REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM (GIS) IN FISHERIES AND AQUACULTURE
(8/22/2019 12:00:00 AM)
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3.1.
Abstract
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.
Hardware
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.
Software
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.
Data
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.
Methods
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.
People
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.
1.1.1.
Abstract
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 (https://landcover.usgs.gov/) 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
(https://nowcoast.noaa.gov/), 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 (https://worldview.earthdata.nasa.gov/) 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 (http://geodata.grid.unep.ch/) 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 (http://portal.mrcmekong.org/index), for example,
provides historical and near real-time hydrological data on the Mekong River
and its main tributaries. SERVIR-Mekong
(https://servir.adpc.net/) 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
(https://opendevelopmentmekong.net/) 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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