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Satellite Image Analysis for Disaster and Crisis-Management Support


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This paper describes how multisource satellite data and efficient image analysis may successfully be used to conduct rapid-mapping tasks in the domain of disaster and crisis-management support. The German Aerospace Center (DLR) has set up a dedicated crosscutting service, which is the so-called "Center for satellite-based Crisis Information" (ZKI), to facilitate the use of its Earth-observation capacities in the service of national and international response to major disaster situations, humanitarian relief efforts, and civil security issues. This paper describes successful rapid satellite mapping campaigns supporting disaster relief and demonstrates how this technology can be used for civilian crisis-management purposes. During the last years, various international coordination bodies were established, improving the disaster-response-related cooperation within the Earth-observation community worldwide. DLR/ZKI operates in this context, closely networking with public authorities (civil security), nongovernmental organizations (humanitarian relief organizations), satellite operators, and other space agencies. This paper reflects on several of these international activities, such as the International Charter Space and Major Disasters, describes mapping procedures, and reports on rapid-mapping experiences gained during various disaster-response applications. The example cases presented cover rapid impact assessment after the Indian Ocean Tsunami, forest fires mapping for Portugal, earthquake-damage assessment for Pakistan, and landslide extent mapping for the Philippines
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Satellite Image Analysis for Disaster
and Crisis-Management Support
Stefan Voigt, Thomas Kemper, Torsten Riedlinger, Ralph Kiefl, Klaas Scholte, and Harald Mehl
Abstract—This paper describes how multisource satellite data
and efficient image analysis may successfully be used to con-
duct rapid-mapping tasks in the domain of disaster and crisis-
management support. The German Aerospace Center (DLR) has
set up a dedicated crosscutting service, which is the so-called
“Center for satellite-based Crisis Information” (ZKI), to facil-
itate the use of its Earth-observation capacities in the service
of national and international response to major disaster situa-
tions, humanitarian relief efforts, and civil security issues. This
paper describes successful rapid satellite mapping campaigns
supporting disaster relief and demonstrates how this technology
can be used for civilian crisis-management purposes. During the
last years, various international coordination bodies were estab-
lished, improving the disaster-response-related cooperation within
the Earth-observation community worldwide. DLR/ZKI operates
in this context, closely networking with public authorities (civil
security), nongovernmental organizations (humanitarian relief
organizations), satellite operators, and other space agencies. This
paper reflects on several of these international activities, such as
the International Charter Space and Major Disasters, describes
mapping procedures, and reports on rapid-mapping experiences
gained during various disaster-response applications. The example
cases presented cover rapid impact assessment after the Indian
Ocean Tsunami, forest fires mapping for Portugal, earthquake-
damage assessment for Pakistan, and landslide extent mapping
for the Philippines.
Index Terms—Crisis information, disaster monitoring, rapid
mapping, risk management, satellite remote sensing.
IN RECENT years, satellite systems and image-analysis
techniques have developed to an extent where civil and
commercial Earth-observation instruments can contribute sig-
nificantly to support the management of major technical and
natural disasters, as well as humanitarian crisis situations. Com-
paring today’s availability of satellite imagery to the situation
about ten years ago, the amount, timeliness, and availability of
satellite imagery covering a certain crisis situation or disaster
Manuscript received February 26, 2006; revised December 27, 2006. This
work was supported in part by the research and development grants of the
German Aerospace Agency (DLR), by the project funds of the Global Mon-
itoring for Environment and Security (GMES) program of the European Space
Agency, namely the RESPOND and RISK-EOS projects, and by research
funding granted within the 6th Research Framework Program of the European
Commission for the Network of Excellence “Global Monitoring for Stability
and Security” (GMOSS).
The authors are with the German Remote Sensing Data Center (DFD),
German Aerospace Center (DLR) at Oberpfaffenhofen, 82234 Wessling,
Germany (e-mail:
Color versions of one or more figures in this paper are available online at
Digital Object Identifier 10.1109/TGRS.2007.895830
event has improved substantially. There are several factors
which have lead to this fact. First of all, ground pixel spacing
of civil Earth-observation systems has developed to the meter
domain for optical and radar systems and to the decameter
domain for thermal imaging satellites. Second, during the
1990s, communication, networking, and interoperability among
the different satellite systems have improved substantially to
facilitate international satellite-based disaster-response capac-
ities. Third, through a number of international scientific and
technical coordination bodies, international cooperation mech-
anisms were established, such as the Disaster Management
Support Group (DMSG) of the International Committee on
Earth Observing Satellites or the International Charter Space
and Major Disasters. The main task of the DMSG is to per-
ceive the specifications, basic observations, and monitoring
requirements of current and future observing systems fully or
partially dedicated to disaster management tasks. Based on con-
clusions of the DMSG [1] and the UNISPACE III conference,
the International Charter Space and Major Disasters [2], from
here onwards referred to as International Charter, was founded
in 1999. Since this time, the International Charter has been
providing a crucial mechanism for globally coordinated disaster
response by civilian governmental satellite operators and space
agencies for natural and man-made disasters.
Currently, the International Charter is operated by a num-
ber of space agencies (Table I) and has been activated over
100 times (July 2006, [2]), providing meaningful mapping and
analysis products to the civil-protection and relief organizations
at appropriate scale in time and space. Taking into account
the increasing rate of natural and technological disaster situa-
tions affecting human activities on this planet, the International
Charter still bares potential for extension and improvement.
First of all, the commitment to provide only plain satellite
imagery, rather than analysis results, should be extended to also
formally committing the provision of ready to use information
products (maps) applicable in the domain of humanitarian
and natural crisis situations. Second, the speed of data and
information delivery may still be improved in the context of
the Charter, as natural, humanitarian or technical disaster often
cannot be predicted in space and time and thus requires max-
imum responsiveness to maximize mitigation efforts. Third,
the actors in the domain of satellite-based response to civilian
crisis and disaster situations may improve their mutual coordi-
nation and cooperation to allow best use of existing systems
and mechanisms and to exploit their synergistic potential to
the maximum level possible. Such coordination shall address
technical and organizational matters, as well as information
sharing or capacity building.
