24 results on '"Kerle, Norman"'
Search Results
2. Evaluation from the Bird's-Eye View: Innovative Use of Remote Sensing Techniques
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Leppert, Gerald, Lech, Malte, Ghaffarian, Saman, Kerle, Norman, and Deutsches Evaluierungsinstitut der Entwicklungszusammenarbeit (DEval)
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development policy ,evaluation ,Entwicklungspolitik ,international cooperation ,methodology ,Krisenmanagement ,Internationale Beziehungen ,Methodik ,International Relations, International Politics, Foreign Affairs, Development Policy ,internationale Zusammenarbeit ,Development Cooperation ,Remote Sensing ,Messung ,measurement ,International relations ,internationale Beziehungen, Entwicklungspolitik ,crisis management (econ., pol.) ,ddc:327 - Abstract
A systematic use of Remote Sensing (RS) data opens a door for evaluators to better address evaluation questions by adding a spatial dimension. This policy brief highlights DEval’s methodological approach to the analysis of high-resolution RS data through the application of image classification and machinelearning (ML) techniques. DEval has been developing this approach in close cooperation with RS experts from the Faculty of Geo-information Science and Earth Observation (ITC) at the University of Twente in the Netherlands.
- Published
- 2022
3. A Proof-of-Concept of Integrating Machine Learning, Remote Sensing, and Survey Data in Evaluations: The Measurement of Disaster Resilience in the Philippines
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Lech, Malte, Ghaffarian, Saman, Kerle, Norman, Leppert, Gerald, Nawrotzki, Raphael, Moull, Kevin, Harten, Sven, and Deutsches Evaluierungsinstitut der Entwicklungszusammenarbeit (DEval)
- Subjects
Klimawandel ,algorithm ,natural disaster ,Ecology ,remote sensing ,machine learning ,Fernerkundung ,Philippines ,socioeconomic factors ,Krisenmanagement ,Ecology, Environment ,risk management ,Southeast Asia ,Ökologie und Umwelt ,Philippinen ,sozioökonomische Faktoren ,Risikomanagement ,data capture ,Algorithmus ,Südostasien ,climate change ,Naturkatastrophe ,Ökologie ,ddc:577 ,Datengewinnung ,crisis management (econ., pol.) - Abstract
Disaster resilience is a topic of increasing importance for policy makers in the context of climate change. However, measuring disaster resilience remains a challenge as it requires information on both the physical environment and socio-economic dimensions. In this study we developed and tested a method to use remote sensing (RS) data to construct proxy indicators of socio-economic change. We employed machine-learning algorithms to generate land-cover and land-use classifications from very high-resolution satellite imagery to appraise disaster damage and recovery processes in the Philippines following the devastation of typhoon Haiyan in November 2013. We constructed RS-based proxy indicators for N=20 barangays (villages) in the region surrounding Tacloban City in the central east of the Philippines. We then combined the RS-based proxy indicators with detailed socio-economic information collected during a rigorous-impact evaluation by DEval in 2016. Results from a statistical analysis demonstrated that fastest post-disaster recovery occurred in urban barangays that received sufficient government support (subsidies), and which had no prior disaster experience. In general, socio-demographic factors had stronger effects on the early recovery phase (0-2 years) compared to the late recovery phase (2-3 years). German development support was related to recovery performance only to some extent. Rather than providing an in-depth statistical analysis, this study is intended as a proof-of-concept. We have been able to demonstrate that high-resolution RS data and machine-learning techniques can be used within a mixed-methods design as an effective tool to evaluate disaster impacts and recovery processes. While RS data have distinct limitations (e.g., cost, labour intensity), they offer unique opportunities to objectively measure physical, and by extension socio-economic, changes over large areas and long time-scales. Zunehmende Wetterextreme und Naturkatastrophen sind Folgen des Klimawandels. Aufgrund dieser steigenden Risiken rückt die Resilienz der Bevölkerung im Katastrophenfall als zentrales Thema in den Vordergrund und hat zunehmende Bedeutung für politische Entscheidungstragende. Dennoch bleibt die Messung des mehrdimensionalen Konzepts der Katastrophenresilienz eine Herausforderung, da sie Informationen