(8/22/2019 12:00:00 AM)

Ø Geographic Information Systems and Remote Sensing - Fundamentals and Trends


A Geographic Information System (GIS) is a system for deriving location intelligence from geographic data. A GIS is comprised of five components: hardware, software, data, methods and people. Software choices for GIS are available both commercially and free via open source communities. In essence, GIS data renders real-world locations into either vector or raster data. With “cloud” computing, geospatial data are increasingly shared as data services and used in web mapping and mobile computing environments. The spread of mobile devices with Geographic Positioning Systems (GPS) represent a new, important source of data for GIS. Additionally, drones are becoming an important method for data collection. Advances in web mapping have led to the integration of GIS data in multimedia web browser applications that are used as communication tools.


Components of a GIS

A Geographic Information Systems (GIS) is essentially a computer system for housing and manipulating geographic data. A GIS can be thought of as a map with data tied to it. In a GIS, a map of wetlands, for example, will not only remark wetland areas but contain basic information about each wetland such as plant species, soil characteristics and basic water quality associated with each wetland area. However, a fully implemented GIS is more than a computer system. A complete GIS comprises five key elements: Hardware, Software, Data, Methods and People.



The necessary hardware for a Geographic Information System has evolved from networked Unix-based systems to personal computers connected via the internet. Additional needed hardware includes Geographic Positioning Systems (GPS) for data collection, large format printers (plotters) for hard copy map production a large format scanner for creating data from old, printed maps. Remotely piloted drones are also becoming a common part of the GIS toolkit. In addition to a personal computer, a GIS office often also houses web and geodatabase servers to which the personal computers running desktop applications connect to share data.


Geographic Information System software is computer intensive. Today’s typical GIS workstation will include a high-end processor, at least 16GB of RAM and high-end video card to handle complex graphics. Because a GIS practitioner is often examining data in both tabular and map formats, as well as navigating file management structures, at least two large monitors are recommended; one for displaying file lists and/or data tables and another for displaying maps.



A fully deployed GIS requires several types of software. Chief among them is the core desktop software used to manipulate geospatial data. Additional software may include specific database software designed to house and serve geo data, software for data collection such as for a GPS or other mobile solution and web mapping packages aimed at creating interactive web mapping sites.

The industry leader for core GIS software is the Environmental Systems Research Institute (Esri), ArcGIS suite of applications. The Esri core packages of ArcMap and ArcCatalog are available for purchase and access is controlled via software license management. Purchasing Esri products includes technical support from the company which can be augmented by a worldwide community using similar software. The costs, however, have led some organizations and countries to pursue open source alternatives.

A wide variety of powerful, cost-free, open source GIS software solutions are available. While open source applications do not typically have the technical support backing of an established company, most have robust user communities that can offer a great degree of support. Some examples of open source GIS software include QGIS, LibreOffice and GeoServer. Open source solutions also exist for the database, mobile data and web mapping software that complement core GIS applications.


Web mapping software that creates many of the functions of a GIS within common web browsers has become a standard part of a GIS office. Web mapping sites such as Google and Bing maps have become ubiquitous in many offices making basic interactive mapping accessible to many. Web maps can be further developed through custom programming to bring more tools, functions and data into a web browser environment. For the GIS industry, web mapping has become a critical derivative of the internet and “cloud” computing.


“The Cloud”

The advent of remotely accessible computers that can host data and run applications-- “The Cloud” -- has fundamentally changed how GIS operates. Cloud computing can be considered as both a hardware and software component to GIS. The remote servers and related infrastructure to which local computers can connect is hardware but the burden of maintaining that hardware is often removed from the local GIS office. Software that local machine can run on the remote servers is also freeing local GIS teams from having to maintain software packages locally. As with other GIS software, cloud services are available for purchase or free via open source communities.

Perhaps the biggest impact of the cloud on GIS is with data. Now that data can be hosted in cloud environments, GIS managers can share live copies of their data to a broad base of users. The cloud, in many cases, makes it unnecessary for GIS users to download data to their local workstations. This helps minimize problems with data versioning and ensures that the most up- to-date data are readily available. Some common examples of cloud computing in GIS include Esri’s ArcGIS Online, GeoServer and OpenLayers.



Geospatial data is categorized into two principle types: vector and raster. Vector geospatial data are those consisting of any locations, linear features or areas (points, lines or polygons). Vector data attempts to capture specific geometries and locations for mapped features. In contrast, raster geospatial data renders feature into a series of grid cells--the size of which can vary for any one dataset. By generalizing features, raster data often allow for powerful data modeling and analysis of very large datasets. Figure 1 demonstrates the vector and raster data types.

Figure 1. Vector data represented on the left is rendered as raster in the righthand diagram. The accuracy of features in the raster data depends on the cell size used to render from the vector data.


Common vector data types include digitized maps, GPS data or any data with specific latitude and longitudes. Common raster data includes aerial and satellite imagery, light detection and ranging (LiDAR) and digital elevation models (DEM). For both vector and raster data, a wide variety of data file types exist including shapefiles, file geodatabases, GRIDs, JPEG2000 and others.

Increasingly in today’s world, “Big Data” are also becoming an important data source for GIS work. Big Data refers to massive amounts of data being collected for example by the millions of mobile phones that automatically track location. How to collect, manage and analyze big data has become yet another challenge to GIS teams.


Regardless of the source and type of data, key characteristics about data such as how and when it was collected or created, how accurate the generally are and how it is formatted should be recorded as metadata. The creation of metadata documents or records is an important method in a fully functioning GIS.



