Supervised / unsupervised change detection

The aim of this deliverable is to provide an overview of the state of the art in change detection techniques and a critique of what could be programmed to derive SENSUM products. It is the product of the collaboration between UCAM and EUCENTRE. The document includes as a necessary requirement a discussion about a proposed technique for co-registration. Since change detection techniques require an assessment of a series of images and the basic process involves comparing and contrasting the similarities and differences to essentially spot changes, co-registration is the first step. This ensures that the user is comparing like for like. The developed programs would then be used on remotely sensed images for applications in vulnerability assessment and post-disaster recovery assessment and monitoring. One key criterion is to develop semi-automated and automated techniques. A series of available techniques are presented along with the advantages and disadvantages of each method. The descriptions of the implemented methods are included in the deliverable D2.7 ”Software Package SW2.3”. In reviewing the available change detection techniques, the focus was on ways to exploit medium resolution imagery such as Landsat due to its free-to-use license and since there is a rich historical coverage arising from this satellite series. Regarding the change detection techniques with high resolution images, this was also examined and a recovery specific change detection index is discussed in the report

Is Jakarta's New Flood Risk Reduction Strategy Transformational?

On a conceptual and normative level, the debate around transformation in the context of disaster risk reduction and climate change adaptation has been rising sharply over the recent years. Yet, whether and how transformation occurs in the messy realities of policy and action, and what separates it from other forms of risk reduction, is far from clear. Jakarta appears to be the perfect example to study these questions. It is amongst the cities with the highest flood risk in the world. Its flood hazard is driven by land subsidence, soil sealing, changes in river discharge, andincreasinglysea level rise. As all of these trends are set to continue, Jakarta's flood hazard is expected to intensify in the future. Designing and implementing large-scale risk reduction and adaption measures therefore has been a priority of risk practitioners and policy-makers at city and national level. Against this background, the paper draws on a document analysis and original empirical household survey data to review and evaluate current adaptation measures and to analyze in how far they describe a path that is transformational from previous risk reduction approaches. The results show that the focus is clearly on engineering solutions, foremost in the Giant Sea Wall project. The project is likely to transform the city's flood hydrology. However, it cements rather than transforms the current risk management paradigm which gravitates around the goal of controlling flood symptoms, rather than addressing their largely anthropogenic root causes. The results also show that the planned measures are heavily contested due to concerns about ecological impacts, social costs, distributional justice, public participation, and long-term effectiveness. On the outlook, the results therefore suggest that the more the flood hazard intensifies in the future, the deeper a societal debate will be needed about the desired pathway in flood risk reduction and overall development planningparticularly with regards to the accepted levels of transformation, such as partial retreat from the most flood-affected areas

Simulating Future Urban Expansion in Monastir, Tunisia, as an Input for the Development of Future Risk Scenarios

Under scenarios of urbanization coupled with increasing frequency and intensity of natural hazards, urban disaster risk is set to rise. Simulating future urban expansion can provide relevant information for the development of future exposure scenarios and the identification of targeted risk reduction and adaptation strategies. Here, we present an urban growth simulation for the coastal city of Monastir, Tunisia. The approach integrates local knowledge and a data-driven urban growth model to simulate urban sprawl up to 2030. A business-as-usual projection is used to predict the future growth of the city based on the historical trend. Thirteen Landsat images for the period 1975 to 2017 were used to delineate past changes in urban land cover following the European Urban Atlas standard, which served as the main input for the urban growth model. The simulation revealed that the city’s residential area is likely to grow by 127 ha to an overall size of 1,690 ha by 2030, corresponding to an increase of 8.1% compared to the urban footprint of 2017. The outcomes of the analysis presented here served as an input for the spatial simulation of future exposure to flash floods in the case study area