sowohl über die physische Umgebung als auch sozioökonomische Faktoren erfordert. In dieser Studie wird eine Methode entwickelt, um aus Fernerkundungsdaten (RS-Daten) Indikatoren zu entwickeln, die Aspekte des sozioökonomischen Wandels approximieren und somit messbar machen (Proxy-Indikatoren). Zu diesem Zweck wurden Algorithmen des maschinellen Lernens eingesetzt. Mit Hilfe dieser Algorithmen wurden aus hochauflösenden Satellitenbildern Klassifizierungen für Landstruktur und Landnutzung konstruiert, um Katastrophenschäden und iederaufbauprozesse auf den Philippinen nach der Zerstörung durch den Taifun Haiyan im November 2013 zu messen. Aus den RS-Daten wurden die Indikatoren für N=20 Barangays (Dörfer) in der Region um die Stadt Tacloban im zentralen Osten der Philippinen berechnet. Diese auf RS-Daten basierenden Indikatoren wurden mit detaillierten sozioökonomischen Informationen kombiniert, die für eine DEval-Evaluierung im Jahr 2016 erhoben wurden. Die Ergebnisse der statistischen Analyse zeigen, dass der schnellste Wiederaufbau nach der Katastrophe in städtischen Barangays zu beobachten war, die ausreichend staatliche Unterstützung (Subventionen) erhielten und über keine Katastrophenerfahrung verfügten. Im Vergleich hatten soziodemografische Faktoren allgemein stärkere Auswirkungen auf die frühe (0-2 Jahre) als auf die spätere (2-3 Jahre) Wiederaufbauphase. Es konnte nur ein bedingter Bezug zwischen der deutschen Entwicklungszusammenarbeit und den Wiederaufbauerfolgen festgestellt werden. Diese Studie versteht sich als Nachweis der Machbarkeit, weniger als detaillierte statistische Analyse. Sie belegt, dass hochauflösende RS-Daten und Techniken des maschinellen Lernens innerhalb eines integrierten Methodendesigns als effektives Werkzeug zur Bewertung von Katastrophenauswirkungen und Wiederherstellungsprozessen eingesetzt werden können. Trotz spezifischer Einschränkungen (hohe Kosten, Arbeitsintensität etc.) bieten RS-Daten einzigartige Möglichkeiten sowohl Umweltbedingungen als auch sozioökonomische Veränderungen über große Gebiete und lange Zeiträume hinweg objektiv messen zu können.
- Published
- 2020
4. Post-Disaster Recovery Monitoring with Google Earth Engine.
- Author
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Ghaffarian, Saman, Rezaie Farhadabad, Ali, and Kerle, Norman
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LAND cover ,DISASTER relief ,REMOTE-sensing images ,CLOUD computing ,SURFACE of the earth ,LAND use mapping ,REMOTE sensing - Abstract
Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal (pre-disaster), or even improved, conditions. Hence, it is essential to use methods to understand the dynamics/variables of the post-disaster recovery process, and rapid and cost-effective data and tools to monitor the process. Google Earth Engine (GEE) provides free access to vast amounts of remote sensing (RS) data and a powerful computing environment in a cloud platform, making it an attractive tool to analyze earth surface data. In this study we assessed the suitability of GEE to analyze and track recovery. To do so, we employed GEE to assess the recovery process over a three-year period after Typhoon Haiyan, which struck Leyte island, in the Philippines, in 2013. We developed an approach to (i) generate cloud and shadow-free image composites from Landsat 7 and 8 satellite imagery and produce land cover classification data using the Random Forest method, and (ii) generate damage and recovery maps based on post-classification change analysis. The method produced land cover maps with accuracies >88%. We used the model to produce damage and three time-step recovery maps for 62 municipalities on Leyte island. The results showed that most of the municipalities had recovered after three years in terms of returning to the pre-disaster situation based on the selected land cover change analysis. However, more analysis (e.g., functional assessment) based on detailed data (e.g., land use maps) is needed to evaluate the more complex and subtle socio-economic aspects of the recovery. The study showed that GEE has good potential for monitoring the recovery process for extensive regions. However, the most important limitation is the lack of very-high-resolution RS data that are critical to assess the process in detail, in particular in complex urban environments. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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5. Usability of aerial video footage for 3D-scene reconstruction and structural damage assessment.
