CHAPTER 2
CARTOGRAPHY AND REMOTE SENSING
Cartography is the art and science of creating maps. It involves gathering of geographical
information, storage, processing, and editing of this information, and the presentation of the
data in map form. The way this information is gathered and processed evolved with time and in
the last few decades the remote sensing techniques are used to gather this information and
digital image processing techniques are used to analyze and interpret.
Section 2.1 explains the cartography and its applications and Section 2.2 deals with remote
sensing and applications. While Section 2.3 describes satellite sensors and Section 2.4
concludes the chapter.
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2.1 CARTOGRAPHY AND INFLUENCE OF TECHNOLOGY
Cartography has been practiced from the early civilizations. For many centuries its highest
application was the production of hand-drawn flat maps and charts assembled from
information collected visually by explorers. Hence, these maps were not as accurate as modern
maps, but demonstrate the level of knowledge and thought during those the times. Though the
exact date of the first map is not known, the ancient Romans and Egyptians were the first to
create maps for military purpose and to delineate the kingdoms. Map-making process has since
evolved with technology and time [Albertz (1995)].
Major technological advances started in map-making with the invention of the printing press in
the 12th century in China and 15th century in Europe. The interplay between mapping and
exploration can be seen in the rapidly increasing detail in the maps made by Spanish
adventurers in North America as they explored the continent between the 15th and 17th
centuries. The next major advance in map-making technology came in the mid-1800s with the
development of photography. Photography enabled cartographers to capture detailed
data.
The invention of Photogrammetry i.e., using special cameras and image projectors to translate
photographs into accurate survey maps was the next advancement in the field of cartography
[Albertz et al., (1995)].
The development of public infrastructure in and around major cities began in the late 1800s
and early 1900s and the importance of cartography grew manifold. The growing infrastructure,
especially, the construction of transport systems, required extensive planning and mapping by
civil authorities.
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Although cartographers found that recording data with photographs was relatively easy, the
only way to get an overview of the landscape for photogrammetric purposes was to climb a
mountain, an observation tower or use a hot air balloon. Technological advances in the early
20th century helped cartography make rapid progress. The greatest leap came with the
invention of the aeroplane and aerial photography. Detailed high-altitude photographs could be
taken, making it easy to render accurate orthogonal maps of relatively great detail.
In the last 40 years, remote sensing (refer to section2.2) has enabled modern cartographers to
chart the depths of the ocean or the frontiers of outer space. The space programme has taken
cameras into orbit, providing vast image collections of Earth, other planets, and even other
galaxies. Modern computers can store and transmit large amounts of mapping data, and then
collate the assembly of such data to produce maps using sophisticated digitizers, plotting
machines, and electronic typography.
2.2 REMOTE SENSING
Remote Sensing, in a broad sense, is the measuring or acquiring the information of some
property of an object or phenomenon, by a recording device that is not in physical contact with
the object or phenomenon under study. The utilization of a device from distance (an aircraft or
satellite) and the display of information pertaining to the environment, such as measurements
of force fields, electromagnetic radiation, or acoustic energy are used in the remote sensing.
The technique employs such devices as the camera, lasers, and radio frequency receivers, radar
systems, sonar, seismographs, gravimeters, magnetometers, and scintillation counters.
Remote sensing including aerial photography has been recognized as a valuable tool for
viewing, analyzing, characterizing, and making decisions about the environment. In the past
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few decades, remote sensing technology has advanced on three fronts: 1) from predominantly
military uses to a variety of environmental analysis applications that relate to land, ocean, and
atmosphere; 2) from (analog) photographic systems to sensors that convert energy from many
parts of the electromagnetic spectrum to electronic signals; and 3) from aircraft to satellite
platforms.
Satellite remote sensing is now being used to observe, measure, and record the electromagnetic
radiation reflected or emitted by the Earth and its environment for subsequent analysis and
extraction of information. Remote sensing aids in extensive surveys that are made from high
altitudes to show the urban development, rural development, mountain areas, desserts, etc
which help the cartographers. High-resolution satellite cameras located at altitudes of several
hundred kilometres can record details as small as a few metres on the surface of the Earth.
Satellites such as LANDSAT, IKONOS, and QuickBird etc sweep the globe with continuous
scans to provide detailed up-to-date maps of nearly the entire Earth. Satellite imagery is also
used to create weather maps. This vast amount of data needs to be processed by the computers
with human vision understanding to generate the information required automatically and
accurately.
2.2.1 COMPARISON OF RS WITH MAPS, GIS, AERIAL PHOTOGRAMMETRY
Satellite images obtained through RS Vs Maps:
According to The International
Cartographic Union, a map is “a conventionalized image representing selected features or
characteristics of geographical reality, designed for use when spatial relationships are of
primary importance.”
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A map is subjective; a cartographer can decide what to put on it, and how to represent it. A
remote sensing image in contrast, is an objective recording of the electromagnetic signal
reaching the sensor. While a map is a projection of the earth on paper, without any relief
displacements, while in a remote sensing image has both relief displacements and geometric
distortions.
Remote Sensing Vs GIS: Geographic Information System is software that enables the
collection of spatial data from different sources, relating spatial and tabular data, performing
tabular and spatial analysis and symbolize and design the layout of a map. A GIS software may
handle both vector and raster data. Remote Sensing data belongs to the raster type, and usually
requires special data manipulation procedures that regular GIS do not offer. After a remotely
sensed image is analyzed, its results are used for GIS applications or put into the database.
Remote Sensing Vs Aerial Photography / Photogrammetry: The application of both the
systems is the same i.e., to gather data about the upper surface of the Earth. Aerial photos are
taken by an analog instrument; a film of a (photogrammetric) camera then scanned to be
transformed to digital media and the Remote Sensing data is usually gathered by a digital CCD
camera.
The difference between remote sensing and aerial photography are as given below:

An Aerial photograph is a central projection, with the whole picture taken at one
instance. A Remote Sensing image is created line after line i.e., raster scan, therefore
the geometrical correction is much more complex.

Aerial photos usually gather data only in the visible spectrum, while Remote Sensing
sensors can be designed to measure radiation all along the Electromagnetic spectrum.
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
Aerial photos are usually taken from aero planes, Remote Sensing images are taken
from satellites.

Both systems are affected by atmospheric disturbances.

In Photogrammetry the main efforts are dedicated for the accurate creation of a 3D
model, in order to plot with high level of accuracy the location and boundaries of
objects, and to create a Digital Elevation Model, by applying sophisticated geometric
corrections.

In Remote Sensing the main efforts are dedicated for the analysis of the incoming
electromagnetic spectrum, using atmospheric corrections, sophisticated statistical
methods for classification of the pixels to different categories, and analysing the data
according to known physical processes that affect the light as it moves in space and
interacts with objects.

Remote Sensing images are very useful for tracking phenomena on regional,
continental and even global scale, using the fact that satellites cover in each image a
wide area and taking images all the time (whether fixed above a certain point, or
“revisiting” the same place after few days as per requirement).

Remote Sensing images are available since the early 1970’s. Aerial photos provide a
longer time span for landscape change detection (the regular coverage of Israel by
Aerial photos started in 1944/5, for example, with many Aerial photos taken also during
World War 1).

Remote Sensing images are more difficult to process, and require trained personnel,
while aerial photographs can be interpreted more easily.
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2.2.2 APPLICATIONS OF REMOTE SENSING
Each application itself has specific demands, for spectral resolution, spatial resolution, and
temporal resolution. There are many applications for Remote Sensing, in different fields, a few
of which are as described below.

Agriculture: Agricultural applications of remote sensing include crop type
classification, crop condition assessment, crop yield estimation, mapping of soil
characteristics, mapping of soil management practices, compliance monitoring (farming
practices).

Forestry: Forestry applications of remote sensing include Reconnaissance mapping,
Commercial forestry, Environmental monitoring.

Geology: Geological applications of remote sensing include surficial deposits / bedrock
mapping litho-logical mapping, structural mapping, sand and gravel (aggregate)
exploration/ exploitation, mineral exploration, hydrocarbon exploration, environmental
geology, geo-botany, baseline infrastructure, sedimentation mapping and monitoring,
event mapping and monitoring, geo-hazard mapping, planetary mapping.