Remote Sensing in Multirisk Assessment: Improving disaster preparedness

Scientific literature reports several possible ways for remote sensing (RS) to contribute to risk assessment for natural disasters, not only from a theoretical perspective but also in concrete applications. However, the typical RS scientist's approach to risk assessment has so far reflected one of the main limitations of the general risk-assessment process where several natural disasters are concerned. That is, to avoid facing the sometimes unmanageable complexities arising from inter-hazard or vulnerability dependencies, risk-assessment activities tend to focus on one hazard at a time, sometimes leaving dangerous gaps in understanding the real risk for a community or an economic system. Given the current trend in the risk-assessment community to move from a sum of hazards to a multi-hazard approach, this article builds on previous scientific literature to bring the same perspective to RS. The importance of the subject is supported and explained, a comprehensive review of the existing multi-risk assessment approaches is provided, and tangible contributions of space-based Earth observation are highlighted in the different phases of the disaster-management cycle. Different strategies are discussed, and a specific example is presented in depth as one of the most promising approaches

Remote Sensing Applications in Detecting Electromagnetic Earthquake Precursors

This study is intended to highlight an emerging trend in the research field of seismic hazard identification. Among natural disasters, earthquakes have attracted special consideration for their enormous capability to kill, to damage, and to trigger other disasters (tsunami, landslides, technical failures...). Traditionally there has been a focus on quake's rock and soil mechanics to develop a mechanical precursor system for earthquakes. But the instantaneous nature of the hazard so far prevented any breakthrough in the short term prediction of quakes. This fact leads the scientists to rely on the historical data records of certain area to build a seismological probabilistic model. The shortfalls of such approach are widely discussed in the literature, for instance its assumption of the reoccurrence of the historical events with the same frequency. Thus it is widely admitted that our inadequate knowledge regarding a complex physical phenomenon like earthquake is far from allowing us to predict its existence in the short term. Recently there has been a growing trend towards researches discussing the availability of a unified approach towards studying earthquake precursors including the so-called "electromagnetic precursors" as possible clues. The underlying principle is that strain accumulation generates electromagnetic effects through various mechanisms. But, so far, identifying these noisy perturbations and refining them continued to be a lost challenge for scientists due to several complexities including quake nature, measurements sensitivity, and data refinement. These complications always question the reliability all over the prediction process. However, the usage of remote sensing satellites, with the appropriate temporal monitoring, for detecting the infrared thermal anomalies and relating it to quakes has recently reactivated the researches in quakes prediction field [1,2]. Particularly, after the new promising physical explanations offered by the p-hole theory which contributed in adding a new piece to the puzzle for detecting the pre-earthquake electromagnetic precursors[3]. References 1. Choudhury S., Dasgupta S., Saraf A. K., Panda S. (2006). Remote sensing observations of pre-earthquake. International Journal of Remote Sensing, Vol. 27 (20), Pages 4381-4396. 2. Ma Y. , Wu L., Liu S., Ma B. (2011). A new method to extract and analyse abnormal phenomenon of earthquake from remote sensing information. Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International, Pages 2208-2211. 3. Freund F. (2003). Rocks That Crackle and Sparkle and Glow: Strange Pre-Earthquake Phenomena. Journal of ScientiZ c Exploration, Vol. 17, No. 1, pp. 37-71

Insights on Earth Observation Capabilities in Updating the Spatial Distribution of Exposed Values in Quake-Prone Areas