0196-2892/$25.00 © 2007 IEEE
After several years of research and development in the
domain using satellite systems for civilian crisis and disaster
response, the German Aerospace Center (DLR) has set up a
dedicated crosscutting service, the Center for satellite-based
Crisis Information (ZKI), to combine and facilitate the use of
its Earth-observation capacities in the service of national and
international response to major disaster situations, humanitar-
ian relief efforts, or civil security issues. The aim of this paper
is to describe and discuss the assessment of multiple satellite
data sources, the crisis support service cycle, the multisource
image analysis, and adding of the geospatial context to satellite
information in order to rapidly supply self-explaining geospa-
tial information products for disaster and crisis-management
Maps, geospatial information, and thematic analysis derived
from satellite imagery can support decision making and situa-
tion awareness during all phases of the disaster and crisis cycle.
This is defined through preparedness, alertness, rapid analy-
sis, response, recovery, and reconstruction [3]. In particular,
during the analysis, response, and recovery phase, only very
fast delivery of up-to-date, accurate, and comprehensive image-
analysis products can significantly help in the assessment of
large disaster situations, in particular in remote areas, where
other means of assessment or mapping either fail or are of
insufficient quality. Before the formation of the International
Charter, it was mainly by chance or in very special cases where
programming, access, delivery, and analysis of civil satellite
imagery was fast enough so that a response time of hours or
a few days was reached and allowed a proper use for relief-
work purposes. Only with the installation of the International
Charter, a globally functioning mechanism was established
to coordinate the tasking of multiple satellites and archiving
systems in very short time, without hindering formalities.
Hence, a meaningful satellite observation information capacity
was established for a variety of nonexpert users such as civil
protection, humanitarian relief workers, and donor/funding
Shortly after the International Charter was introduced, very
high resolution (VHR) commercial satellite imaging in the
1-m domain became publicly available. Also, this 1-m domain
imagery allowed nonexperts in many situations to read informa-
tion intuitively from the carefully processed imagery itself or to
allow the analysts to generate fast and easy-to-read space maps
showing location, situation, scale, or extent of a given disaster
or crisis situation. In spite of these crucial developments in the
domain of satellite imagery provision, it is important to note
that no decision maker or relief worker can work with raw
satellite imagery—it always takes a very careful processing,
analysis, mapping, and interpretation process to generate the
required situation maps, reports, or statistics which can be
read and understood by nonsatellite expert users. It has to be
mentioned that the International Charter still has quite some
shortcomings in this respect as it formally only commits to
provide raw satellite imagery to the users. All analysis and
value adding work has to be conducted in other frame works
and contexts such as described next.
In order to process the full cycle, from an emergency call
or request for assistance, through satellite tasking, data ac-
quisition, analysis, map provision, and interpretation, one has
to go through a chain of various steps involving coordination
of satellite commanding and data reception tasks, as well as
data ingestion, preprocessing, correction, and analysis. Just as
important as the data processing and delivery is the close con-
tact and interaction with the key actors in the user community
(Fig. 1) [4].
Experience over the past years has shown that neither sophis-
ticated image analysis and processing nor mapping capacities
or geographic information system (GIS) skills alone allow
providing a meaningful disaster-related information service to
crisis response staff and within operational scenarios. Only if
it is possible to operate the whole crisis support service cycle,
linking Earth-observation systems, information extraction, and
dissemination with specifically trained decision makers, or field
staff of the relief organizations without interrupt, space-based
crisis mapping can have a positive impact on disaster-relief
Satellite image analysis for disaster and crisis-management
support has to rely on whatever geoinformation is available
Fig. 1. DLR-ZKI service cycle. ZKI touches both the space segment and
decision-maker cycles, and due to this interlinking network, it is possible to
provide optimal service delivery to its users.
fast in order to respond as quickly as possible. Moreover,
for each disaster type, different data and analysis techniques
are required. Consequently, a wide expertise and capacity for
various types of data is essential. The most important data
sources are VHR optical data, thermal imagery, and synthetic
aperture radar (SAR) systems.
Optical data are of great importance for disaster management
support such as planning the logistics of relief actions in the
field immediately after, for example, an earthquake or tsunami
[5]. One major advantage of VHR optical data is that their
interpretation is also intuitive for nonexperts. For example, a
map at scale 1 : 7500 using predisaster VHR imagery gives the
aid workers in the field an overview of the predisaster building
structure. Such information is highly valuable to search and
rescue operations, as well as for reconstruction planning after
The thermal imagery offers excellent possibilities for map-
ping of hot spots caused by wild fires. Operational wild-
fire-detection systems using Advanced Very High Resolution
Radiometer (AVHRR) data have been developed for use in
Canada [6] and Finland [7], among other countries. Thermal
satellite data can give an overview information about the ex-
tent and the number of actually burning fires due to the fact
that the sensors of the National Oceanic and Atmospheric
Administration (NOAA) AVHRR and Moderate Resolution
Imaging Spectroradiometer (MODIS) are sensitive to fires that
are much smaller than their spatial resolution [8]. In partic-
ular, the MODIS sensor is also useful for large-scale flood
monitoring [9].
Although most of the commercial or research Earth-
observation satellite systems are optical/thermal systems, the
SAR systems onboard European Remote Sensing satellites
(ERS), Radarsat, ENVISAT, or the Advanced Land Observing
Satellite (ALOS) are of great value for the fast response map-
ping and analysis tasks as they allow imaging at wavelengths
almost unaffected by atmospheric disturbances such as rain
or cloud. Although radar imagery is somewhat less intuitive
to be interpreted by nonexperts, they resemble a very useful
source for flood events [6], [10], oil spills [11], landslides, and
earthquakes [12], particularly when postevent imagery can be
jointly analyzed with archived reference imagery for change
detection or interferometric coherence or displacement mea-
surements. The German TerraSAR-X System and the Italian
Cosmo-Skymed, which are to be launched in 2007, will extend
the civilian SAR availability to the 1-m domain, allowing VHR
all weather image acquisitions.
For many disaster-related image analysis and mapping tasks,
the availability of appropriate and accurate topographic eleva-
tion data on the affected area is of uttermost importance. Thus,
interferometrically derived digital elevation models (DEMs),
such as which resulted from the Shuttle Radar Topogra-
phy Mission (SRTM) [13], are of very high value for im-
age processing (e.g., orthorectification) and map generation
(e.g., hillshading).
It is evident that satellite-derived information alone does not
suffice to generate a meaningful analysis of a given disaster
situation. Experiences gathered during the work with relief
organizations show that it is an absolute key to fuse the satellite-
based information with additional data to present it in a proper
geospatial context. Thus, in addition to the expertise in image
analysis, an equally important task is the generation of compre-
hensive and easy-to-use map products. For this purpose, refer-
ence data sets such as place names, road network, rivers, critical
infrastructure, and topographic information are required.