- Author
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Cusicanqui, Johnny, Kerle, Norman, and Nex, Francesco
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REMOTE sensing ,AERIAL videography - Abstract
Remote sensing has evolved into the most efficient approach to assess post-disaster structural damage, in extensively affected areas through the use of space-borne data. For smaller, and in particular, complex urban disaster scenes, multi-perspective aerial imagery obtained with Unmanned Aerial Vehicles and derived dense colour 3D-models are increasingly being used. These type of data allow the direct and automated recognition of damage-related features, supporting an effective post-disaster structural damage assessment. However, the rapid collection and sharing of multi-perspective aerial imagery is still limited due to tight or lacking regulations and legal frameworks. A potential alternative is aerial video footage, typically acquired and shared by civil protection institutions or news media, and which tend to be the first type of airborne data available. Nevertheless, inherent artifacts and the lack of suitable processing means, have long limited its potential use in structural damage assessment and other post-disaster activities. In this research the usability of modern aerial video data was evaluated based on a comparative quality and application analysis of video data and multi-perspective imagery (photos), and their derivative 3D point clouds created using current photogrammetric techniques. Additionally, the effects of external factors, such as topography and the presence of smoke and moving objects were determined by analyzing two different earthquake-affected sites: Tainan (Taiwan) and Pescara del Tronto (Italy). Results demonstrated similar usabilities for video and photos. This is shown by the short 2cm of difference between the accuracies of video and photo-based 3D Point clouds. Despite the low video resolution, the usability of this data was compensated by a small ground sampling distance. Instead of video characteristics, low quality and application resulted from non-data related factors, such as changes in the scene, lack of texture or moving objects. We conclude that current video data are not only more rapidly available than photos, but they also have a comparable ability to assist in image-based structural damage assessment and other post-disaster activities. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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6. Object-based gully system prediction from medium resolution imagery using Random Forests.
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Shruthi, Rajesh B.V., Kerle, Norman, Jetten, Victor, and Stein, Alfred
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OPTICAL resolution , *RANDOM forest algorithms , *EROSION , *ENVIRONMENTAL remediation , *GEOLOGICAL mapping , *REMOTE sensing - Abstract
Abstract: Erosion, in particular gully erosion, is a widespread problem. Its mapping is crucial for erosion monitoring and remediation of degraded areas. In addition, mapping of areas with high potential for future gully erosion can be used to assist prevention strategies. Good relations with topographic variables collected from the field are appropriate for determining areas susceptible to gullying. Image analysis of high resolution remotely sensed imagery (HRI) in combination with field verification has proven to be a good approach, although dependent on expensive imagery. Automatic and semi-automatic methods, such as object-oriented analysis (OOA), are rapid and reproducible. However, HRI data are not always available. We therefore attempted to identify gully systems using statistical modeling of image features from medium resolution imagery, here ASTER. These data were used for determining areas within gully system boundaries (GSB) using a semi-automatic method based on OOA. We assess if the selection of useful object features can be done in an objective and transferable way, using Random Forests (RF) for prediction of gully systems at regional scale, here in the Sehoul region, near Rabat, Morocco. Moderate success was achieved using a semi-automatic object-based RF model (out-of-bag error of 18.8%). Besides compensating for the imbalance between gully and non-gully classes, the procedure followed in this study enabled us to balance the classification error rates. The user's and producer's accuracy of the data with a balanced set of class showed an improved accuracy of the spatial estimates of gully systems, when compared to the data with imbalanced class. The model over-predicted the area within the GSB (13–27%), but its overall performance demonstrated that medium resolution satellite images contain sufficient information to identify gully systems, so that large areas can be mapped with relatively little effort and acceptable accuracy. [Copyright &y& Elsevier]
- Published
- 2014
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7. Active Learning in the Spatial Domain for Remote Sensing Image Classification.
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Stumpf, Andre, Lachiche, Nicolas, Malet, Jean-Philippe, Kerle, Norman, and Puissant, Anne
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REMOTE-sensing images ,ACTIVE learning ,STATISTICAL bootstrapping ,SPATIAL distribution (Quantum optics) ,CLASSIFICATION algorithms - Abstract
Active learning (AL) algorithms have been proven useful in reducing the number of required training samples for remote sensing applications; however, most methods query samples pointwise without considering spatial constraints on their distribution. This may often lead to a spatially dispersed distribution of training points unfavorable for visual image interpretation or field surveys. The aim of this study is to develop region-based AL heuristics to guide user attention toward a limited number of compact spatial batches rather than distributed points. The proposed query functions are based on a tree ensemble classifier and combine criteria of sample uncertainty and diversity to select regions of interest. Class imbalance, which is inherent to many remote sensing applications, is addressed through stratified bootstrap sampling. Empirical tests of the proposed methods are performed with multitemporal and multisensor satellite images capturing, in particular, sites recently affected by large-scale landslide events. The assessment includes an experimental evaluation of the labeling time required by the user and the computational runtime, and a sensitivity analysis of the main algorithm parameters. Region-based heuristics that consider sample uncertainty and diversity are found to outperform pointwise sampling and region-based methods that consider only uncertainty. Reference landslide inventories from five different experts enable a detailed assessment of the spatial distribution of remaining errors and the uncertainty of the reference data. [ABSTRACT FROM AUTHOR]
- Published
- 2014
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8. Transferability of Object-Oriented Image Analysis Methods for Slum Identification.
