Remote Sensing and GIS Integration
(9/25/2019 12:00:00 AM)
Geographic Information Systems (GIS) are a new and
blossoming concept and continue to grow in complexity and utility thanks in large
part to the proceeding and continual development of Remote Sensing. Remote Sensing plays a large role in the
enhancement of any GIS, and in most cases, allows data to become much more
relatable and useful for anyone. A GIS
receives much of the data for its built-in layers from Remote Sensing platforms
such as satellites, radars and airplanes.
Passive sensors contribute to imagery and data for land cover mapping,
change detection, snow monitoring, thermal changes and terrain modeling. Active sensors contribute heavily to data for
extremely accurate terrain models known as Digital Elevation Models
(DEMs). These large quantities of data
can be geo-referenced and integrated into one large GIS, allowing a user to
access a powerful amount of information at one time with relative ease. And as remote sensing technology continues to
increase in resolution and power, the data base will enlarge and increase the
potential power of users of a Geographic Information System.
Remote Sensing is the process of obtaining information
about land, water, or an object without any contact between the sensor and the
subject of analysis. Satellite and
digital imagery play an important role in remote sensing and GIS. There are two types of remote sensors, passive
and active sensors. Passive sensors
measure reflected electromagnetic radiation from the sun or are emitted from
the earth’s surface (photography and satellite imagery). Active sensors emit their own electromagnetic
radiation and measure the response (radar, sonar, and LIDAR). See the figure below for an example.
Remote sensing has many advantages over traditional
data collection. It provides a regional
view over a large area, it provides repetitive looks at the same area over
time, sensors see a broader portion of the light spectrum than the human eye,
it provides geo-referenced digital data that is used a geographic information
system, it allows retrieval of data for regions difficult to reach such as
oceans or hazardous terrain, and it can collect large amounts of data in a
short period of time.
Resolution is a fundamental term involved with remote
sensing and there are four different types: Spatial, Spectral, Temporal, and
Radiometric. The earth’s surface area is
represented by pixels of an image which is known as spatial resolution. A large area covered by a pixel is known as
low spatial resolution and a small area covered by a pixel is known as high
spatial resolution. Spectral resolution
is the ability to resolve spectral features and bands into their separate
components. More number of bands in a
specified bandwidth means higher spectral resolution and vice versa. The position, number, and width of spectral bands
determines the degree to which individual targets can be discriminated in an
image. Temporal resolution is the
frequency at which images are recorded or captured in a specified place on the
earth’s surface. The more frequently it
is capture, the better or finer the temporal resolution is said to be. For example, a sensor that captures an image
of agricultural land twice a day has better temporal resolution than a sensor
that only captures that same image once a week.
Radiometric resolution is the sensitivity of the sensor to the magnitude
of the received electromagnetic energy.
The finer the radiometric resolution of a sensor, it is more sensitive
in detecting small differences in reflected or emitted energy.
Some of the popular satellites include: NOAA Advanced
Very High-Resolution Radiometer (AVHRR) with 1100-meter resolution,
Geostationary Operational Environmental Satellites (GOES) with 700-meter
resolution, Moderate Resolution Imaging Spectroradiometer (MODIS) with
resolutions of 250, 500, and 1000 meters, Landsat Thematic Mapper with a
resolution of 30 meters, IKONOS with a resolution of 1 and 4 meters, and
Quickbird with a resolution of 0.6 meters.
Information System (GIS)
A geographic information system is a system designed
to capture, store, manipulate, analyze, manage, and present spatial or
geographic data. Remote sensing provides
numerous sources of spatial data for use in a GIS. Some examples include: Imagery as base data
for GIS mapping, Land use and land cover mapping, Biophysical phenomena,
feature extraction from satellite imagery, surface elevation determination, and
landscape change. GIS layers such as
roads and rivers can be easily seen from aerial and satellite photos, this
information is digitized, separated into layers, and integrated into a GIS. Features can also be automatically extracted
using computer segmentation algorithms and the resulting data can be imported
into a GIS for further display and analysis.
Geospatial Tools and Techniques
Basic geoprocessing can be used in Geographic
Information Systems to answer a wide variety of mapping questions. Geospatial
modeling is used to predict values in locations or in different timeframes for
which data are not directly available. Map algebra can be used to apply
mathematical operations to multiple inputs for modeling. Inputs are often
weighted to reflect or test the impact of different variables on a given model.
Environmental carrying capacity is often a derived from multiple models
representing each contributing factor to a system’s carrying capacity.
Geospatial change analysis may also be accomplished with basic map algebra
showing the difference between one or more inputs. Building a conceptual
flowchart or process model along with custom scripting is often used to
facilitate iterative modeling analyses allowing researchers and managers to
test multiple scenarios for varying conditions.
Many common mapping questions can be answered in a GIS
with basic geoprocessing. Frequently used geoprocessing techniques include
intersections of locations, linear features and areas to show spatial
coincidence as well as proximity analyses such as buffering, nearest neighbor
determinations. Additional basic geoprocessing techniques are used to combine,
summarize or simplify geospatial data. Processes such as merge, union and dissolve may be used to understand data
in different ways or viewpoints or to prepare data for inclusion in additional
nearest neighbor and cluster analyses provide standard
proximity analysis in a GIS. These operations help answer the basic question of
“what’s around” and can dynamically show spatial relationships.