Hydrology : Hydrology applications include the study water on the Earth's surface,
whether flowing above ground, frozen in ice or snow, or retained by soil Examples of
hydrological applications include, wetlands mapping and monitoring, soil moisture
estimation, snow peak monitoring / delineation of extent, measuring snow thickness,
determining snow-water equivalent, river and lake ice monitoring, flood mapping and
monitoring, glacier dynamics monitoring (surges, ablation), river /delta change
detection, drainage basin mapping and watershed modelling, irrigation canal leakage
detection, irrigation scheduling.
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
Sea Ice: the study includes sea ice information and applications include, ice
concentration, ice type / age /motion, iceberg detection and tracking, surface
topography, tactical identification of leads: navigation: safe shipping routes/rescue, ice
condition (state of decay), historical ice and iceberg conditions and dynamics for
planning purposes, wildlife habitat, pollution monitoring, meteorological / global
change research.

Land Cover & Land Use: Land use applications of remote sensing include natural
resource management, wildlife habitat protection, baseline mapping for GIS input,
urban expansion / encroachment, routing and logistics planning for seismic /
exploration / resource extraction activities, damage delineation (tornadoes, flooding,
volcanic, seismic, fire), legal boundaries for tax and property evaluation, target
detection - identification of landing strips, roads, clearings, bridges, land/water
interface.

Mapping: Mapping applications of remote sensing include planimetry, Digital
Elevation Models (DEM's), Baseline thematic mapping / topographic mapping.