- Author
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Kohli, Divyani, Warwadekar, Pankaj, Kerle, Norman, Sliuzas, Richard, and Stein, Alfred
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IMAGE analysis ,REMOTE sensing ,PHOTOGRAPHY of slums ,ENTROPY - Abstract
Updated spatial information on the dynamics of slums can be helpful to measure and evaluate progress of policies. Earlier studies have shown that semi-automatic detection of slums using remote sensing can be challenging considering the large variability in definition and appearance. In this study, we explored the potential of an object-oriented image analysis (OOA) method to detect slums, using very high resolution (VHR) imagery. This method integrated expert knowledge in the form of a local slum ontology. A set of image-based parameters was identified that was used for differentiating slums from non-slum areas in an OOA environment. The method was implemented on three subsets of the city of Ahmedabad, India. Results show that textural features such as entropy and contrast derived from a grey level co-occurrence matrix (GLCM) and the size of image segments are stable parameters for classification of built-up areas and the identification of slums. Relation with classified slum objects, in terms of enclosed by slums and relative border with slums was used to refine classification. The analysis on three different subsets showed final accuracies ranging from 47% to 68%. We conclude that our method produces useful results as it allows including location specific adaptation, whereas generically applicable rulesets for slums are still to be developed. [ABSTRACT FROM AUTHOR]
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- 2013
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9. Object-oriented mapping of landslides using Random Forests
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Stumpf, André and Kerle, Norman
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OBJECT-oriented methods (Computer science) , *FORESTS & forestry , *LANDSLIDES , *REMOTE sensing , *INVENTORIES , *MACHINE learning - Abstract
Abstract: Landslide inventory mapping is an indispensable prerequisite for reliable hazard and risk analysis, and with the increasing availability of very high resolution (VHR) remote sensing imagery the creation and updating of such inventories on regular bases and directly after major events is becoming possible. The diversity of landslide processes and spectral similarities of affected areas with other landscape elements pose major challenges for automated image processing, and time-consuming manual image interpretation and field surveys are still the most commonly applied mapping techniques. Taking advantage of recent advances in object-oriented image analysis (OOA) and machine learning algorithms, a supervised workflow is proposed in this study to reduce manual labor and objectify the choice of significant object features and classification thresholds. A sequence of image segmentation, feature selection, object classification and error balancing was developed and tested on a variety of sample datasets (Quickbird, IKONOS, Geoeye-1, aerial photographs) of four sites in the northern hemisphere recently affected by landslides (Haiti, Italy, China, France). Besides object metrics, such as band ratios and slope, newly introduced topographically-guided texture measures were found to enhance significantly the classification, and also feature selection revealed positive influence on the overall performance. With an iterative procedure to examine the class-imbalance within the training sample it was furthermore possible to compensate spurious effects of class-imbalance and class-overlap on the balance of the error rates. Employing approximately 20% of the data for training, the proposed workflow resulted in accuracies between 73% and 87% for the affected areas, and approximately balanced commission and omission errors. [Copyright &y& Elsevier]
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- 2011
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10. Object-Oriented Change Detection for Landslide Rapid Mapping.
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Lu, Ping, Stumpf, André, Kerle, Norman, and Casagli, Nicola
- Abstract
A complete multitemporal landslide inventory, ideally updated after each major event, is essential for quantitative landslide hazard assessment. However, traditional mapping methods, which rely on manual interpretation of aerial photographs and intensive field surveys, are time consuming and not efficient for generating such event-based inventories. In this letter, a semiautomatic approach based on object-oriented change detection for landslide rapid mapping and using very high resolution optical images is introduced. The usefulness of this methodology is demonstrated on the Messina landslide event in southern Italy that occurred on October 1, 2009. The algorithm was first developed in a training area of Altolia and subsequently tested without modifications in an independent area of Itala. Correctly detected were 198 newly triggered landslides, with user accuracies of 81.8% for the number of landslides and 75.9% for the extent of landslides. The principal novelties of this letter are as follows: 1) a fully automatic problem-specified multiscale optimization for image segmentation and 2) a multitemporal analysis at object level with several systemized spectral and textural measurements. [ABSTRACT FROM PUBLISHER]
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- 2011
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11. Optimal region growing segmentation and its effect on classification accuracy.