The paper highlight the usage of space borne products to address reliable indicators and aggregation methodologies for tracking the dynamic physical exposure of urban areas to natural disasters. The active view of cities due to the population growth and the increasing urbanization trends lead to a general risk underestimation. Such a result is particularly observed where urban planning is not considered a priority or in areas of non-frequent hazards like earthquakes. The regular consequence of such situation would be people indifference to inhibit hazard-prone areas. The critical point of creating a guided development is by instantaneously monitoring and controlling it. Mainly the sources for such process would be either the slow statistical records (Census) or the separate studies that might lack the statistical significance due to several reasons like their restricted areas of coverage, the limited number of specimens or for being a temporal snap shot of the current situation. In addition to that, the problem gets more complicated when we want to consider areas with access difficulty or data scarcity. The on-going developments of space borne technology have created an expanding hole in the wall of time barrier and though enabled getting the necessary geo-information near the real time. The different capabilities of the sensors used would allow extracting reliable physical indicators of a wide urban area within relatively short time. The reliable extracted indicators like building size, height, occupancy, location, and the usage class‎, when aggregated using a convenient methodology, would enrich the knowledge of the spatial and temporal variation of the physical exposure ‎and thus increase our precision in developing new generation of risk assessment models. Moreover, the foreseen limitation of such monitoring technology like the low accuracy could be counteracted by a convenient integration with ancillary data sources to create a more consistent model. All the above issues are addressed in projects like GEM-IDCT and the FP7 Space SENSUM Project, which will be discussed in the paper

A PCA-based hybrid approach for built-up area extraction from Landsat 5, 7 and 8 datasets

Urban expansion monitoring and organization can be performed through space-based observation thanks to the revisit time and level of details guaranteed by satellite remote sensing. In particular, the Landsat mission products are the most used thanks to the long time coverage and open access policy. This paper proposed a hybrid method - combination of pixel- and object-based analysis - in order to automatically extract built-up areas from Landsat imagery. Segments are delineated from spectral indices computed in order to increase the spectral distance among the different land cover classes. The principal component analysis is applied to the original bands and constitutes the pixel-based side of the method. Segments and PCA are then combined and classified using an unsupervised approach. Results of the method were quite satisfying with an average Kappa value over 0.5 in both case studies

Multi-risk buildings exposure and physical vulnerability mapping from optical satellite images: Developing an integrated toolset

The phenomenon of urban sprawling calls for a cost-effective and rapid monitoring tool. Space borne technology offered the ability of extracting such information which is critical for urban planning and resource management. This paper introduces a newly developed indicator from Landsat acquisitions for highlighting built-up areas. On a multitemporal scale the results obtained on urban extraction were used to estimate the age of built up area, connected to codes applied in building design and ultimately to their vulnerability. In conclusion, the extracted information serves as inputs for an integrated approach on multi-risk buildings' exposure and physical vulnerability assessment and mapping

Automatic Delineation of Clouds and Their Shadows in Landsat and CBERS (HRCC) Data

The presence of clouds and their shadows is an obvious problem for maps obtained from multispectral images. As a matter of fact, clouds and their shadows create occluded and obscured areas, hence information gaps that need to be filled. The usual approach-pixel substitution-requires first to recognize the cloud/shadow pixels. This work presents a cloud/shadow delineation algorithm, the cloud/shadow delineation tool (CSDT) designed for Landsat and CBERS medium resolution multispectral data. The algorithm uses a set of literature indices, as well as a set of mathematical operations on the spectral bands, in order to enhance the visibility of the cloud/shadow objects. The performance of CSDT was tested on a set of scenes from the Landsat and CBERS catalogues. The obtained results showed more accurate and stable performance on Landsat data. In order to validate the proposed approach, this work presents also a comparison with the F-mask algorithm on Landsat scenes. Results show that the F-mask technique tends to overestimate the cloud cover, while CSDT slightly underestimates it. However, accuracy measures show a significantly better performance of the proposed method than the F-mask algorithm in our investigation

Automatic hybrid-based built-up area extraction from Landsat 5, 7, and 8 data sets

The rapid expansion of human activities in time and space is tangible in different parts of the world. Urban sprawl is one of the phenomena that need to be controlled in order to ensure a sustainable development of communities. Satellite remote sensing provides a repository of Earth observations - and thus information on surface changes - since the early seventies. This research proposes a hybrid method applied to Landsat acquisitions for highlighting built-up areas and thus their changes, to estimate their age. The results as are also useful products in risk assessment and urban planning