the field.(human skill) Error due to natural conditions

88 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Error: Processing Error Processing error includes factors such as precision, interpolation,generalization, data conversion, digitization and other methodical operations. Error due to combining data layers Error due to registration

89 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Data Analysis Perform queries: –Who owns the hospital on the corner? –How far is it between two places? –Where is the site suitable for building new hospital? –How big is the area serviced by the hospital? GIS provides both simple point-and-click query capabilities and sophisticated analysis tools

90 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Data Analysis Perform analysis and modeling to look for patterns and trends and to undertake "what if" scenarios. –If a new factory is built here, how will the residents health be affected? –Given a series of environmental data, where will malaria most likely break out? –If all the factories near a wetland accidentally release chemicals into the river at the same time, how long would it take for a damaging amount of pollutant to enter the wetland reserve?

91 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) GIS Queries Attribute Query Spatial Query Combination of Attribute Query and Spatial Query

92 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Spatial Overlay An operation that merges the features of two coverage layers into a new layer and relationally joins their feature attribute table. When overlay occurs, spatial relationships between objects are updated for the new, combined map. In some circumstances, the result may be information about relationships (new attributes) for the old maps rather than the creation of new objects.

93 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Spatial Analysis GIS operational procedure and analytical tasks that are particularly useful for spatial analysis include: Single layer operations Multi layer operations/ Topological overlay Geometric modeling –Calculating the distance between geographic features –Calculating area, length and perimeter –Geometric buffers. Network analysis Surface analysis Raster/Grid analysis

94 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) GIS Applications in Natural Resource Management Agricultural development Land evaluation analysis Change detection of vegetated areas Analysis of deforestation and associated environmental hazards Monitoring vegetation health Mapping percentage vegetation cover for the management of land Crop acreage and production estimation Wasteland mapping

95 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) References: –Fundamentals of GIS by P.L.N. Raju, Geoinformatics Division Indian Institute of Remote Sensing, Dehra Dun –Bobby Crisostomo, NAMRIA, Philippines

96 Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) THANK YOU


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