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Gao, Yan, Mas, JeanFrancois, Kerle, Norman, and Navarrete Pacheco, Jose Antonio
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IMAGE processing ,LAND use mapping ,REMOTE sensing ,PIXELS ,IMAGE quality analysis - Abstract
Image segmentation is a preliminary and critical step in object-based image classification. Its proper evaluation ensures that the best segmentation is used in image classification. In this article, image segmentations with nine different parameter settings were carried out with a multi-spectral Landsat imagery and the segmentation results were evaluated with an objective function that aims at maximizing homogeneity within segments and separability between neighbouring segments. The segmented images were classified into eight land-cover classes and the classifications were evaluated with independent ground data comprising 600 randomly distributed points. The accuracy assessment results presented similar distribution as that of the objective function values, that is segmentations with the highest objective function values also resulted in the highest classification accuracies. This result shows that image segmentation has a direct effect on the classification accuracy; the objective function not only worked on a single band image as proved by (Espindola, G.M., Camara, G., Reis, I.A., Bins, L.S. and Monteiro, A.M., 2006, Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation. International Journal of Remote Sensing, 27, pp. 3035-3040.) but also on multi-spectral imagery as tested in this, and is indeed an effective way to determine the optimal segmentation parameters. McNemar's test (z2 = 10.27) shows that with the optimal segmentation, object-based classification achieved accuracy significantly higher than that of the pixel-based classification, with 99% significance level. [ABSTRACT FROM AUTHOR]
- Published
- 2011
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12. Combining Random Forests and object-oriented analysis for landslide mapping from very high resolution imagery.
- Author
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Stumpf, André and Kerle, Norman
- Subjects
HIGH resolution imaging ,LANDSLIDES ,REMOTE sensing ,DISASTERS ,IMAGE analysis ,MACHINE learning ,MATHEMATICAL mappings - Abstract
Abstract: The increasing availability of very high resolution (VHR) remote sensing images has been leading to new opportunities for the cartography of landslides in risk management and disaster response. Object-oriented image analysis has become one of the key-concepts to better exploit additional spatial, spectral and contextual information. The multitude of additional object attributes calls for the use of advanced data mining and machine learning tools to identify the most suitable features and handle the non-linear classification task. In this study we used the Random Forest algorithm for the selection of useful features and object classification in the context of landslide mapping. A workflow for image segmentation, feature extraction, feature selection and classification was developed and tested with multi-sensor optical imagery from four different test sites. Due to class imbalance and class overlap between landslide and non-landslide areas the classifier can be heavily biased towards over- and under-prediction of the affected areas. This is a common issue for many real-world applications and a procedure to estimate a well-adjusted class ratio from the training samples was designed and tested. A number of potentially useful object metrics was evaluated and it was demonstrated that topographically guided texture measures provide significant enhancements. Employing 20% of the image objects for training accuracies between 73.3% and 87.1% were achieved at four different test sites. [Copyright &y& Elsevier]
- Published
- 2011
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13. The Function of Remote Sensing in Support of Environmental Policy.
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de Leeuw, Jan, Georgiadou, Yola, Kerle, Norman, de Gier, Alfred, Inoue, Yoshio, Ferwerda, Jelle, Smies, Maarten, and Narantuya, Davaa
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ENVIRONMENTAL policy ,REMOTE sensing in earth sciences ,ENVIRONMENTAL protection ,LANDSAT satellites ,OZONE ,TURBIDITY ,CLIMATE change - Abstract
Limited awareness of environmental remote sensing's potential ability to support environmental policy development constrains the technology's utilization. This paper reviews the potential of earth observation from the perspective of environmental policy. A literature review of "remote sensing and policy" revealed that while the number of publications in this field increased almost twice as rapidly as that of remote sensing literature as a whole (15.3 versus 8.8% yr-1), there is apparently little academic interest in the societal contribution of environmental remote sensing. This is because none of the more than 300 peer reviewed papers described actual policy support. This paper describes and discusses the potential, actual support, and limitations of earth observation with respect to supporting the various stages of environmental policy development. Examples are given of the use of remote sensing in problem identification and policy formulation, policy implementation, and policy control and evaluation. While initially, remote sensing contributed primarily to the identification of environmental problems and policy implementation, more recently, interest expanded to applications in policy control and evaluation. The paper concludes that the potential of earth observation to control and evaluate, and thus assess the efficiency and effectiveness of policy, offers the possibility of strengthening governance. [ABSTRACT FROM AUTHOR]
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- 2010
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14. Satellite Remote Sensing as a Tool in Lahar Disaster Management.
