Sarah Mubareka - Academia.edu (original) (raw)

Papers by Sarah Mubareka

Research paper thumbnail of A high resolution land use/cover modelling framework for Europe: introducing the EU-ClueScanner100 model

… Science and Its …, Jan 1, 2011

In this paper we introduce the new configuration of the EU-ClueScanner model (EUCS100) that is de... more In this paper we introduce the new configuration of the EU-ClueScanner model (EUCS100) that is designed for evaluating the impact of policy alternatives on the European territory at the high spatial resolution of 100 meters. The high resolution in combination with the vast extent of the model called for considerable reprogramming to optimize processing speed. In addition, the calibration of the model was revised to account for the fact that different spatial processes may be prominent at this more detailed resolution. This new configuration of EU-ClueScanner also differs from its predecessors in that it has increased functionalities which allow the modeller more flexibility. It is now possible to work with irregular regions of interest, composed of any configuration of NUTS 2 regions. The structure of the land allocation model allows it to act as a bridge for different sector and indicator models and has the capacity to connect Global and European scale to the local level of environmental impacts. The EUCS100 model is at the core of a European Land Use Modelling Platform that aims to produce policy-relevant information related to land use/cover dynamics.

Research paper thumbnail of Identification d'indicateurs de risque des populations victimes de conflits par imagerie satellitaire. Etude de cas: Le nord de l'Irak

Remote sensing and security, terms which are not usually associated, have found a common platform... more Remote sensing and security, terms which are not usually associated, have found a common platform this decade with the conjuring of the GMOSS network (Global Monitoring for Security and Stability), whose mandate is to discover new applications for satellite-derived imagery to security issues. This study focuses on human security, concentrating on the characterisation of vulnerable areas to conflict. A time-series of satellite imagery taken from Landsat sensors from 1987 to 2001 and the SRTM mission imagery are used for this purpose over a site in northern Iraq. Human security issues include the exposure to any type of hazard. The region of study is first characterised in order to understand which hazards are and were present in the past for the region of study. The principal hazard for the region of study is armed conflict and the relative field data was analysed to determine the links between geographical indicators and vulnerable areas. This is done through historical research and the study of open-sourced information about disease outbreaks; the movements of refugees and the internally displaced; and humanitarian aid and security issues. These open sources offer information which are not always consistent, objective, or normalized and are therefore difficult to quantify. A method for the rapid mapping and graphing and subsequent analysis of the situation in a region where limited information is available is developed. This information is coupled with population numbers to create a "risk map": A disaggregated matrix of areas most at risk during conflict situations. The results show that describing the risk factor for a population to the hazard conflict depends on three complex indicators: Population density, remoteness and economic diversity. Each of these complex indicators is then derived from Landsat and SRTM imagery and a satellite-driven model is formulated. This model based on satellite imagery is applied to the study site for a temporal study. The output are three 90 m x 90 m resolution grids which describe, at a pixel level, the risk level within the region for each of the dates studies, and the changes which occur in northern Iraq as the result of the Anfal Campaigns. Results show that satellite imagery, with a minimum of processing, can yield indicators for characterising risk in a region. Although by no means a replacement for field data, this technological source, in the absence of local knowledge, can provide users with a starting point in understanding which areas are most at risk within a region. If this data is coupled with open sourced information such as political and cultural discrimination, economy and agricultural practices, a fairly accurate risk map can be generated in the absence of field data. Keywords. SRTM, Landsat, risk indicators, Iraq, conflict, population vulnerability, segmentation, land-use, fuzzy-classification, atmospheric corrections.