The most crucial problem is the availability and the access to
accurate and up-to-date spatial data, particularly in remote re-
gions. The benefits of interoperable spatial data infrastructures
(SDI) for data access and dissemination in the framework of
disaster response are already demonstrated [14]. This promising
approach implies that different organizations provide the data
that they are responsible for by web-based systems. However,
up to the present, only few countries established and operate
an SDI; this is particularly the case for Africa and the Asian-
Pacific countries due to technical or legal constraints [15].
Therefore, there is still a gap in the availability, particularly of
local-scale geodata.
Fast and easy accessible global data sets such VMap [16]
are often not accurate enough for a high-resolution mapping.
Hence, high-resolution data sets on infrastructure and settle-
ment boundaries have to be derived by visual interpretation
of satellite imagery. Global gazetteers such as the GEOnet
Names Server [17] can be used for labeling settlements and
physiographical features like rivers and mountains. For a rough
estimation of the population affected by a disaster, the Land-
Scan database [18] gives a good representation of rural and
urban population densities. A combination of interferometri-
cally derived DEMs from SRTM X- and C-band, ERS, and
GLOBE 30-arcsecond data [19] provides a global basis for the
derivation of contour lines as an adequate representation of the
Generally, the map-generation process consists of different
steps: integration of spatial data, data analysis, layout, quality
control, map editing, and dissemination, as well as possible
Fig. 2. High-resolution satellite map from new and archived satellite imagery of the northern Khao Lak Region, Thailand (1: 12.500). The archived predisaster
and new acquired postdisaster satellite images allow easy and quantitative damage assessment by visual change detection. Tsunami-affected areas are delinated
using a red polygon signature, and in blue, the coastline before the tsunami is indicated.
updating of the map. The following section will show some
examples and in more detail how the rapid mapping can suc-
cessfully be accomplished.
Going back only for the last two years, several outstanding
examples can be given where satellite-based maps could pro-
vide information supporting international humanitarian relief
teams or domestic disaster-relief operations in very fast and
efficient ways. Four of the most prominent internationally
relevant application examples, on which DLR/ZKI has worked,
are described in the following section and cover disaster
situations in the following geographic regions: Southeast Asia
(Tsunami), Portugal (fire hot spots), Pakistan (earthquake),
and the Philippines (landslide). It is important to note that
the following examples of satellite imagery application for
disaster relief intend to showcase swift and synergistic use of
state-of-the-art processing techniques and rapid data access.
These rapid-mapping results could be achieved by building on
existing scientific results and long-term engineering experience
in the domain of satellite data processing. It is not indented to
report major generic methodological research results or method
comparison here.
A. Impact Assessment for the Indian Ocean Tsunami
In the early morning of December 26, 2004, a severe earth-
quake (30 km below sea level, magnitude 9) caused Tsunami
flood waves in the Indian Ocean, which struck the coastal
regions of Sumatra, Thailand, Sri Lanka, and southern India,
and killed more than 200 000 people. Due to the immense
extent of the affected coastal areas, the International Charter
was triggered three times for India, Sri Lanka, and Indonesia/
Thailand. After consultation and coordination with various
international partners, DLR/ZKI concentrated its activities on
Thailand and Indonesia. Until postdisaster imagery became
available, reference maps using archived satellite data were
produced giving first basic information about the affected areas.
These archived satellite data from the global land-cover facility
[20] were combined with geospatial information.
The first postdisaster images of Sumatra were delivered on
December 29, 2006, three days after the disaster. Due to the
scale of the damage, both medium [Landsat-7 Enhanced The-
matic Mapper (ETM), SPOT, disaster monitoring constellation
(DMC)] and very high-resolution imageries (IKONOS, Quick-
bird) were used for quantitative damage assessment mainly
by means of visual change detection (Fig. 2). This up-to-date
mapping, covering large parts of the affected area, enabled
disaster managers to achieve an overview of the situation, to
Fig. 3. Satellite map of the Serra do Estrela, Portugal (1 :50.000). The image shows in red the area that burned until August 25, 2005, where the different colored
circles denote the MODIS-derived hot spots between August 14 (dark red), August 16 (red), August 17 (orange), and August 21 (yellow), allowing a detail tracing
of the fire propagation.
assess the damage, and to supply local logistic teams with
reliable information on a short notice. ZKI provided this ser-
vice in cooperation with its partners in the Global Monitor-
ing for Environment and Security/RESPOND consortium and
followed requests of the crisis reaction team of Germany’s
Federal Foreign Office (Auswärtiges Amt), the joint federal
situation and information center, the German technical relief
agency, the German Red Cross, and Medicines Sans Frontiers.
The products were also provided to a wide international user
community, as well as the press and the public via the Internet.
B. Providing an Overview on Forest Fires in Portugal
In the Mediterranean, each summer, natural- and human-
induced wildfires are a threat not only to the environment but
also to the local population. In the year 2005, after one of the
most severe droughts over the Iberian Peninsula, the wildfires
were raging out of control across central and northern Portugal,
killing 15 people and destroying more than 150 000 ha of
agricultural land and urban area.
The worst-hit areas included the central region of Portugal,
where fires were threatening the outskirts of Coimbra, which
is the third largest city of the country. Wildfires were also con-
tinuing to burn in the northern districts of Viseu and Viana do
Castelo. After a request of the Portuguese fire-fighting service,
the International Charter was triggered, and the burned area was
mapped using SPOT, Landsat-7 ETM, DMC, and IKONOS.
The burned area was mapped from visual image comparison
of pre- and postdisaster data, as it proofed to be faster and more
accurate than automated classification tools. Due to the large
areas that were affected, medium resolution satellites such as
SPOT-5, Landsat-7 ETM, and DMC proofed to be more ad-
vantageous over the very high-resolution IKONOS data, which
were used only for the affected urban areas.
In parallel, the situation was monitored using the sensors
NOAA-AVHRR 17 and MODIS Aqua and Terra for the de-
tection of fire hot spots. Near-real time hot-spot detection for
the Mediterranean is achieved at DLR through an inhouse
reception of NOAA AVHRR and MODIS data and direct
processing. This processing chain is based on the adapted
APOLLO tool for NOAA AVHRR [21] and the enhanced
MODIS fire detection developed by [22]. The value-added
product was delivered daily, 2 to 5 h after the satellite over-
pass via the Internet and for direct integration into GISs
of the civil-protection units. This continuous monitoring al-
lowed a detailed tracing of the fire propagation and a better
planning of fire-fighting capacities by the Portuguese civil
protection (Fig. 3).