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Kerle, Norman and Oppenheimer, Clive
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LAHARS , *REMOTE sensing , *ARTIFICIAL satellites - Abstract
At least 40,000 deaths have been attributed to historic lahars (volcanic mudflows). The most recent lahar disaster occurred in 1998 at Casita volcano, Nicaragua, claiming over 2,500 lives. Lahars can cover large areas and be highly destructive, and constitute a challenge for disaster management. With infrastructure affected and access frequently impeded, disaster management can benefit from the synoptic coverage provided by satellite imagery. This potential has been recognised for other types of natural disasters, but limitations are also known. Dedicated satellite constellations for disaster response and management have been proposed as one solution. Here we investigate the utility of currently available and forthcoming optical and radar sensors as tools in lahar disaster management. Applied to the Casita case, we find that imagery available at the time could not have significantly improved disaster response. However, forthcoming satellites, especially radar, will improve the situation, reducing the benefit of dedicated constellations. [ABSTRACT FROM AUTHOR]
- Published
- 2002
- Full Text
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15. Agent-based modelling of post-disaster recovery with remote sensing data.
- Author
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Ghaffarian, Saman, Roy, Debraj, Filatova, Tatiana, and Kerle, Norman
- Abstract
Disaster risk management, and post-disaster recovery (PDR) in particular, become increasingly important to assure resilient development. Yet, PDR is the most poorly understood phase of the disaster management cycle and can take years or even decades. The physical aspects of the recovery are relatively easy to monitor and evaluate using, e.g. geospatial remote sensing data compared to functional assessments that include social and economic processes. Therefore, there is a need to explore the impacts of different dimensions of the recovery, including individual behaviour and their interactions with socio-economic institutions. In this study, we develop an agent-based model to simulate and explore the PDR process in urban areas of Tacloban, the Philippines devastated by Typhoon Haiyan in 2013. Formal and informal (slum) sector households are differentiated in the model to explore their resilience and different recovery patterns. Machine learning-derived land use maps are extracted from remote sensing images for pre- and post-disaster and are used to provide information on physical recovery. We use the empirical model to evaluate two realistic policy scenarios: the construction of relocation sites after a disaster and the investments in improving employment options. We find that the speed of the recovery of the slum dwellers is higher than formal sector households due to the quick reconstruction of slums and the availability of low-income jobs in the first months after the disaster. Finally, the results reveal that the households' commuting distance to their workplaces is one of the critical factors in households' decision to relocate after a disaster. [ABSTRACT FROM AUTHOR]
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- 2021
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16. UAV-Based Structural Damage Mapping: A Review.
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Kerle, Norman, Nex, Francesco, Gerke, Markus, Duarte, Diogo, and Vetrivel, Anand
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SITUATIONAL awareness , *RESCUE work , *REMOTE sensing , *STEREO image , *IMAGE processing , *TEMPORAL databases - Abstract
Structural disaster damage detection and characterization is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of unmanned aerial vehicles (UAVs) in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. This study provides a comprehensive review of how UAV-based damage mapping has evolved from providing simple descriptive overviews of a disaster science, to more sophisticated texture and segmentation-based approaches, and finally to studies using advanced deep learning approaches, as well as multi-temporal and multi-perspective imagery to provide comprehensive damage descriptions. The paper further reviews studies on the utility of the developed mapping strategies and image processing pipelines for first responders, focusing especially on outcomes of two recent European research projects, RECONASS (Reconstruction and Recovery Planning: Rapid and Continuously Updated Construction Damage, and Related Needs Assessment) and INACHUS (Technological and Methodological Solutions for Integrated Wide Area Situation Awareness and Survivor Localization to Support Search and Rescue Teams). Finally, recent and emerging developments are reviewed, such as recent improvements in machine learning, increasing mapping autonomy, damage mapping in interior, GPS-denied environments, the utility of UAVs for infrastructure mapping and maintenance, as well as the emergence of UAVs with robotic abilities. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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17. Evaluating Resilience-Centered Development Interventions with Remote Sensing.