Research paper thumbnail of A land-cover map for South and Southeast Asia derived from SPOT-VEGETATION data

Journal of Biogeography, 2007

Aim Our aim was to produce a uniform ‘regional’ land-cover map of South and Southeast Asia based... more Aim Our aim was to produce a uniform ‘regional’ land-cover map of South and Southeast Asia based on ‘sub-regional’ mapping results generated in the context of the Global Land Cover 2000 project.Location The ‘region’ of tropical and sub-tropical South and Southeast Asia stretches from the Himalayas and the southern border of China in the north, to Sri Lanka and Indonesia in the south, and from Pakistan in the west to the islands of New Guinea in the far east.Methods The regional land-cover map is based on sub-regional digital mapping results derived from SPOT-VEGETATION satellite data for the years 1998–2000. Image processing, digital classification and thematic mapping were performed separately for the three sub-regions of South Asia, continental Southeast Asia, and insular Southeast Asia. Landsat TM images, field data and existing national maps served as references. We used the FAO (Food and Agriculture Organization) Land Cover Classification System (LCCS) for coding the sub-regional land-cover classes and for aggregating the latter to a uniform regional legend. A validation was performed based on a systematic grid of sample points, referring to visual interpretation from high-resolution Landsat imagery. Regional land-cover area estimates were obtained and compared with FAO statistics for the categories ‘forest’ and ‘cropland’.Results The regional map displays 26 land-cover classes. The LCCS coding provided a standardized class description, independent from local class names; it also allowed us to maintain the link to the detailed sub-regional land-cover classes. The validation of the map displayed a mapping accuracy of 72% for the dominant classes of ‘forest’ and ‘cropland’; regional area estimates for these classes correspond reasonably well to existing regional statistics.Main conclusions The land-cover map of South and Southeast Asia provides a synoptic view of the distribution of land cover of tropical and sub-tropical Asia, and it delivers reasonable thematic detail and quantitative estimates of the main land-cover proportions. The map may therefore serve for regional stratification or modelling of vegetation cover, but could also support the implementation of forest policies, watershed management or conservation strategies at regional scales.

Research paper thumbnail of A High Resolution Land Use/Cover Modelling Framework for Europe: Introducing the EU-ClueScanner100 Model

In this paper we introduce the new configuration of the EU-ClueScanner model (EUCS100) that is de... more In this paper we introduce the new configuration of the EU-ClueScanner model (EUCS100) that is designed for evaluating the impact of policy alternatives on the European territory at the high spatial resolution of 100 meters. The high resolution in combination with the vast extent of the model called for considerable reprogramming to optimize processing speed. In addition, the calibration of the model was revised to account for the fact that different spatial processes may be prominent at this more detailed resolution. This new configuration of EU-ClueScanner also differs from its predecessors in that it has increased functionalities which allow the modeller more flexibility. It is now possible to work with irregular regions of interest, composed of any configuration of NUTS 2 regions. The structure of the land allocation model allows it to act as a bridge for different sector and indicator models and has the capacity to connect Global and European scale to the local level of environmental impacts. The EUCS100 model is at the core of a European Land Use Modelling Platform that aims to produce policy-relevant information related to land use/cover dynamics.

Research paper thumbnail of Development of a composite index of urban compactness for land use modelling applications

Landscape and Urban Planning, 2011

This paper introduces a composite index to characterise urban expansion patterns based on four as... more This paper introduces a composite index to characterise urban expansion patterns based on four associated indices that describe the degree of compactness of urban land: nuclearity, ribbon development, leapfrogging and branching processes. Subsequently, principal component and cluster analysis are applied to build the composite index. Two baseline scenarios and three hypothetical policy alternatives, run from 2000 to 2030 using the pan-European EU-ClueScanner 1 km resolution land use model are then used to test the sensitivity and robustness of the composite index in large urban zones (LUZs).

Research paper thumbnail of Identifying and modelling environmental indicators for assessing population vulnerability to conflict using ground and satellite data

Ecological Indicators, 2010

Research paper thumbnail of Standardising and mapping open-source information for crisis regions: the case of post-conflict Iraq

Disasters, 2005

Painting an accurate picture of the situation on the ground in countries in crisis is vital for t... more Painting an accurate picture of the situation on the ground in countries in crisis is vital for the efficiency of humanitarian aid and reconstruction agencies. This study describes a method for standardising and mapping the plethora of open-source information. The test site for the study is post-conflict Iraq. Important information on aid distribution, reconstruction and security in Iraq can be derived from the reports of humanitarian aid agencies and the media, before being formatted, inserted into a database and mapped. The product is a visual, cartographic structure of otherwise random information, showing which organisations are working in the country, which thematic and geographic areas are being prioritised in the field, and which areas most frequently experience security events. This type of mapping not only highlights the overall working environment within different parts of the country, but it may also serve as a decision-making tool for donors and humanitarian aid agencies planning to deploy personnel. 5 Although governorates in Iraq are sometimes also referred to as provinces, the term governorate is more commonly used and hence will be applied throughout this text.