Oceans & Coastal Monitoring: Ocean applications of remote sensing include ocean
pattern identification, storm forecasting, fish stock and marine mammal assessment, oil
spill, shipping.
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2.3 SATELLITE SENSORS
2.3.1. HISTORY OF SATELLITE SENSORS
Since the early 1960s, numerous satellite sensors have been launched into orbit to observe and
monitor the Earth and its environment. Most early satellite sensors acquired data for
meteorological purposes. The advent of earth resources satellite sensors (those with a primary
objective of mapping and monitoring land cover) occurred when the first Landsat satellite was
launched in July 1972. The remote sensing satellites (sensors) like the Landsat, SPOT, IRS,
AVHRR, IKONOS, Quickbird, FORMOSAT, CARTOSAT, Worldview, ALOS, and GeoEye
have been deployed for data acquisition in different GIS applications is given in Table 2.1.
2.3.2. Satellite Sensor Characteristics
Spatial resolution: Spatial resolution refers to the smallest possible feature that can be
detected. Spatial resolution of passive sensors depends primarily on their Instantaneous Field
of View (IFOV).
Pixel: Pixel is the smallest unit of an image. Image pixels are normally square and represent a
certain area on an image.
Scale: The ratio of distance on an image or map, to actual ground distance is referred to as
scale.
Spectral / Radiometric resolution: Every time an image is acquired on film or by a sensor,
its sensitivity to the magnitude of the electromagnetic energy determines the radiometric
resolution. The radiometric resolution of an imaging system describes its ability to discriminate
very slight differences in energy. The finer the radiometric resolution of a sensor the more
sensitive it is to detecting small differences in reflected or emitted energy.
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Table 2.1 History of satellite sensors and applications
Sr.
no
1
2
3
4
5
6
7
8
9
10
11
12
13
Satellite,
launch date,
altitude(km
)
LANDSAT
ETM +,
April 1999,
705
IKONOS,
September
1999, 681
Spatial
Resolution
Applications
15m (Pan)
30m (MS)
QuickBird,
October
2001, 482
CBERS -2,
October
2001, 778
SPOT-5,
May 2002,
822
FORMOSA
T-2, May
2004, 888
65 cm (Pan)
2.44m (MS)
CARTOSAT
-1, May
2005,
618
CARTOSAT
-2, January
2007, 630
ALOS,
January
2006, 692
2.5m
Global change research, agriculture, forestry, geology, resource management,
geography, mapping, hydrology, and oceanography, agricultural development,
deforestation, desertification, natural disasters, urbanization, and the development and
degradation of water resources. (revisit time: 16 days)
Urban and rural mapping of natural resources and of natural disasters, tax mapping,
agriculture and forestry analysis, mining, engineering, construction, and change
detection. Mining, Oil and Gas Exploration and many other applications. (revisit time:
3 days)
Imagery helps in detecting changes in land usage agricultural and forest climates, oil
and gas exploration, engineering and construction and environmental studies.
(revisit time:1-3.5 days)
Deforestation, fire control in the Amazon region, water resources monitoring, urban
growth, soil occupation, education, hydrological basin monitoring by the ANA and
SIVAM platform networks, in Brazilian river, rain data. (revisit time: 5 days)
Urban and rural planning, oil and gas exploration, and natural disaster management,
3D terrain modeling and computer environments, such as flight simulator databases,
pipeline corridors, and mobile phone network planning.(revisit time: 2-3 days)
Urban land utilization monitoring, environment monitoring and planning, farm crop
management, forest management and monitoring, disaster area evaluation, ecological
environment and resources research, tourism and traveling, defense mapping, coastal
management. (revisit time: daily)
Large scale mapping applications, stimulate newer applications in the urban and rural
development, land and water resources management, disaster assessment, land cover
change detection, relief planning and management, environmental impact assessment
and GIS applications.
( revisit time: 15 days and 4 days respectively)
RapidEye,
August
2008, 630
Geoeye-1,
681
Worldview2, October
2009, 770
ASTER,
December,
2009, 705
Resourcesat2, April,
2011, 816
0.82 (Pan)
3.2 m (MS)
20m (Pan)
and & 260m
(MS)
2.5 to 5m
(Pan) and
10m (MS)
2m (Pan)
and 8m
(MS)
<0.8 m
10m
( AVNIR-2)
2.5m
(PRISM)
6.5m
0.41 (Pan)
and
1.65 ( MS)
0.46 m (Pan)
and
1.8m (MS)
15m(VNIR)
30m (SWIR)
90m (TIR)
5.8m(Pan)
5.8m(MS)
Cartography, disaster monitoring, natural resource surveys technology development. (
revisit time: 46 days)
Imagery used in agriculture, forestry, insurance, exploration, power and
communication, government, cartography, visualization. (revisit time: daily- off
nadir, 5.5- nadir)
Collection for change detection; surveillance; habitat monitoring, regional, large-area
mapping; general GIS applications, base mapping, land use, economic development,
high positional accuracy for urban applications detailed urban analysis, cadastral and
infrastructure mapping for transportation, infrastructure, and utilities planning, DEM
creation for flood plain analysis, engineering grade quality for transportation,
infrastructure and utilities planning, and economic development.(revisit time <3 days)
Mapping and monitoring applications, land-use planning, disaster relief, exploration,
defense and intelligence, and visualization and simulation environments.(revisit time:
1.1 days)
Vegetation, ecosystem dynamics, hazard monitoring, geology, soils, land surface
climatology, hydrology, land cover change, the generation of digital elevation models
(DEMs). ( revisit time: 16 days)
The high-resolution data are useful for applications such as urban planning and
mapping, while the average resolution is used for vegetation discrimination, land
mapping, and natural resources management. ( revisit time <5 days)
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Digital resolution: It is the number of bits comprising each digital sample. This range
corresponds to the number of bits used for coding numbers in binary format. Each bit records
an exponent of power 2. The maximum number of brightness levels available depends on the
number of bits used in representing the energy recorded.
Spectral range: There are general spectral ranges that are in common use, each to first order
controlled by detector technology:
Visible near infrared (VNIR)
0.4 to 1.0 μm
Short wave infrared (SWIR)
1.0 to 2.5 μm
Spectral bandwidth: Spectral bandwidth is the width of an individual spectral channel in the
spectrometer.
Spectral sampling: Spectral sampling is the distance in wavelength between the spectral
bandpass profiles for each channel in the spectrometer as a function of wavelength.
Signal-to-noise ratio: A spectrometer must measure the spectrum with enough precision to
record details in the spectrum. The signal-to-noise ratio (S/N) required to solve a particular
problem will depend on the strength of the spectral features under study. The S/N is dependent
on the detector sensitivity, the spectral bandwidth, and intensity of the light reflected or
emitted from the surface being measured.
Temporal Resolution: The absolute temporal resolution of a remote sensing system to image
the exact same area at the same viewing angle a second time is equal to this period. However,
the actual temporal resolution of a sensor depends on a variety of factors, including the
satellite/sensor capabilities, the swath overlap, and latitude.
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2.3.3. SATELLITE SENSOR DATA FORMAT
Remote Sensing imagery data is organized in a matrix (raster) form. The columns are termed
samples, and the rows lines. As an image scene contains information from several bands, there
can be different ways to organize the data. The data is stored as a binary stream of bytes in
Band Sequential (BSQ), Band Interleaved by Pixel (BIP), or Band Interleaved by Line (BIL)
format.