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Kerle, Norman, Ghaffarian, Saman, Nawrotzki, Raphael, Leppert, Gerald, and Lech, Malte
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REMOTE sensing , *SUPER Typhoon Haiyan, 2013 , *HIGH resolution imaging , *LAND cover , *NATURAL disasters , *OPTICAL remote sensing , *SHRUBLANDS - Abstract
Natural disasters are projected to increase in number and severity, in part due to climate change. At the same time a growing number of disaster risk reduction (DRR) and climate change adaptation measures are being implemented by governmental and non-governmental organizations, and substantial post-disaster donations are frequently pledged. At the same time there has been increasing demand for transparency and accountability, and thus evidence of those measures having a positive effect. We hypothesized that resilience-enhancing interventions should result in less damage during a hazard event, or at least quicker recovery. In this study we assessed recovery over a 3 year period of seven municipalities in the central Philippines devastated by Typhoon Haiyan in 2013. We used very high resolution optical images (<1 m), and created detailed land cover and land use maps for four epochs before and after the event, using a machine learning approach with extreme gradient boosting. The spatially and temporally highly variable recovery maps were then statistically related to detailed questionnaire data acquired by DEval in 2012 and 2016, whose principal aim was to assess the impact of a 10 year land-planning intervention program by the German agency for technical cooperation (GIZ). The survey data allowed very detailed insights into DRR-related perspectives, motivations and drivers of the affected population. To some extent they also helped to overcome the principal limitation of remote sensing, which can effectively describe but not explain the reasons for differential recovery. However, while a number of causal links between intervention parameters and reconstruction was found, the common notion that a resilient community should recover better and more quickly could not be confirmed. The study also revealed a number of methodological limitations, such as the high cost for commercial image data not matching the spatially extensive but also detailed scale of field evaluations, the remote sensing analysis likely overestimating damage and thus providing incorrect recovery metrics, and image data catalogues especially for more remote communities often being incomplete. Nevertheless, the study provides a valuable proof of concept for the synergies resulting from an integration of socio-economic survey data and remote sensing imagery for recovery assessment. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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18. Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review.
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Ghaffarian, Saman, Kerle, Norman, and Filatova, Tatiana
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REMOTE sensing , *EMERGENCY management , *DISASTER resilience , *DATA acquisition systems , *CROSS-fertilization of plants - Abstract
Rapid increase in population and growing concentration of capital in urban areas has escalated both the severity and longer-term impact of natural disasters. As a result, Disaster Risk Management (DRM) and reduction have been gaining increasing importance for urban areas. Remote sensing plays a key role in providing information for urban DRM analysis due to its agile data acquisition, synoptic perspective, growing range of data types, and instrument sophistication, as well as low cost. As a consequence numerous methods have been developed to extract information for various phases of DRM analysis. However, given the diverse information needs, only few of the parameters of interest are extracted directly, while the majority have to be elicited indirectly using proxies. This paper provides a comprehensive review of the proxies developed for two risk elements typically associated with pre-disaster situations (vulnerability and resilience), and two post-disaster elements (damage and recovery), while focusing on urban DRM. The proxies were reviewed in the context of four main environments and their corresponding sub-categories: built-up (buildings, transport, and others), economic (macro, regional and urban economics, and logistics), social (services and infrastructures, and socio-economic status), and natural. All environments and the corresponding proxies are discussed and analyzed in terms of their reliability and sufficiency in comprehensively addressing the selected DRM assessments. We highlight strength and identify gaps and limitations in current proxies, including inconsistencies in terminology for indirect measurements. We present a systematic overview for each group of the reviewed proxies that could simplify cross-fertilization across different DRM domains and may assist the further development of methods. While systemizing examples from the wider remote sensing domain and insights from social and economic sciences, we suggest a direction for developing new proxies, also potentially suitable for capturing functional recovery. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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19. Image-based mapping of surface fissures for the investigation of landslide dynamics
- Author
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Stumpf, André, Malet, Jean-Philippe, Kerle, Norman, Niethammer, Uwe, and Rothmund, Sabrina
- Subjects
- *
SOIL cracking , *LANDSLIDES , *GEOPHYSICAL prediction , *AERIAL photography , *REMOTE sensing , *ALGORITHMS , *GEOMORPHOLOGY , *GEOLOGY fieldwork - Abstract