The methods component of a GIS is not only data manipulation and analysis but also the procedures surrounding the whole data lifecycle from collection to deriving information from analysis to storing and sharing. Critical to a functioning GIS is the development of proper documentation of data (metadata) and the business procedures comprising the data lifecycle. A long-term GIS strategy will also include methods for new data creation and monitoring of previously collected data. This helps ensure that the most current information is available for answering questions and supporting decision making.



It goes without saying that no GIS would exist without the people to operate it. In addition to GIS specialists and data collection professionals, it is important for managers, communications people and scientists to embrace and understand the fundamentals of a GIS. This allows for more effective and appropriate use of the location intelligence derived from geospatial data.


Trends in GIS

With the advent and spread of mobile computing coupled with the internet and cloud services for data and software, GIS has evolved from a technology accessible only to specialized jobs to a readily available tool for many. The new paradigm of centrally managed data served out for use in a wide variety of applications has greatly improved the dispersal and use of geospatial data. Such data are referred to as “data services”. Serving data has become a primary means of sharing location intelligence and maps in a controlled manner. Services can now be used on mobile devices and in turn, location information recorded by mobiles has become an important new source of data.

New data are also increasingly collected via drones. Drones are available in a wide range of sizes and can carry a diverse set of sensing instruments such as high-resolution cameras, infrared sensors and LiDAR. Airborne drones have effectively overcome what was once a significant logistical and technical problem for targeted data collection efforts.

Developments in web mapping technology are allowing sophisticated geospatial analysis and mapping to be performed web browsers and mobile devices often times negating the need for installed software on user computers. Mapping can now be integrated in a website with text and other media to create powerful communication tools. Esri’s “Story Map” product simplifies creation of such sites that blend GIS data with multimedia and is increasingly used to facilitate communications.

Ø Sources of Spatial Data


Spatial data are composed of primary data (personally collected) and secondary data (collected by others).  These data can be used in multiple analytical contexts.  Publicly available spatial data is becoming more readily available with advancing technologies and often using secondary data is more cost-effective and efficient than building primary data


Primary Data

Primary data are collected directly by a researcher.  This data can be collected specifically for a given project, such as scanned or digitized data, Global Positioning System (GPS) data, or remote sensing data.  Digitized data is generally older reference material such as a paper map that has been loaded as a raster file.  The features on the map can be converted into points, lines, and polygons.  GPS data provides geolocation and a time stamp for an unobstructed line of sight to four or more GPS satellites.  GPS data can be personally collected to answer specific questions.  Remote sensing data can detect and monitor physical characteristics using cameras, satellites, and sonar.  Examples of remote sensing data include topography, temperature, and chlorophyll (for more information, see remote sensing section).   


Secondary Data

Secondary data is collected by someone other than the research who intends to use it.  This data can be primary data collected by a colleague or it can be open source data available publicly.  Many secondary data sources provide free, publicly available data.


Global Secondary Data

Globally available secondary data sources relevant to fisheries and aquaculture include: the National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), United Nations Environment Programme (UNEP), and U.S. Geological Survey (USGS).  For example, The USGS Land Cover Institute ( provides land use data globally.  These data are often used in applications including biodiversity conservation, water quality assessments, phenology, and land cover change over time.  NowCOAST, through the NOAA Web Mapping Portal (, is an example of forecasted data.  Its applications include tide and weather prediction, harmful algal bloom forecasts (for more information, see harmful algal bloom section), and sea surface temperature.  For real-time data, NASA Worldview ( updates every three hours for applications like air quality measurements, flood monitoring, and tropical storm watches.  More specifically for environmental data, the UNEP Environmental Data Explorer ( contains over 500 different variables such as freshwater, populations, forests, emissions, climate, health, and GDP.

Regional Data Sources

Regionally available secondary data sources relevant to fisheries and aquaculture include: the Mekong River Commission, SERVIR - Mekong, and a new hosting element, Open Development Mekong.  The Mekong River Commission Data Portal (, for example, provides historical and near real-time hydrological data on the Mekong River and its main tributaries.  SERVIR-Mekong ( is a new collaboration between the U.S. Agency for International Development (USAID), NASA, and Asian Disaster Preparedness Center (ADPC) which aims to “work with partners to create and/or share geospatial datasets that meet specific user needs.”  Currently available datasets include rice farming suitability, protected areas, and seasonal surface water; other datasets relevant to fisheries and aquaculture may be developed through future SERVIR-Mekong projects.  Additionally, the Open Development Mekong ( is a new data crowdsourcing platform specific to the Mekong region seeking “to expand publicly available resources on topics related to the Lower Mekong.”  Topic areas include agriculture and fisheries, energy, environment and natural resources, land, and populations and censuses.


Example: Climate Change Analyses

As a regional example of the use of spatial data, the International Water Management Institute (IWMI) was able to compare temperature and rainfall projections for all of Southeast Asia.  Using the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) storylines A2 (high population growth and slower economic growth and technological change) and B2 (moderate population growth and economic development with less rapid and more diverse technological change), IWMI projected increases in annual temperature and variation in annual rainfall (some increases and some decreases) across the region using the Providing Regional Climate for Impact Studies (PRECIS) regional climate model over the period of 1960-2049.  Wet seasons are mostly projected to be wetter (more cumulative rainfall depth), with greater potential for flooding.  Additionally, despite greater rainfall in the wet season, drought days are likely to increase due to higher temperatures and increased evapotranspiration.  For more information, please consult IWMI Research Report 136.


VIFEP  (Document for USAID workshop)


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