Fig. 4. Damage assessment for the northern Muzaffarabad city area, Muzaffarabad District, Pakistan (1 : 7500). Visual interpretation on pre- and postdisaster
images with 250 ×250 m grid cells is applied to interpret damage to infrastructure and build-up areas by means of UN housing damage classification: no damage,
moderately damage (<33%), severely damage (33%–66%), and completely destroyed (>66%). Semitransparent color map overlay is used to visualize damages,
and blue-colored grid cells show those areas with moderate (light blue) to high (dark blue) damage to infrastructure, whereas the orange- and red-colored grid
cells show damage to build-up areas.
C. Earthquake Relief Support for Pakistan
A series of severe earthquakes (maximum magnitude of 7.6)
struck the Kashmir region on Saturday October 8, 2005. The
epicenter was located on the India–Pakistan border, which is
about 100-km northeast of Islamabad, and Pakistani authori-
ties reported some 49 700 casualties and over 74 000 injured.
On October 11, which is two days after activation of the
International Charter, DLR produced several detailed maps
(1 : 7500) of a number of cities in the earthquake-affected
region, which are derived from very high-resolution IKONOS
imagery. Typical image preprocessing for such rapid-mapping
services includes atmospheric corrections [23], orthorectifica-
tion [24], pansharpening [25], filtering, contrast enhancements,
and visualization.
The positional accuracy of uncorrected VHR imagery is poor
in mountainous areas such as the Kashmir region, and therefore,
VHR imagery requires orthorectification before application
in high-resolution earthquake-damage (change) mapping. The
initial positional accuracy of about 300 to 1000 m, which
was acquired with 15–30off-nadir viewing angles [26],
was improved to about 3–5 m to meet the CE90 accuracy
requirement [27].
However, since rapid mapping often places very difficult time
constraints on production, time-consuming processes such as a
full sophisticated atmospheric correction [23] are only applied
when crucial for the image analysis and interpretation process.
This is because the emphasis in rapid mapping is primarily
on visual interpretation rather than on automated extraction of
quantified environmental variables.
Damage assessment is an important task within the frame-
work of rapid mapping, and there are almost unlimited ap-
plications of change analysis [5]. However, standard change
analyses are not always suitable for VHR, e.g., object-based
classification is very time demanding (up to 30 h or more)
and not as straightforward as the more conventional Bayesian
methods. Change analysis using polygons of varying size and
type is a very time-consuming approach, as the identification
of homogeneous image sections is a very difficult and time-
consuming task. SAR imagery may be used for assessing
damages to houses and buildings [28]; however, depending
on the type of damage and the type of structures affected,
results may also be limited. Another common approach is to
visualize change analysis through color coding by means of the
predisaster image, e.g., in green, and the postdisaster image,
e.g., in red. Hence, an image combination by superimposing
the different colors (color additive mixing process) will then
reveal unchanged properties in yellow. This method requires
very accurate image coregistration and does not allow intuitive
reading by the user.
Therefore, a new damage-assessment method was tested for
the city of Muzaffarabad (Fig. 4) using visual interpretation on
pre- and postdisaster images with 250 ×250 m grid cells. This
method, which was proposed by the European Joint Research
Center team working on the satellite-based disaster-response
efforts, interprets damage to infrastructure and build-up areas
for each grid cell by means of the UN housing damage classifi-
cation [29]: no damage, moderately damage (<33%), severely
damage (33%–66%), and completely destroyed (>66%). The
method uses semitransparent color map overlay to visualize
damages (Fig. 4) where blue-colored grid cells show those
areas with moderate (light blue) to high (dark blue) damage to
infrastructure whereas the orange- and red-colored grid cells
Fig. 5. Crisis map at a scale of 1 : 25 000 of the landslide-affected area around Guinsaugon, Saint Bernard, Southern Leyte Island, Philippines. The backdrop
image shows a predisaster SPOT5 image, and the landslide extent was derived from a number of different sensors: Radarsat, ASAR, and ALOS AVNIR-2.
show damage to build-up areas. If enough time is available,
map overlay with critical infrastructure information (bridges,
airports, tunnels, ports, hospitals, clinics, and schools) should
be generated.
According to the users’ feedback, the maps and layers
(streets, damage, etc.) yield vital information with respect to
evacuation planning, general “pathfinding/tracking,” to get a
better overview and understanding of problems on site. In
addition, the maps proved very useful for negotiations about
logistics and joint operation among relief organizations in the
D. Mapping a Devastating Landslide in the Philippines
On Friday, February 17, 2006, a landslide triggered by
heavy rains buried the village of Guinsaugon, Saint Bernard,
Southern Leyte Island, Philippines. Most of the approximately
300 houses and the elementary school were fully covered by
the mudslide, affecting the 1411 village inhabitants (including
246 elementary school pupils and 7 teachers) at the time of the
The crisis map at a scale of 1 : 25 000 of the landslide-
affected area uses a predisaster SPOT5 image, which was
acquired on June 1, 2003, as image backdrop. Image process-
ing carried out includes atmospheric correction [23] and the
generation of a synthetic blue channel (to provide a true color
image to the map users), orthorectification by means of direct
georeferencing of SPOT HRS stereo data [30], [31], and image
enhancement such as stretching and filtering. VMAP level 0
yielded input as the main gazetteer information source, whereas
Google Earth and Microsoft Encarta provided additional infor-
mation where the VMAP data were missing or inaccurate.
The landslide extent was derived and crosschecked from
a number of different satellite sensors: Radarsat, Advanced
Synthetic Apperture Radar (ASAR), and ALOS Advanced
Visible and Near-Infrared Radiometer (AVNIR)-2. Geocoding
of the radar imagery is carried out using a geocoding system
developed by DLR [32]–[34], which supports Envisat-ASAR,
ERS, J-ERS, Radarsat-1, SIR-C / X-SAR, and TerraSAR-X.
The pixel spacing of the in- and output data, as well as the
Doppler reference function, are parameterized and are stored in
configuration files used by the system. The geocoding product
is “enhanced” ellipsoid corrected, which means that the product
is projected and resampled to UTM using WGS-84 geodetic
reference, and terrain-induced distortions are corrected
considering a DEM. The geometric quality of the product is
good, due to the height accuracy and resolution of the used
SRTM (C-band and X-SAR), ERS-derived elevation models,
and the GLOBE elevation information, in combination with
the type of terrain and the incidence angle. Even though most
SAR processors refer to zero Doppler, the geocoding system
is able to consider other reference functions. Multipolarized
data are considered as multilayer images. Hence, the landslide
extent was derived by means of on-screen digitizing.