Research paper thumbnail of Settlement location and population density estimation in rugged terrain using information derived from Landsat ETM and SRTM data

International Journal of Remote Sensing, 2008

It is useful to have a disaggregated population database at uniform grid units in disaster situat... more It is useful to have a disaggregated population database at uniform grid units in disaster situations. This study presents a method for settlement location probability and population density estimations at a 90 m resolution for northern Iraq using the Shuttle Radar Topographic Mission (SRTM) digital terrain model and Landsat Enhanced Thematic Mapper satellite imagery. A spatial model each for calculating the probability of settlement location and for estimating population density is described. A randomly selected subset of field data (equivalent to 50%) is first analysed for statistical links between settlement location probability and population density; and various biophysical features which are extracted from Landsat or SRTM data. The model is calibrated using this subset. Settlement location probability is attributed to the distance from roads and water bodies and land cover. Population density can be estimated based upon land cover and topographic features. The Landsat data are processed using a segmentation and subsequent feature–based classification approach making this method robust to seasonal variations in imagery and therefore applicable to a time series of images regardless of acquisition date. The second half of the field data is used to validate the model. Results show a reasonable estimate of population numbers (r = 0.205, p<0.001) for both rural and urban settlements. Although there is a strong overall correlation between the results of this and the LandScan model (r = 0.464, p<0.001), this method performs better than the 1 km resolution LandScan grid for settlements with fewer than 1000 people, but is less accurate for estimating population numbers in urban areas (LandScan rural r = 0.181, p<0.001; LandScan urban r = 0.303, p<0.001). The correlation between true urban population numbers is superior to that of LandScan however when the 90 m grid values are summed using a filter which corresponds to the LandScan spatial resolution (r = 0.318, p<0.001).

Research paper thumbnail of A high resolution land use/cover modelling framework for Europe: introducing the EU-ClueScanner100 model

… Science and Its …, Jan 1, 2011

In this paper we introduce the new configuration of the EU-ClueScanner model (EUCS100) that is de... more In this paper we introduce the new configuration of the EU-ClueScanner model (EUCS100) that is designed for evaluating the impact of policy alternatives on the European territory at the high spatial resolution of 100 meters. The high resolution in combination with the vast extent of the model called for considerable reprogramming to optimize processing speed. In addition, the calibration of the model was revised to account for the fact that different spatial processes may be prominent at this more detailed resolution. This new configuration of EU-ClueScanner also differs from its predecessors in that it has increased functionalities which allow the modeller more flexibility. It is now possible to work with irregular regions of interest, composed of any configuration of NUTS 2 regions. The structure of the land allocation model allows it to act as a bridge for different sector and indicator models and has the capacity to connect Global and European scale to the local level of environmental impacts. The EUCS100 model is at the core of a European Land Use Modelling Platform that aims to produce policy-relevant information related to land use/cover dynamics.

Research paper thumbnail of Identification d'indicateurs de risque des populations victimes de conflits par imagerie satellitaire. Etude de cas: Le nord de l'Irak

