Enhanced change detection index for disaster response, recovery assessment and monitoring of buildings and critical facilities-A case study for Muzzaffarabad, Pakistan

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects such as buildings and critical facilities. The change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. More emphasis is put on the building edges to capture the structural damage in quantifying change after disaster. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in potentially large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management and recovery practices. The recovery and monitoring can be analyzed using the index in zones extending from to epicentre of disaster or administrative boundaries over time.EU FP

Remote Sensing Derived Indices for Tracking Urban Land Surface Change in Case of Earthquake Recovery

The study of post-disaster recovery requires an understanding of the reconstruction process and growth trend of the impacted regions. In case of earthquakes, while remote sensing has been applied for response and damage assessment, its application has not been investigated thoroughly for monitoring the recovery dynamics in spatially and temporally explicit dimensions. The need and necessity for tracking the change in the built-environment through time is essential for post-disaster recovery modeling, and remote sensing is particularly useful for obtaining this information when other sources of data are scarce or unavailable. Additionally, the longitudinal study of repeated observations over time in the built-up areas has its own complexities and limitations. Hence, a model is needed to overcome these barriers to extract the temporal variations from before to after the disaster event. In this study, a method is introduced by using three spectral indices of UI (urban index), NDVI (normalized difference vegetation index) and MNDWI (modified normalized difference water index) in a conditional algebra, to build a knowledge-based classifier for extracting the urban/built-up features. This method enables more precise distinction of features based on environmental and socioeconomic variability, by providing flexibility in defining the indices’ thresholds with the conditional algebra statements according to local characteristics. The proposed method is applied and implemented in three earthquake cases: New Zealand in 2010, Italy in 2009, and Iran in 2003. The overall accuracies of all built-up/non-urban classifications range between 92% to 96.29%; and the Kappa values vary from 0.79 to 0.91. The annual analysis of each case, spanning from 10 years pre-event, immediate post-event, and until present time (2019), demonstrates the inter-annual change in urban/built-up land surface of the three cases. Results in this study allow a deeper understanding of how the earthquake has impacted the region and how the urban growth is altered after the disaster

Spatio-Temporal Modeling of Earthquake Recovery

The recovery process after a major disaster or disruption, is impacted by the inequality of risk prior to and post event. In the past decades there has been few efforts to model the recovery process and the focus is mainly on staged models (i.e. emergency, restoration, and reconstruction). The overarching research question asks how a non-stage-like model could apply to the recovery process. This study poses three broad questions: 1) what are the indicators suitable for monitoring the recovery process; 2) what are the driving factors of differential recovery trends; and 3) what are the predicted development trajectories for communities if there was no disruption? To address the research questions, a new model is proposed for tracking the recovery process as the “Tempo-variant Model of Disaster Recovery” (TMDR), which is implemented for six case studies of recoveries post-earthquakes, in a continuous trend through time (case studies from: Chile, New Zealand, India, Iran, China, and Italy). The recovery process is monitored through a set of proposed indicators representing the changes in six main categories of housing, socio-economic, agriculture, infrastructural, institutional, and development. Satellite imagery is used as a congruent data source to monitor urban land surface change that is implemented with a new model and conditional algebra for change detection. A new method is then developed by combining the satellite imagery data with social indicators, which provides quantitative/relative measure of recovery trend (spatially and temporally) where ground assessments are impractical. The results of implementing the new TMDR model in this cross-cultural comparative study, further highlights the drivers of recovery process across time and nations. The difference between post-event and pre-event trends (i.e. recovery progress) shows significant association with instantaneous impact of the event on community development dynamics in all cases. The spatio-temporal analysis shows majority of the study area in Chile is recovered, but there are regions in the other cases that are still recovering. The comparative view on TMDR results indicates that impact of event is more significant for recovery progress in the initial years post-event, while additional indicators of access to basic infrastructure is more predictive in the long-term. Therefore, this new model provides a case-dependent baseline and an operational tool for monitoring the recovery process