The ALOS imagery from the Japanese Aerospace Explo-
ration Agency, which was launched on January 24, 2006,
yielded optical data input; however, the sensor only partially
imaged the landslide due to partially cloudy weather situation.
Hence, accurate geocoding using the SPOT reference scene was
established, and again, the landslide extent could be derived by
means of on-screen digitizing.
The landslide mapping (Fig. 5) shows the potential of the
usage of a number of data sources (SPOT, SRTM, and ALOS)
for the benefit of humanitarian relief organizations.
Examples could be shown how multiple satellite image
processing and analysis techniques may successfully be applied
individually or in a combined manor to serve rapid-mapping
tasks in the domain of disaster and crisis-management support.
It can be concluded that no single commercial or research-
oriented satellite system alone can effectively provide and guar-
antee fast and reliably image access on archive or new postevent
imagery. Thus, effective and well-balanced coordination among
the different observing systems is required in order to allow
best service to the civil-protection and humanitarian relief
community. As military observing capacities are usually even
more efficient with respect to temporal coverage and spatial
resolution, it may be interesting to improve civilian–military
cooperation in this domain in the future. There is still a need for
an improvement of the availability of consistent, comparable,
and reliable high-resolution geospatial data. Initiatives to estab-
lish SDI can facilitate the access to reference data, as well as
the dissemination of rapid-mapping products. As discussed in
this paper, just as with the civil-protection work itself, effective
satellite-based disaster-relief efforts rely on international, inter-
disciplinary, and interorganizational cooperation mechanisms
and team work. The European Initiative in GMES provides an
important frame in this context.
The authors would like to thank the various colleagues of
German Remote Sensing Data Center and DLR working in the
frame of ZKI, as well as all other national and international
institutions cooperating in the global disaster-relief efforts.
The authors would also like to thank all the colleagues and
institutions involved in the International Charter—Space and
Major Disasters and to the team of European Space Imag-
ing in Munich, Germany, and GeoEye for the rapid imagery
provision efforts. They would also like to thank D. Ehrlich,
C. Louvrier, and D. Khudhairy of the Joint Research Centre
for the excellent collaboration and for providing the grid-cell
damage-assessment procedure.
[1] CEOS, “The use of earth observation satellites for hazard support:
Assessments and scenarios. Final report of the CEOS Disaster Manage-
ment Support Group,” 2002. [Online]. Available:
pages/DMSG/pdf/CEOSDM SG.pdf
[2] The International Charter, “Space and Major Disasters,” 2000. [Online].
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Stefan Voigt received the M.Sc. degree in physi-
cal geography, physics, and remote sensing
from Ludwig-Maximilians-University, München,
Germany, in 1997, and the Ph.D. degree, working
on a European Commission research project on the
remote sensing and runoff/flood forecasting, from
Berne University, Berne, Switzerland, in 2000.
Since then, he has been with the German Re-
mote Sensing Data Center (DFD) of the German
Aerospace Center (DLR), Wessling, leading a
research team on “Crisis Information and Rapid
Mapping.” Furthermore, he is in charge of the scientific and operational
coordination of the DLR/DFD’s Center for satellite-based Crisis Information
(ZKI) which is contributing to several national and international activities
and projects in the field of disaster mitigation, humanitarian relief, as well as
civil security. Since 2001, he has been coordinating an international research
initiative on innovative technologies for detection prevention and monitoring
uncontrolled of coal seam fires in China.
Thomas Kemper was born in Wickede, Germany, in
1969. He received the degree in physical geography
and the Ph.D. degree from the University of Trier,
Trier, Germany, for his work on quantification of
heavy metals in soils using reflectance spectroscopy,
in 1997 and 2003, respectively.
From 1998 to 2004, he was with the Institute for
Environment and Sustainability of the Joint Research
Centre (JRC) in the context of soil degradation and
desertification. He joined the German Remote Sens-
ing Data Center of the German Aerospace Center in
October 2004 and is currently working in the Center for satellite-based Crisis
Information, Wessling, Germany.
Torsten Riedlinger received the M.Sc. degree in
physical geography, climatology, and soil science
from the University of Trier, Trier, Germany, in 1999.
From 2000 to 2003, he had a Ph.D. scholarship
on analyzing and modeling the landscape evolution
of a semiarid watershed in southeast Spain. Since the
spring of 2003, he has been working in the Center for
satellite-based Crisis Information at the German Re-
mote Sensing Data Center of the German Aerospace
Center (DLR), Wessling, Germany, including remote
sensing and geographic information system analysis,
as well as national and international coordination, related to man-made and
natural disasters, humanitarian relief, and civil security. Furthermore, he is
responsible for DLR’s project management within the International Charter
on Space and Major Disasters, and he is Project Manager for the German
Contribution to the Indian Ocean Tsunami Early Warning System.
Ralph Kiefl received the Diploma degree in physical
geography, with a major in geographic information
systems (GIS), from Johannes Gutenberg-University,
Mainz, Germany, in 2002.
Since April 2002, he has been working at the
German Remote Sensing Data Center of the German
Aerospace Center, Wessling, in projects for land-
cover classification. Since autumn 2003, his main
task has been the application of desktop and web-
based GIS in the context of satellite-based crisis
Klaas Scholte received the M.Sc. degree in physical
geography from Utrecht University, Utrecht, The
Netherlands, in 1998, and the Ph.D. degree, focus-
ing on geologic applications by using hyperspectral
remote sensing, InSAR, and geophysical methods,
from the Delft University of Technology, Delft, The
Netherlands, in 2005.
He worked for two years with the International
Institute for Geo-Information Science and Earth Ob-
servation, The Netherlands, and JRC-Ispra, Italy,
on remote sensing methods for desertification and
vegetation monitoring. Since the autumn of 2005, he has been working with
the German Remote Sensing Data Center of the German Aerospace Center,
Wessling, in the context of the European Space Agency Global Monitoring
for Environment and Security Service Element RESPOND on satellite-based
information for humanitarian relief.
Harald Mehl received the Diploma degree in ge-
ography and the Ph.D. (Dr. rer nat) degree in re-
mote sensing from Ludwig-Maximilians-University
of Munich, Munich, Germany, in 1988 and 1991,
He was a Senior Scientist with the Working Group
Remote Sensing at the Institute for Applied Geology
of the Ludwig-Maximilians-University of Munich:
organization and realization of research projects in
Latin America and South East Asia, in 1992–1996.