Abstract: The development of surface fissures is an important indicator for understanding and forecasting slope movements. Landslide investigations therefore frequently include the elaboration and interpretation of maps representing their spatial distribution, typically comprising intensive field work and instrumentation. It is only recently that aerial photography with sub-decimetre spatial resolution is becoming more commonly available and opens a window to analyse such features from a remote sensing perspective. While these data are in principle helpful to elaborate maps from image interpretation techniques, there is still no image processing technique available to extract efficiently these geomorphological features. This work proposes a largely automated technique for the mapping of landslide surface fissures from very-high resolution aerial images. The processing chain includes the use of filtering algorithms and post-processing of the filtered images using object-oriented analysis. The accuracy of the resulting maps is assessed by comparisons with several expert maps in terms of affected area, fissure density and fissure orientation. Under homogenous illumination conditions, true positive rates up to 65% and false positive rates generally below 10% are achieved. The resulting fissure maps provide sufficient detail to infer mechanical processes at the slope scale and to prioritize areas for more detailed ground investigations or monitoring. [Copyright &y& Elsevier]
- Published
- 2013
- Full Text
- View/download PDF
20. An ontology of slums for image-based classification
- Author
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Kohli, Divyani, Sliuzas, Richard, Kerle, Norman, and Stein, Alfred
- Subjects
- *
ONTOLOGY , *ACQUISITION of data , *REMOTE sensing , *FLOODS , *IMAGE analysis , *MARSHES , *SLUMS - Abstract
Abstract: Information about rapidly changing slum areas may support the development of appropriate interventions by concerned authorities. Often, however, traditional data collection methods lack information on the spatial distribution of slum-dwellers. Remote sensing based methods could be used for a rapid inventory of the location and physical composition of slums. (Semi-)automatic detection of slums in image data is challenging, owing to the high variability in appearance and definitions across different contexts. This paper develops an ontological framework to conceptualize slums using input from 50 domain-experts covering 16 different countries. This generic slum ontology (GSO) comprises concepts identified at three levels that refer to the morphology of the built environment: the environs level, the settlement level and the object level. It serves as a comprehensive basis for image-based classification of slums, in particular, using object-oriented image analysis (OOA) techniques. This is demonstrated by with an example of local adaptation of GSO and OOA parameterization for a study area in Kisumu, Kenya. At the object level, building and road characteristics are major components of the ontology. At the settlement level, texture measures can be potentially used to represent the contrast between planned and unplanned settlements. At the environs level, factors which extend beyond the site itself are important indicators, e.g. hazards due to floods plains and marshy conditions. The GSO provides a comprehensive framework that includes all potentially relevant indicators that can be used for image-based slum identification. These characteristics may be different for other study areas, but show the applicability of the developed framework. [Copyright &y& Elsevier]
- Published
- 2012
- Full Text
- View/download PDF
21. Post-disaster recovery assessment using remote sensing image analysis and agent-based modeling
- Author
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Saman Ghaffarian, Kerle, Norman, Filatova, Tatiana, Roy, Debraj, Department of Earth Systems Analysis, UT-I-ITC-4DEarth, and Faculty of Geo-Information Science and Earth Observation
- Subjects
Remote sensing (archaeology) ,Computer science ,Post disaster ,Remote sensing - Published
- 2020
22. Debris, rubble piles and façade damage detection using multi-resolution optical remote sensing imagery
- Author
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Diogo Duarte, Vosselman, George, Kerle, Norman, Nex, Francesco, Department of Earth Observation Science, UT-I-ITC-ACQUAL, and Faculty of Geo-Information Science and Earth Observation
- Subjects
Damage detection ,Remote sensing (archaeology) ,Multi resolution ,Rubble ,engineering ,Environmental science ,Facade ,engineering.material ,Debris ,Remote sensing - Published
- 2020
23. Automatic information extraction from remote sensing images and 3D point clouds for building damage assessment
- Author
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Anand Vetrivel, Vosselman, George, Kerle, Norman, Gerke, Markus, Department of Earth Observation Science, UT-I-ITC-ACQUAL, and Faculty of Geo-Information Science and Earth Observation
- Subjects
Information extraction ,Remote sensing (archaeology) ,Computer science ,Point cloud ,computer.software_genre ,computer ,Remote sensing - Published
- 2018
24. Identifying and classifying slum areas using remote sensing
- Author
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Divyani Kohli, Stein, Alfred, Sliuzas, Richard, Kerle, Norman, Department of Earth Observation Science, Department of Earth Systems Analysis, and Department of Urban and Regional Planning and Geo-Information Management
- Subjects
Remote sensing (archaeology) ,Computer science ,Slum ,Remote sensing - Published
- 2015
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