Remote sensing and security, terms which are not usually associated, have found a common platform... more Remote sensing and security, terms which are not usually associated, have found a common platform this decade with the conjuring of the GMOSS network (Global Monitoring for Security and Stability), whose mandate is to discover new applications for satellite-derived imagery to security issues. This study focuses on human security, concentrating on the characterisation of vulnerable areas to conflict. A time-series of satellite imagery taken from Landsat sensors from 1987 to 2001 and the SRTM mission imagery are used for this purpose over a site in northern Iraq. Human security issues include the exposure to any type of hazard. The region of study is first characterised in order to understand which hazards are and were present in the past for the region of study. The principal hazard for the region of study is armed conflict and the relative field data was analysed to determine the links between geographical indicators and vulnerable areas. This is done through historical research and the study of open-sourced information about disease outbreaks; the movements of refugees and the internally displaced; and humanitarian aid and security issues. These open sources offer information which are not always consistent, objective, or normalized and are therefore difficult to quantify. A method for the rapid mapping and graphing and subsequent analysis of the situation in a region where limited information is available is developed. This information is coupled with population numbers to create a "risk map": A disaggregated matrix of areas most at risk during conflict situations. The results show that describing the risk factor for a population to the hazard conflict depends on three complex indicators: Population density, remoteness and economic diversity. Each of these complex indicators is then derived from Landsat and SRTM imagery and a satellite-driven model is formulated. This model based on satellite imagery is applied to the study site for a temporal study. The output are three 90 m x 90 m resolution grids which describe, at a pixel level, the risk level within the region for each of the dates studies, and the changes which occur in northern Iraq as the result of the Anfal Campaigns. Results show that satellite imagery, with a minimum of processing, can yield indicators for characterising risk in a region. Although by no means a replacement for field data, this technological source, in the absence of local knowledge, can provide users with a starting point in understanding which areas are most at risk within a region. If this data is coupled with open sourced information such as political and cultural discrimination, economy and agricultural practices, a fairly accurate risk map can be generated in the absence of field data. Keywords. SRTM, Landsat, risk indicators, Iraq, conflict, population vulnerability, segmentation, land-use, fuzzy-classification, atmospheric corrections.

Research paper thumbnail of A land-cover map for South and Southeast Asia derived from SPOT-VEGETATION data

Journal of Biogeography, 2007

Aim Our aim was to produce a uniform ‘regional’ land-cover map of South and Southeast Asia based... more Aim Our aim was to produce a uniform ‘regional’ land-cover map of South and Southeast Asia based on ‘sub-regional’ mapping results generated in the context of the Global Land Cover 2000 project.Location The ‘region’ of tropical and sub-tropical South and Southeast Asia stretches from the Himalayas and the southern border of China in the north, to Sri Lanka and Indonesia in the south, and from Pakistan in the west to the islands of New Guinea in the far east.Methods The regional land-cover map is based on sub-regional digital mapping results derived from SPOT-VEGETATION satellite data for the years 1998–2000. Image processing, digital classification and thematic mapping were performed separately for the three sub-regions of South Asia, continental Southeast Asia, and insular Southeast Asia. Landsat TM images, field data and existing national maps served as references. We used the FAO (Food and Agriculture Organization) Land Cover Classification System (LCCS) for coding the sub-regional land-cover classes and for aggregating the latter to a uniform regional legend. A validation was performed based on a systematic grid of sample points, referring to visual interpretation from high-resolution Landsat imagery. Regional land-cover area estimates were obtained and compared with FAO statistics for the categories ‘forest’ and ‘cropland’.Results The regional map displays 26 land-cover classes. The LCCS coding provided a standardized class description, independent from local class names; it also allowed us to maintain the link to the detailed sub-regional land-cover classes. The validation of the map displayed a mapping accuracy of 72% for the dominant classes of ‘forest’ and ‘cropland’; regional area estimates for these classes correspond reasonably well to existing regional statistics.Main conclusions The land-cover map of South and Southeast Asia provides a synoptic view of the distribution of land cover of tropical and sub-tropical Asia, and it delivers reasonable thematic detail and quantitative estimates of the main land-cover proportions. The map may therefore serve for regional stratification or modelling of vegetation cover, but could also support the implementation of forest policies, watershed management or conservation strategies at regional scales.

Research paper thumbnail of A High Resolution Land Use/Cover Modelling Framework for Europe: Introducing the EU-ClueScanner100 Model

In this paper we introduce the new configuration of the EU-ClueScanner model (EUCS100) that is de... more In this paper we introduce the new configuration of the EU-ClueScanner model (EUCS100) that is designed for evaluating the impact of policy alternatives on the European territory at the high spatial resolution of 100 meters. The high resolution in combination with the vast extent of the model called for considerable reprogramming to optimize processing speed. In addition, the calibration of the model was revised to account for the fact that different spatial processes may be prominent at this more detailed resolution. This new configuration of EU-ClueScanner also differs from its predecessors in that it has increased functionalities which allow the modeller more flexibility. It is now possible to work with irregular regions of interest, composed of any configuration of NUTS 2 regions. The structure of the land allocation model allows it to act as a bridge for different sector and indicator models and has the capacity to connect Global and European scale to the local level of environmental impacts. The EUCS100 model is at the core of a European Land Use Modelling Platform that aims to produce policy-relevant information related to land use/cover dynamics.