In 1996–1998, he was Head of the Executive Office
of German Aerospace Center Organization and Management. Since 1999, he
has been Head of the Unit Environment and Security of the German Remote
Sensing Data Center, Wessling, Germany: Management of the unit and defini-
tion of new application areas. His profile includes the following: Specialist in
remote sensing, geographic information system, geography, geology, and land
use, and Education Generalist in agriculture, forestry, soil science, cartography,
and hazards.
... United States reconnaissance satellites operated from the 1960s; however, these optical images were only declassified in the last few decades (Fowler, 2013). Optical imagery is intuitive to interpret by the human eye and is therefore interpretable by non-specialists (Voigt et al., 2007), even considering non-visible spectral information such as near-or short-wave infrared, which can reveal non-visible characteristics such as the presence of chlorophyll and therefore the health of vegetation ( Figure 1a) (Pettorelli et al., 2005). By comparison, SAR data, representing transmitted radio wave returns from a side-looking antenna, 45 produce an all-weather view of the earth characterising the scattering properties of the reflecting surface or object (Rosen et al., 2000) but are less readily interpretable. ...
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Satellite-based earth observation sensors are increasingly able to monitor geophysical signals related to natural hazards, and many groups are working on rapid data acquisition, processing, and dissemination to data users with a wide range of expertise and goals. A particular challenge in the meaningful dissemination of Interferometric Synthetic Aperture Radar (InSAR) data to non-expert users is its unique differential data structure and sometimes low signal to noise ratio. In this study, we evaluate the online dissemination of ground deformation measurements from InSAR through Twitter, alongside the provision of open access InSAR data from the Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) Looking Into Continents from Space with Synthetic Aperture Radar (LiCSAR) processing system. Our aim is to evaluate (1) who interacts with disseminated InSAR data, (2) how the data are used and (3) to discuss strategies for meaningful communication and dissemination of open InSAR data. We found that InSAR Twitter activity was primarily associated with natural hazard response, specifically following earthquakes and volcanic activity, where InSAR measurements of ground deformation were disseminated, often using wrapped and unwrapped interferograms. For earthquake events, Sentinel-1 data were acquired, processed, and tweeted within 4.7±2.8 days (shortest was one day). Open access Sentinel-1 data dominated the InSAR tweets and were applied to volcanic and earthquake events in the most engaged with (retweeted) content. Open access InSAR data provided by LiCSAR was widely accessed, including automatically processed and tweeted interferograms and interactive event pages revealing ground deformation following earthquake events. The further work required to integrate dissemination of InSAR data into longer-term disaster risk reduction strategies is highly specific, both to hazard-type, international community of practice, and local political setting and civil protection mandates. Notably, communication of uncertainties and processing methodologies are still lacking. We conclude by outlining the future direction of COMET LiCSAR products to maximise their useability.
... Principally, remote sensing is a process of gathering information about an object, area or phenomenon obtained from a distance, typically from satellite or aircraft. Information gathered are presented in raster images and are ready to be analyzed for several uses, namely, land cover changes Prasad et al.,2015), disaster observation (Voigt et al., 2007), species distribution and zonation pattern (Lee et al., 2016;Chun et al., 2011). Palmer et al. (2005) suggested that a guiding image could be useful to reflect the ecosystem health status. ...
... Thanks to the growth of remote sensing technologies, today's satellites are like eyes in the sky. Indeed, sustainable transformation in using different types of satellites and sensors in space produces large amounts of remote sensing images for different applications, such as smart cities (Kucherov et al. 2017) and (Ismail et al. 2018), disaster management (Voigt et al. 2007) and (Boccardo and Giulio Earth Science Informatics Tonolo 2015), agriculture Wang and Gamon 2019), and (Huang et al. 2018), change detection (Yang et al. 2013;Shafique et al. 2022), and (Ghosh et al. 2011), etc. One of the most critical research issues in remote sensing is the optimal performance of storage and processing techniques in terms of response time and resource management. ...
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In recent years, one of the biggest concerns of researchers has been environmental knowledge. This concern can be resolved by collecting and processing remote sensing data in the shortest possible time and cost with the highest accuracy and efficiency. In remote sensing, various types of satellite data are processed for different purposes and applications. In this process, data storage and processing methods, resource management, scalability, performance improvement, and efficiency are among the issues and challenges in this scope. This paper presents a service-oriented framework using big data and parallel processing in remote sensing to address these challenges. The proposed framework provides scalability, flexibility, and generalization without dependency on specific data or processing techniques. In addition, it provides reasonable results to quality criteria such as response time, efficiency, and performance. The evaluation results of the proposed framework show the effectiveness of the framework for various types of analysis of remote sensing data with acceptable accuracy.
... The inclusion of Sentinel-1 SAR data enables the exploration of multi-modal fusion for cloud removal, and the inclusion of WorldCover land cover product enables to disentangle the performance over different land cover types and evaluate the quality of recovered semantic information. While the short revisit time may facilitate to temporal mosaicing, it is not applicable to time-critical applications (Voigt et al., 2007). And when encountering continual cloudy days, cloud-free reference data from an adjacent period is largely unavailable . ...
In this paper, we introduce Planet-CR, a benchmark dataset for high-resolution cloud removal with multi-modal and multi-resolution data fusion. Planet-CR is the first public dataset for cloud removal to feature globally sampled high resolution optical observations, in combination with paired radar measurements as well as pixel-level land cover annotations. It provides solid basis for exhaustive evaluation in terms of generating visually pleasing textures and semantically meaningful structures. With this dataset, we consider the problem of cloud removal in high resolution optical remote sensing imagery by integrating multi-modal and multi-resolution information. Existing multi-modal data fusion based methods, which assume the image pairs are aligned pixel-to-pixel, are hence not appropriate for this problem. To this end, we design a new baseline named Align-CR to perform the low-resolution SAR image guided high-resolution optical image cloud removal. It implicitly aligns the multi-modal and multi-resolution data during the reconstruction process to promote the cloud removal performance. The experimental results demonstrate that the proposed Align-CR method gives the best performance in both visual recovery quality and semantic recovery quality. The project is available at, and hope this will inspire future research.
... Since most casualties are primarily related to building collapses [1], immediate planning and response based on accurate building damage are important after earthquakes. Remote sensing techniques have been actively used as one of the most effective tools for responding to disasters due to the advantage of mapping damage over a wide area [2]. In particular, the Synthetic Aperture Radar (SAR) system can play an important role in the rapid detection of damaged areas because it enables timely observation with its all-weather and day/night observation capability. ...