The Environmental Systems Research Institute (Esri)
provides an overview and details on basic geoprocessing at http://pro.arcgis.com/en/pro-app/help/analysis/geoprocessing/basics/what-is-geoprocessing-.htm.
Modeling is a technique that predicts values and
attributes for locations that do not otherwise have data. In other words,
modeling can fill in the gaps in geospatial knowledge or predict values at
future dates when conditions may change. Modeling is also used to predict
future changes or conditions in space such as predicting where certain fish
species may occur and how they travel through regions or areas. Many of the
techniques outlined below can be performed using most GIS packages.
Additionally, add-on software specifically aimed at geospatial modeling is
often available. The Geospatial Modeling Environment is one such software
add-on available for free at http://www.spatialecology.com/gme/.
Interpolation and extrapolation are basic modeling
techniques used to model geospatial data. Starting with known values from as
many locations as can be measured, extrapolation allows for calculated guesses
at values everywhere (Figure 2). Interpolation performs the same operation but
is programmatically limited to the geospatial extent of the starting, known
locations (i.e., interpolation does not calculate values far and wide across
Figure 2. Starting with values and locations in the left diagram, a
value is calculated for all locations within the area via interpolation.
Because extrapolation/interpolation provide values in
all locations, results are best expressed as raster data. The size of the grid
cells may be set by the GIS analyst, but most GIS software use standard
algorithms to determine cell sizes based on the spacing of the initial inputs.
It is important to note that interpolation/extrapolation should only be
performed on datasets where a value for the parameter being measured can
reasonably be expected at any location. For example, salinity values in an
estuary can be measured at any place throughout estuary, therefore, it is
acceptable to model values in all locations.
Interpolation and extrapolation help fill in spatial
gaps and are often used to develop input data layers for additional analysis.
Map algebra is a technique used, for example, to add GIS layers--perhaps
resulting from interpolation--together to derive a predictive result (Figure
3). Any mathematical operation can be used on the input layers. Depending on
the phenomenon being modeled, mathematical formulas can be simple or complex.
The section on Habitat Mapping and
Modeling provides some examples of more complex map algebra operations.
Figure 3. Example of input layers of salinity, disease incidence and
water quality combined to model predicted shrimp across an area.
A common technique used in map algebra is
weighting--applying a multiplier or other modifier to a layer to control the
impact of that particular layer on the modeled results. For example, the
salinity layer shown in Figure 3 may be known to be more important to shrimp
yield than the other inputs and so may be multiplied by some factor before the
overall model operation is performed. Different weights can be applied until
the overall results conform with other known maps if available and related to
the phenomenon being modeled.
Using map algebra, perhaps with weighted overlaying,
“hotspots” -- areas that are important or high value or “suitable” based on the
weighting and/or algorithm applied. A “suitability” map can be thought of as a
map showing areas where something may be expected to occur. The term is often
used to refer to habitat for a given species (e.g., areas suitable as deer
habitat). Suitability mapping is essentially “hot spot” mapping. In the case of
fish disease outbreaks, all the conditions that would allow for disease could be
mapped and added together to show the result as a hotspot map. This would be a
“suitability for disease” map (or perhaps better said as a “areas with high
potential for disease” map).
Geospatial modeling is also used in
determining carrying capacity for habitats or ecosystems. “Carrying capacity”
in this sense refers to population sizes in numbers of individuals (or possibly
spatial extent in the case of plant species). The carrying capacity of an area
is often determined by applying the methods such as hot spotting, weighted
overlays, etc. to numerous factors or thematic conditions affecting carrying
capacity and then combining these using map algebra. An extensive carrying
capacity study may therefore consist of map algebra performed on model results
which are derived from map algebra in the first place.
If layer inputs include time variables,
basic map algebra may be used to determine the difference between two more
layers to show a “difference map” or, in essence, before and after conditions.
Documenting change over time and space can then be used as a means for
examining what drove the change and how those drivers might continue to cause
change. Predicting change by examining how things have changed so far underpins
basic research into climate and resulting environmental change.
Routing, or least-cost path modeling, is another
powerful technique that can be used to predict, for example, the paths
different fish species may travel. Using modeling as described, a least-cost
data layer can be developed that assigns values to each grid cell representing
the “cost” of moving into that cell. A starting point is selected, and the GIS
process checks the value of each neighboring grid cell and advances the route
to the cell with the lowest, or least cost, value. A simple least-cost model is
shown in Figure 4. A routing model could also be based on previously modeled
habitat suitability and ran such that the path moves to the neighboring cell
with the most suitable habitat. Rules can be set where neighboring cells have
equal values. For example, in the case of a tie, the path may choose the cell
to the west or south. Routing may also be performed on a vector data layer and
is often used to model the best routes for delivery vehicles in a road network.
Figure 4. Simple
least-cost path determination in a raster data layer.
Building and Scripting
Geoprocessing and modeling are made easier with model
building and scripting. “Model building” in this sense is the creation within a
GIS of a process flow chart that conceptually maps the modeling process
including data inputs. Model building and scripting allow for more easily
repeated processing and analysis to test different inputs and scenarios (Figure
5). Scripting allows for customs algorithms or other data manipulations unique
to a given scenario.
Figure 5. Example of geoprocessing model. Blue ovals represent input
data layers. Green ovals represent output layers resulting from processes noted
in the rectangles. In some cases, processes are custom scripts called and
executed by the model.
VIFEP (USAID workshop )