BSQ is the simplest format, with each line of data followed immediately by the next line of
the same spectral band. BSQ format is optimal for spatial (X,Y) access to any part of a
single spectral band.

BIP format provides optimal spectral processing performance. Images stored in BIP format
have the pixels for all bands in sequential order, first, second, third pixel etc., interleaved
up to the number of pixels. This format provides optimum performance for spectral access
of the image data.

BIL format provides a compromise in performance between spatial and spectral processing
and is the recommended file format for most Remote Sensing processing tasks. Images
stored in this format have first lines of all bands interleaved up to the number of bands.
Similarly all other lines are also interleaved.
As an image file contains only pixel values, additional information is needed in order to
display it and to keep track of it. Depending on the software used, this metadata is located
either in a separate file or as header along with data. This information is known as header, or
documentation.
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Metadata: The growing mass of digital data in the GIS marketplace is fuelling a demand for
information about geographic data, i.e. geospatial metadata.
1. Identification Information - basic information about the data set (Data set title, area
covered, keywords, purpose, abstract, and access and use restrictions).
2. Data Quality Information - a general assessment of the quality of the data set (Horizontal
and vertical accuracy assessment, data set completeness and lineage), completeness reportinformation about omissions, selection criteria, generalization, and definitions used and other
rules used to derive the data set. Lineage - information about the events, parameters, and
source data which constructed the data set and information about the responsible parties.
3. Spatial Data Organization Information - the mechanism used to represent spatial
information in the data set. Raster, vector, or an indirect (e.g. address) link to location.
4. Spatial Reference Information - the description of the reference frame for, and the means
to encode, coordinates in the data set (Latitude /longitude, horizontal and vertical coordinate
system, map projection, datum).
5. Entity and Attribute Information - details about the information content of the data set,
including the entity types, their attributes, and the domains from which attribute values may be
assigned.
Attribute Domain Values -- the valid values that can be assigned for an attribute.
Attribute Value Accuracy Information -- an assessment of the accuracy of the assignment of
attribute values.
6. Distribution Information - information about the distributor of and options for obtaining
the data set. (Distributor, file format of data, off-line media types, on-line link to data, fees)
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7. Metadata Reference Information - Information on the basis of the time period of content
information is determined.
2.3.4. PRINCIPLES OF OBJECT IDENTIFICATION
For image recognition, professional image interpreters use a number of characteristics to help
them identify remotely sensed objects. Some of these characteristics include:
Shape: This characteristic alone may serve to identify many objects. Examples include the
long linear lines of highways, the intersecting runways of an airfield, the perfectly rectangular
shape of buildings, or the recognizable shape of an outdoor baseball diamond.
Size: The relative and absolute sizes of objects are important in their identification. The scale
of the image determines the absolute size of an object. As a result, it is very important to
recognize the scale of the image to be analyzed.
Image Tone or Color: All objects reflect or emit specific signatures of electromagnetic
radiation.
Pattern: Many objects arrange themselves in typical patterns. This is especially true of humanmade phenomena. For example, orchards have a systematic arrangement imposed by a farmer,
while natural vegetation usually has a random or chaotic pattern.
Shadow: Shadows can sometimes be used to get a different view of an object. For example, an
overhead photograph of a towering smokestack or a radio transmission tower normally
presents an identification problem. These shadows display the shape of the object on the
ground and often conceal things found on the Earth's surface causing problems in
interpretation.
Texture: Imaged objects display some degree of coarseness or smoothness. This characteristic
can sometimes be useful in object interpretation. There would be textural differences when
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comparing an area of grass with a field corn. Texture, just like object size, is directly related to
the scale of the image.
2.4 CONCLUSION
Cartographic object extraction from digital imagery is a fundamental operation for GIS update.
Remote sensing is a multidisciplinary technique of electronic and analog image acquisition and
exploitation which includes aerial photography. The images thus acquired by remote sensing
are further processed to extract the different features.
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