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Synthetic Aperture Radar (SAR) remote sensing has been widely used as one of the most effective tools for responding to earthquake disasters. In general, damaged-building detection with SAR data has been conducted based on change detection using temporal SAR data acquired in the same observation mode. However, it is not always possible to use SAR data obtained with the appropriate observation mode in unexpected events such as natural disasters. This study aims to detect earthquake-induced damaged buildings using temporal SAR data having different observation modes. We presented a contextual change analysis method to map damaged buildings based on novel textural features. This study was conducted using the bi-temporal Komapsat-5 data obtained in different polarization modes. Experimental results for the area severely damaged by the 2016 Kumamoto earthquake showed that the proposed textural analysis can improve detectability in building-damaged areas while maintaining low false alarm rates in agricultural areas. According to the grid-based accuracy analysis, the proposed method can successfully detect the damaged areas with a detection rate of about 72.5% and false alarms of about 6.8% even on challenging data sets.
... Accurate and wellannotated maps can inform evacuation planning, retrofitting campaigns, and delivery of relief [59,200]. Further, this imagery can assist damage assessment, by comparing scenes immediately pre-and post-disaster [315,816]. Social media data can contain kernels of insight-places without water, clinics without supplies-which can inform relief efforts. ML can help properly surface these insights, compressing large volumes of social media data into the key takeaways, which can be acted upon by disaster managers [118,368,596]. ...
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Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.
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Collecting and sharing information about affected areas is an important activity for optimal decision-making in relief processes. Defects such as over-sending some items to affected areas and mistakes in transferring injured people to medical centers in accidents are due to improper management of this information. Because cloud computing as a processing and storage platform for big data is independent of the device and location and can also perform high-speed processing, its use in disasters has been highly regarded by researchers. In this environment, a three-stage dynamic procedure for evacuation operations and logistics issues is presented. The first stage of the proposed model is image processing and tweet mining in a cloud center in order to determine the disaster parameters. In stage II, a mixed-integer multi-commodity model is presented for the relief commodity delivery, wounded people transportation with capacity constraints, and locating of the possible on-site clinics and local distribution centers near disaster areas. In stage III, by using a system of equations, detailed vehicle load/unload instructions are obtained. Finally, the effectiveness of the proposed model on the data of an earthquake disaster in Iran is investigated. The results of comparing the proposed approach with a two-stage algorithm show that the total number of unsatisfied demand for all types of commodities in the proposed approach was better than the other. Also, the number of survivors in the three-stage model is significantly higher than in the two-stage one. The better performance of the proposed algorithm is due to the fact that online data is continuously available and that decisions such as sending relief items and dispatching are made more effectively.
Emergency situations encompassing natural and human-made disasters, as well as their cascading effects, pose serious threats to society at large. Machine learning (ML) algorithms are highly suitable for handling the large volumes of spatiotemporal data that are generated during such situations. Hence, over the years, they have been utilized in emergency management to aid first responders and decision-makers in such situations and ultimately improve disaster prevention, preparedness, response, and recovery. In this survey article, we highlight relevant work in this area by first focusing on the commonalities of emergency management applications and key challenges that ML algorithms need to address. Then, we present a categorization of relevant works across all the emergency management phases and operations, highlighting the main algorithms used. Based on our review, we conclude that ML algorithms can provide the basis for tackling different activities across the emergency management phases with a unified algorithmic framework that can solve a large set of problems. Finally, through the systematic literature review, we provide promising future directions for utilizing ML algorithms more effectively in emergency management applications. More importantly, we identify the need for better generalization of algorithms, improved explainability, and trustworthiness of ML algorithms with respect to the emergency management personnel, as well as more efficient ways of addressing the challenges associated with building appropriate datasets.
We propose an automated disaster mapping technique using pre- and post-disaster satellite imagery. We first find the geometric transformation for automatic image registration by matching regions represented by shape and intensity descriptors. We produce piece-wise constant approximations of the two images using the delineated regions. We perform linear subspace learning in the joint regional space and project the samples onto the orthogonal to tangent subspace to produce a change map and identify the outliers using statistical tests. We tested our method on multiple disaster datasets that is, four wildfire events and two flooding events. We validated our results by measuring the overlap score (DSC), and classification accuracy of our disaster map and ground-truth data. We performed comparisons to representative change detection techniques, namely Gabor Two-Level Clustering (G-TLC), and spectral index-based detection methods. Performance metrics indicated that the proposed Subspace Learning-based Disaster Mapping (SLDM) method produced more accurate change maps than the compared methods for multiple types of disaster events. Visual interpretation of the proposed SLDM method confirms its capacity for creating change maps for disaster mapping.
Full-text available
Spatial data and related technologies have proven to be crucial for effective collaborative decision-making in disaster management. However, there are currently substantial problems with availability, access and usage of reliable, up-to-date and accurate data for disaster management. This is a very important aspect to disaster response as timely, up-to-date and accurate spatial data describing the current situation is paramount to successfully responding to an emergency. This includes information about available resources, access to roads and damaged areas, required resources and required disaster response operations that should be available and accessible for use in a short period of time. Any problem or delay in data collection, access, usage and dissemination has negative impacts on the quality of decision-making and hence the quality of disaster response. Therefore, it is necessary to utilize appropriate frameworks and technologies to resolve current spatial data problems for disaster management.This paper aims to address the role of Spatial Data Infrastructure (SDI) as a framework for the development of a web-based system as a tool for facilitating disaster management by resolving current problems with spatial data. It is argued that the design and implementation of an SDI model and consideration of SDI development factors and issues, together with development of a web-based GIS, can assist disaster management agencies to improve the quality of their decision-making and increase efficiency and effectiveness in all levels of disaster management activities.The paper is based on an ongoing research project on the development of an SDI conceptual model and a prototype web-based system which can facilitate sharing, access and usage of spatial data in disaster management, particularly disaster response.
Full-text available
The LandScan Global Population Project produced a worldwide 1998 population database at 30" X 30" resolution for estimating ambient populations at risk. Best available census counts were distributed to cells based on probability coefficients which, in turn, were based on road proximity, slope, land cover, and nighttime lights. LandScan 1998 has been completed for the entire world. Verification and validation (V&V) studies were conducted routinely for all regions and more extensively for Israel, Germany, and the Southwestern United States. Geographic information systems (GIS) were essential for conflation of diverse input variables, computation of probability coefficients, allocation of population to cells, and reconciliation of cell totals with aggregate (usually province) control totals. Remote sensing was an essential source of two input variables–land cover and nighttime lights–and one ancillary database–high-resolution panchromatic imagery–used in V&V of the population model and resulting LandScan database.