Research paper thumbnail of Development of a composite index of urban compactness for land use modelling applications

Landscape and Urban Planning, 2011

This paper introduces a composite index to characterise urban expansion patterns based on four as... more This paper introduces a composite index to characterise urban expansion patterns based on four associated indices that describe the degree of compactness of urban land: nuclearity, ribbon development, leapfrogging and branching processes. Subsequently, principal component and cluster analysis are applied to build the composite index. Two baseline scenarios and three hypothetical policy alternatives, run from 2000 to 2030 using the pan-European EU-ClueScanner 1 km resolution land use model are then used to test the sensitivity and robustness of the composite index in large urban zones (LUZs).

Research paper thumbnail of Identifying and modelling environmental indicators for assessing population vulnerability to conflict using ground and satellite data

Ecological Indicators, 2010

Research paper thumbnail of Standardising and mapping open-source information for crisis regions: the case of post-conflict Iraq

Disasters, 2005

Painting an accurate picture of the situation on the ground in countries in crisis is vital for t... more Painting an accurate picture of the situation on the ground in countries in crisis is vital for the efficiency of humanitarian aid and reconstruction agencies. This study describes a method for standardising and mapping the plethora of open-source information. The test site for the study is post-conflict Iraq. Important information on aid distribution, reconstruction and security in Iraq can be derived from the reports of humanitarian aid agencies and the media, before being formatted, inserted into a database and mapped. The product is a visual, cartographic structure of otherwise random information, showing which organisations are working in the country, which thematic and geographic areas are being prioritised in the field, and which areas most frequently experience security events. This type of mapping not only highlights the overall working environment within different parts of the country, but it may also serve as a decision-making tool for donors and humanitarian aid agencies planning to deploy personnel. 5 Although governorates in Iraq are sometimes also referred to as provinces, the term governorate is more commonly used and hence will be applied throughout this text.

Research paper thumbnail of Settlement location and population density estimation in rugged terrain using information derived from Landsat ETM and SRTM data

International Journal of Remote Sensing, 2008

It is useful to have a disaggregated population database at uniform grid units in disaster situat... more It is useful to have a disaggregated population database at uniform grid units in disaster situations. This study presents a method for settlement location probability and population density estimations at a 90 m resolution for northern Iraq using the Shuttle Radar Topographic Mission (SRTM) digital terrain model and Landsat Enhanced Thematic Mapper satellite imagery. A spatial model each for calculating the probability of settlement location and for estimating population density is described. A randomly selected subset of field data (equivalent to 50%) is first analysed for statistical links between settlement location probability and population density; and various biophysical features which are extracted from Landsat or SRTM data. The model is calibrated using this subset. Settlement location probability is attributed to the distance from roads and water bodies and land cover. Population density can be estimated based upon land cover and topographic features. The Landsat data are processed using a segmentation and subsequent feature–based classification approach making this method robust to seasonal variations in imagery and therefore applicable to a time series of images regardless of acquisition date. The second half of the field data is used to validate the model. Results show a reasonable estimate of population numbers (r = 0.205, p<0.001) for both rural and urban settlements. Although there is a strong overall correlation between the results of this and the LandScan model (r = 0.464, p<0.001), this method performs better than the 1 km resolution LandScan grid for settlements with fewer than 1000 people, but is less accurate for estimating population numbers in urban areas (LandScan rural r = 0.181, p<0.001; LandScan urban r = 0.303, p<0.001). The correlation between true urban population numbers is superior to that of LandScan however when the 90 m grid values are summed using a filter which corresponds to the LandScan spatial resolution (r = 0.318, p<0.001).