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Daniele Ehrlich's Profile
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Ispra
Italy
Ispra
Italy
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Daniele Ehrlich's Profile
Profile Information
OrganizationJoint Research Centre of the European Commission
Position
Senior Researcher
Phone
+39 0332 78 9384
Areas of Interest
Disaster risk, mapping built up areas, quantifying building stock from Earth Observation
Bio
Daniele Ehrlich holds a B.S. in Forestry from the University of Padua, Italy (1984) and a Master’s (1989) and PH.D. (1992) in Geography from the University of California, at Santa Barbara, USA. He was awarded a post doctoral position with UNEP/GRID North American node at USGS Sioux Falls to address global land cover mapping, and he also worked as a remote sensing consultant in Southern California (1993). In 1994 he joined the Joint Research Centre (JRC) in Italy as a postdoctoral researcher and was subsequently awarded a position as a Scientific Technical Officer. At the JRC he established a research group focusing on developing methods and generating information products for the humanitarian community at large and the European Commission services that address international crises. Since 2001 he has been a senior staff member at the Institute for the Protection and Security of the Citizen at the JRC. In his scientific and professional career he has used optical Earth Observation data for crop area estimations, tropical deforestation assessments, land cover changes at broad spatial scales, refugee camp mapping, and more recently, post disaster damage assessments. His current focus of research is on the use of VHR imagery for settlement mapping and geo-spatial technologies for disaster risk modelling.
Comment wall (16 comments)
Hello Daniele,
Thanks for your comments. I am Indonesian student, who study for my Phd in Yokohama National University. My research is about evaluation on urban fires and evacuation planning, mostly i use GIS for my research.
Nice to meet you, Daniele
Rizka
Test. Comment back
For the study on social vulnerability (SV) we used Quickbird data (pan + ms, pansharpened), as well as Resourcesat MS (5.8 m res), deriving SV values per neighborhood. Previously we did a study also in Tegucigalpa to map urban elements at risk using object-oriented analysis, using also Quickbird and 1.5 m resolution lidar data (a gridded DSM). The result was buildings footprints and outlines of informal settlements. When we integrated lidar we got individual buildings and their heights. For our Delhi work we use also use pansharpened Quickbird and are aiming at automatic delineation of so-called homogenous urban patches (HUPs), with a particular focus on slum/deprived area delineation.
________
Daniele Ehrlich added a comment to your profile on Understanding Risk:
Hi Norman,
In your work what type of satellite imagery have you used to map exposure?
What type of exposure datasets have you derived.
If you can please reply in comment section.
Daniele
Hi Daniele,
In the case of CAPRA for urban risk asessments we use, for exposure, shape files developed based on cadastral information avalable of the cities or other spatial units (aggregated, such as blocks, districts, municipalities, etc.) based on a proxy (from demographic, social indicators). In the wiki of CAPRA also is avalable a set of graphic tools to define expose elements (point, line, polygone) on Google (also there is a tool for the NASA World Wind) and the possibility to gather information (including pictures) with Blackberry phones.
Only, some words more... perhaps the problem is not the identification of the assets at risk or how to represent them, for example, building by building as polygones, or a lifelines as segments. I think that in risk modeling the main problem is how to assing the vulnerability functions to each asset or identify them. Remote sensing are indeed useful and perhaps necessary but it is not enough and it is a challenge to resolve with the current technology (fuzzy logic, image recongnition, neural networks, etc.) because the expert knowledge is necessary in any case. How to know about the foundation of a building (key data for earthquake behaviour of buildings) using remote sensing?
Please give me your email address.
Best,
Sergio
Hi Daniele, I hope you can get a great interchange related to exposure and how to capture, characterize and value the element at risk. It will be very useful also for CAPRA and one of the big problems at present, as you know. Then, we will be very eager to know advances and possibilities in june. All the best.
Hi Daniele, thank you for your message and question. I can see with the messages posted here below that you already got some good feedback regarding the use of EO for measuring exposure and risks. The topic is of course very broad and dependent on the scale of work. In the context of the VRAM program we are looking at population and health infrastructures/services. When it comes to population, some of the dataset we are using to predict hazard, vulnerability and risks at 1km resolution are derived from satellite images (e.g. roads, rivers) but we are not performing the extraction ourselves, we use secondary data. When it comes to infrastructures we would love to be able to automatically locate health facilities from satellite images but we are unfortunately not there yet, most of the dataset we use have therefore been created using GPS devices (another space based technology…). Best regards, Steeve
Hi Daniele, no problem for you to use my comments in the discussion group you will lead. Just one addition to what I said, it might indeed be difficult to automatically identify health facilities but EO would definitively help for collecting these locations through crowd mapping exercises for example and/or validating the data collected in the filed using GPS. Cheers, Steeve
Hi Daniele, thank you for your message and question. I can see with the messages posted here below that you already got some good feedback regarding the use of EO for measuring exposure and risks. The topic is of course very broad and dependent on the scale of work. In the context of the VRAM program we are looking at population and health infrastructures/services. When it comes to population, some of the dataset we are using to predict hazard, vulnerability and risks at 1km resolution are derived from satellite images (e.g. roads, rivers) but we are not performing the extraction ourselves, we use secondary data. When it comes to infrastructures we would love to be able to automatically locate health facilities from satellite images but we are unfortunately not there yet, most of the dataset we use have therefore been created using GPS devices (another space based technology…). Best regards, Steeve
Hi Daniele, I hope you can get a great interchange related to exposure and how to capture, characterize and value the element at risk. It will be very useful also for CAPRA and one of the big problems at present, as you know. Then, we will be very eager to know advances and possibilities in june. All the best.
Please give me your email address.
Best,
Sergio
Only, some words more... perhaps the problem is not the identification of the assets at risk or how to represent them, for example, building by building as polygones, or a lifelines as segments. I think that in risk modeling the main problem is how to assing the vulnerability functions to each asset or identify them. Remote sensing are indeed useful and perhaps necessary but it is not enough and it is a challenge to resolve with the current technology (fuzzy logic, image recongnition, neural networks, etc.) because the expert knowledge is necessary in any case. How to know about the foundation of a building (key data for earthquake behaviour of buildings) using remote sensing?
Hi Daniele,
In the case of CAPRA for urban risk asessments we use, for exposure, shape files developed based on cadastral information avalable of the cities or other spatial units (aggregated, such as blocks, districts, municipalities, etc.) based on a proxy (from demographic, social indicators). In the wiki of CAPRA also is avalable a set of graphic tools to define expose elements (point, line, polygone) on Google (also there is a tool for the NASA World Wind) and the possibility to gather information (including pictures) with Blackberry phones.
For the study on social vulnerability (SV) we used Quickbird data (pan + ms, pansharpened), as well as Resourcesat MS (5.8 m res), deriving SV values per neighborhood. Previously we did a study also in Tegucigalpa to map urban elements at risk using object-oriented analysis, using also Quickbird and 1.5 m resolution lidar data (a gridded DSM). The result was buildings footprints and outlines of informal settlements. When we integrated lidar we got individual buildings and their heights. For our Delhi work we use also use pansharpened Quickbird and are aiming at automatic delineation of so-called homogenous urban patches (HUPs), with a particular focus on slum/deprived area delineation.
________
Daniele Ehrlich added a comment to your profile on Understanding Risk:
Hi Norman,
In your work what type of satellite imagery have you used to map exposure?
What type of exposure datasets have you derived.
If you can please reply in comment section.
Daniele
Test. Comment back