Full-text available
This paper discusses a general methodology for post‐tsunami damage assessment and an automatic procedure able to distinguish between different kinds of damage on built‐up structures using very‐high‐resolution satellite data. The procedure for automatic detection of damaged built‐up structures was designed using a multi‐criteria approach fusing radiometric, textural, and morphological image features related to pre‐ and post‐disaster data detection. The proposed procedure shows good performance with an estimated overall accuracy equal to 93.97%. The best performances are estimated in the discrimination between non‐flooded and flooded built‐up structures and in the recognition of collapsed built‐up structures with debris in place. Problems of omission error were detected in the recognition of collapsed built‐up structures without debris in place as in the case of completed erased built‐up structures situated close to the shoreline.
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The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, launched on the National Aeronautics and Space Administration Terra satellite at the end of 1999, was designed with 36 spectral channels for a wide array of land, ocean, and atmospheric investigations. MODIS has a unique ability to observe fires, smoke, and burn scars globally. Its main fire detection channels saturate at high brightness temperatures: 500 K at 4 µm and 400 K at 11 µm, which can only be attained in rare circumstances at the 1 km fire detection spatial resolution. Thus, unlike other polar orbiting satellite sensors with similar thermal and spatial resolutions, but much lower saturation temperatures (e.g. Advanced Very High Resolution Radiometer and Along Track Scanning Radiometer), MODIS can distinguish between low intensity ground surface fires and high intensity crown forest fires. Smoke column concentration over land is for the first time being derived from the MODIS solar channels, extending from 0.41 µm to 2.1 µm. The smoke product has been provisionally validated both globally and regionally over southern Africa and central and south America. Burn scars are observed from MODIS even in the presence of smoke, using the 1.2 to 2.1 µm channels. MODIS burned area information is used to estimate pyrogenic emissions. A wide range of these fire and related products and validation are demonstrated for the wild fires that occurred in northwestern USA in Summer 2000. The MODIS rapid response system and direct broadcast capability is being developed to enable users to obtain and generate data in near real-time. It is expected that health and land management organizations will use these systems for monitoring the occurrence of fires and the dispersion of smoke within two to six hours after data acquisition.
Full-text available
Many countries have spent considerable resources over the past few years debating optimal national spatial data infrastructures. One of the (main) elements of these infrastructures is the national spatial data clearinghouse, which facilitates access to required spatial data and provides complementary services. With this in mind, in April 2000, 2001, 2002 and December 2000, 2001, 2002, a web survey was carried out to assess systematically the developments of these national clearinghouses worldwide. Regarding the development in the number of implementations, it can be considered a worldwide success. However, of concern are the declining trends in use, management and content. One of the main reasons for these negative trends could be the dissatisfaction of the spatial data community with the functional capability of current clearinghouses. The functional capabilities of clearinghouses should likely be changed from a data-oriented to a user and application-oriented focus. This is in accord with the objectives of the second generation of spatial data infrastructures. The main factors, therefore, that will have positive impacts on developments in this field are the inclusion of web services, stability of funding and creation of user-friendly interfaces.
NASA has sponsored the creation of an orthorectified and geodetically accurate global land data set of Landsat Multispectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper data, from the 1970s, circa 1990, and circa 2000, respectively, to support a variety of scientific studies and educational purposes. This is the first time a geodetically accurate global compendium of orthorectified multi-epoch digital satellite data at the 30- to 80-m spatial scale spanning 30 years has been produced for use by the international scientific and educational communities. We describe data selection, orthorectification, accuracy, access, and other aspects of these data.
Satellite gaging reaches are polygonal land areas encompassing river and floodplain reaches where total surface water area expands and contracts as river discharge varies. Traditional gaging stations measure water level, or stage, as a surrogate for discharge. Such stations are commonly located where discharge changes are primarily accommodated by stage and not width changes. In contrast, we identify for testing purposes 191 gaging reaches distributed worldwide where multi-temporal remote sensing demonstrates significant water surface area variability. Typical reach lengths are 30 km and reach widths average 10-30 km. The gaging reaches are sufficiently wide to accommodate the largest floods. Measured water surface areas using the MODIS sensor 250 m resolution bands are converted to river characteristic widths (water area/river length) and, in the U.S., are compared to adjacent gaging station data. Preliminary results indicate that, along many U.S. gaging reaches, MODIS-derived widths are robust predictors of discharge. As is the case for in situ gaging stations, local width/discharge relations vary, and some reaches record discharge changes with greater precision than others. Time series of MODIS river characteristic widths can provide hydrologists and water resource managers with a geographically extensive and economical river monitoring capability
Forest fires in large sparsely populated areas in the boreal forest zone are difficult to detect by ground based means. Satellites can be a viable source of information to augment air-borne reconnaissance. The Advanced Very High Resolution Radiometer (AVHRR) sensor aboard the National Oceanic and Atmospheric Administration (NOAA) satellites has been used to detect and map fires in the past mainly in the tropics and mainly for environmental monitoring purposes. This article describes real-time forest fire detection where the aim is to inform local fire authorities on the fire. The fire detection is based on the 3.7 mu m channel of the NOAA AVHRR sensor. In the fire detection algorithm, imaging geometry is taken into account in addition to the data from the near-infrared and thermal infrared channels. In an experiment in summer 1995, 16 fires were detected in Finland. One was a forest fire, 11 were prescribed burnings and 4 false alarms. Three of the false alarms were due to steel factories. We conclude that satellite-based fire detection for fire control is feasible in the boreal forest zone if the continuous supply of frequent middle-infrared data can be guaranteed in the future.
This letter presents a block bundle adjustment process applied to four Ikonos Geo-product in-track images with few ground control points (GCPs) over a high mountainous area of Venezuela. Various configurations of block bundle adjustment (1-4 images and different GCPs) were evaluated with independent check points. Whatever the number of images, the block bundle adjustment results were consistent with planimetric errors of about @ 5 m to - 7 m. Part of this error was due to the error of the ground data. The evaluation of the final ortho-mosaic with 1:1000 vector lines gives an approximation of relative and absolute errors to be - 2 m and - 3 m to - 4 m with maximum errors of - 6 m and - 10 m, respectively.