International Archives of the Photogrammetry, 
  
The pipeline monitoring system is structured into four main 
system components: 
|. The Pipeline Operator System (POS), which is the part of the 
monitoring system which 1s used by the pipeline operator for 
delivery and handling of alarms and for specifying the 
monitoring characteristics for different parts of the pipeline 
network. 
2. The Pipeline Information Management System (PIMS), which 
stores all relevant information on the pipeline network, the 
environment around it, and the integrity monitoring and 
which provides analyses and scheduling functionalities for the 
pipeline operator. The PIMS also includes an alarm 
production system, which decides what hazards should be 
considered as alarms. 
3 The Hazard Extraction System (HES), which extracts the 
hazard report information out of the basic remote sensing 
imagery layers, using advanced image interpretation 
techniques. 
4.The Imagery Collection System (ICS), which collects the 
required remote sensing imagery with a suit of both 
spaceborne and airborne platforms and different types of 
sensors. conform the monitoring priorities. In the ICS all 
these means are scheduled optimally conform the specified 
priorities of the pipeline operators and the weather and season 
conditions. Here also the data are pre-processed to remote 
sensing basic imagery layers. 
The four components in principle can be independent of each 
other so that maximal flexibility exists. Also each system 
component in itself is set up as much as possible in a modular 
and flexible way. By doing this, new technologies on sensors, 
platforms, data processing, data storage and transfer can be 
integrated and the system easily can be extended to other 
operators or areas. 
3. HIGH RESOLUTION OPTICAL SATELLITE 
OBSERVATION 
This required flexibility also holds for the Information 
Collection System. Given the high costs of the imagery 
collection part, optimisation is essential for the overall 
feasibility of the pipeline monitoring system. 
Clear is that the ICS will be a hybrid system consisting of 
different type of sensors and platforms (both commercial 
available services and/or own operated dedicated systems) 
complementing each other for different areas (network density, 
cloud coverage and light conditions in northern regions) and 
different conditions (cloud coverage, snow and vegetation 
coverage etc.). The required flexibility of the imagery collection 
is also related to the flexibility of the Hazard Extraction System 
to combine different types of imagery layers in the extraction 
process, see also (Dekker, 2004) 
In the Information Collection System optical satellites will play 
an essential role given the high spatial resolution and good 
interpretation capabilities. Limitation of optical systems 
however is the dependence on weather conditions, especially 
cloud cover. Therefore in this study special attention is given to 
the capacity and effectiveness of the high resolution optical 
satellite component of the ICS. 
Within PRESENSE the National Aerospace Laboratory NLR 
performs a study to the optimisation of the high-resolution 
optical satellite constellation as part of the data acquisition 
system. The extent and effectiveness of a constellation of 
optical satellites is analysed and simulated in relation to the 
Remote Sensing and Spatial Inf 
ormation Sciences, Vol XXXV, Part B7. Istanbul 2004 
    
   
  
orbit configuration, the sensor/platform capabilities (swath, 
pointing), the form of the network, light/season conditions and 
the relation with the other sensors and platforms. Special 
attention is given to minimise the negative impact of cloud 
cover on the effectiveness of the system by using the pointing 
capability of the system to actively select cloud-free areas in 
combination with intelligent tasking based on actual cloud 
information. 
4. CLIMAS SIM ULATOR 
For the analyses the Cloud Impact and Avoidance Simulator 
(CLIMAS), as in development at NLR, is used to support the 
analyses (Algra, 2004). An overview of the CLIMAS simulator 
Target 0B : 
generator 
r sensor 
is shown in figure-2. 
Targets pre- 
interface interface : 
processing 
interface 
Kernel 
1Kernel 
Observation Results 
result calculation DB 
User interface and display / store 
Figure-2: CLIMAS architecture 
Various types of missions can be simulated, including 
constellations, with and without cloud avoidance scheduling. 
Satellite orbit parameters and instrument parameters can freely 
be chosen. Target area information can be imported from a 
GeoTIFF file, or from XML-format file in which target areas 
are described and observation priorities can be specified. Other 
major simulation input parameters are the maximum accepted 
cloud percentage in a target area and the minimal required solar 
elevation. As a result of a simulation run, CLIMAS generates a 
data file with for all targets the times of imaging request and the 
actual time(s) of capturing. CLIMAS supports statistical 
analysis of this information: e.g. average delivery time, effective 
revisit time, distribution of delivery times, number of targets 
successfully recorded per month, etc. CLIMAS uses the global 
CHANCES cloud database which is derived from real satellite 
data with high spatial and temporal resolution. The spatial 
resolution is 5x5km and the temporal resolution is one hour 
(Haar, 1995). 
Different types of satellite tasking strategies can be 
implemented for simulation. The simulator allows the user to 
define target areas in any Area Of Interest (AOT). For each pass 
over the Area Of Interest the area is divided into rectangular 
sub-areas called strips, with a width equal to the swath of the 
optical sensor. The length in along-track direction is an 
independent input parameter. Basically, with two-dimensional 
pointing, after each strip any other strip in the AOI can be 
imaged. However, the order and number of imaged strips are 
limited by a set of constraints such as slew time, across track 
and along track pointing capabilities, the simulated time 
dependent satellite position, and the locations of candidate 
strips within the AOI. 
Whether a strip is put on the task schedule depends on the 
number of target area elements it contains, the priority and 
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