Role of Remote Sensing in Disaster Management

The objective of this report is to review the existing satellites monitoring Earth’s resources and natural disasters. Each satellite has different repeat pass frequency and spatial resolution (unless it belongs to the same series of satellites for the purpose of continuation of data flow with same specifications). Similarly, different satellites have different types of sensors on-board, such as, panchromatic, multispectral, infrared and thermal. All these sensors have applications in disaster mitigation, though depending on the electromagnetic characteristics of the objects on Earth and the nature of disaster itself. With a review of the satellites in orbit and their sensors the present work provides an insight to suitability of satellites and sensors to different natural disasters. For example, thermal sensors capture fire hazards, infrared sensors are more suitable for floods and microwave sensors can record soil moisture. Several kinds of disasters, such as, earthquake, volcano, tsunami, forest fire, hurricane and floods are considered for the purpose of disaster mitigation studies in this report. However, flood phenomenon has been emphasized upon in this study with more detailed account of remote sensing and GIS (Geographic Information Systems) applicability. Examples of flood forecasting and flood mapping presented in this report illustrate the capability of remote sensing and GIS technology in delineating flood risk areas and assessing the damages after the flood recedes. With the help of a case study of the Upper Thames River watershed the use of remote sensing and GIS has been illustrated for better understanding. The case study enables the professionals and planning authorities to realize the impact of urbanization on river flows. As the urban sprawl increases with the increase of population, the rainfall and snow melt reaches the river channels at a faster rate with higher intensity. In other words it can be inferred that through careful land use planning flood disasters can be mitigated.https://ir.lib.uwo.ca/wrrr/1002/thumbnail.jp

A Study of Types of Sensors used in Remote Sensing

Of late, the science of Remote Sensing has been gaining a lot of interest and attention due to its wide variety of applications. Remotely sensed data can be used in various fields such as medicine, agriculture, engineering, weather forecasting, military tactics, disaster management etc. only to name a few. This article presents a study of the two categories of sensors namely optical and microwave which are used for remotely sensing the occurrence of disasters such as earthquakes, floods, landslides, avalanches, tropical cyclones and suspicious movements. The remotely sensed data acquired either through satellites or through ground based- synthetic aperture radar systems could be used to avert or mitigate a disaster or to perform a post-disaster analysis

Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data: a case study in an urban-rural landscape in the Brazilian Amazon.

This research explored the integrated use of Landsat Thematic Mapper (TM) and radar (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) data for mapping impervious surface distribution to examine the roles of radar data with different spatial resolutions and wavelengths. The wavelet-merging technique was used to merge TM and radar data to generate a new dataset. A constrained least-squares solution was used to unmix TM multispectral data and multisensor fusion images to four fraction images (high-albedo, low-albedo, vegetation, and soil). The impervious surface image was then extracted from the high-albedo and low-albedo fraction images. QuickBird imagery was used to develop an impervious surface image for use as reference data to evaluate the results from TM and fusion images. This research indicated that increasing spatial resolution by multisensor fusion improved spatial patterns of impervious surface distribution, but cannot significantly improve the statistical area accuracy. This research also indicated that the fusion image with 10-m spatial resolution was suitable for mapping impervious surface spatial distribution, but TM multispectral image with 30 m was too coarse in a complex urban?rural landscape. On the other hand, this research showed that no significant difference in improving impervious surface mapping performance by using either PALSAR L-band or RADARSAT C-band data with the same spatial resolution when they were used for multi-sensor fusion with the wavelet-based method

Land use/cover classification in the Brazilian Amazon using satellite images.

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation?based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi?resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical?based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data

Advances in remote sensing applications for urban sustainability

Abstract: It is essential to monitor urban evolution at spatial and temporal scales to improve our understanding of the changes in cities and their impact on natural resources and environmental systems. Various aspects of remote sensing are routinely used to detect and map features and changes on land and sea surfaces, and in the atmosphere that affect urban sustainability. We provide a critical and comprehensive review of the characteristics of remote sensing systems, and in particular the trade-offs between various system parameters, as well as their use in two key research areas: (a) issues resulting from the expansion of urban environments, and (b) sustainable urban development. The analysis identifies three key trends in the existing literature: (a) the integration of heterogeneous remote sensing data, primarily for investigating or modelling urban environments as a complex system, (b) the development of new algorithms for effective extraction of urban features, and (c) the improvement in the accuracy of traditional spectral-based classification algorithms for addressing the spectral heterogeneity within urban areas. Growing interests in renewable energy have also resulted in the increased use of remote sensing—for planning, operation, and maintenance of energy infrastructures, in particular the ones with spatial variability, such as solar, wind, and geothermal energy. The proliferation of sustainability thinking in all facets of urban development and management also acts as a catalyst for the increased use of, and advances in, remote sensing for urban applications

Remote Sensing Systems Optimization for Geobase Enhancement

The U.S. Air Force is in the process of implementing GeoBase, a geographic information system (GIS), throughout its worldwide installations, Air Force GIS needs can be augmented by imagery from aerial and satellite platforms. Imagery has greatly improved over the past several years and provides high resolution coverage of features on earth. Various imagery types will significantly increase GeoBase usefulness in a range of mission requirements, Potential Air Force uses of imagery include identifying heat loss, environmental monitoring, command decision-making, and emergency response, The research develops a decision tool to determine the appropriate imagery for a given Air Force Application, Current literature identified proven imagery applications, Literature review and a 2002 Air Force Geo-Integration Office (AF/GIO) survey were used to develop a comprehensive imagery applications list that satisfies Air Force mission requirements, An imagery decision matrix was crafted that allows a user to select an application and see imagery that fulfills the requirements for the task, An imagery system key provides further details of each imagery type, The matrix was tested at three Air Force bases, Increased awareness of the possibilities of an imagery-enriched GeoBase, and the efficiency afforded by the matrix, greatly reduces the time to identify and implement imagery, Available imagery was identified for the three Air Force bases at the National Imagery and Mapping Agency (NIMA) through a government contract at no additional cost, Current IKONOS imagery of Elmendoff Air Force base was obtained for analysis and implementation into GeoBase

Landslide mapping and monitoring by using radar and optical remote sensing: examples from the EC-FP7 project SAFER

This paper focuses on the Landslide Thematic services of the EU-funded FP7-SPACE project SAFER (Services and Applications For Emergency Response) for inventory mapping, monitoring and rapid mapping by using Earth Observation (EO). We exploited satellite Interferometric Synthetic Aperture Radar (InSAR) and Object-Based Image Analysis (OBIA), and discuss example applications in South Tyrol and Abruzzo (Italy), Lower Austria (Austria), Lubietova (Slovakia) and the Kaohsiung County (Taiwan). These case studies showcase the significance of radar and optical EO data, InSAR and OBIA methods for landslide mapping and monitoring in different geological environments and during all phases of emergency management: mitigation, preparedness, crisis and recovery

Land-cover classification in the Brazilian Amazon with the integration of Landsat ETM + and Radarsat data.

Land-cover classification with remotely sensed data in moist tropical regions in a challenge due to the complex biophysical conditions. This paper explores techniques to improve land-cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM +) and Radarsat data. A wavelet-merging technique was used to integrate Landsat ETM + multispectral and panchromatic data or Radarsat data. Grey-level co-occurrence matrix (GLCM) textures based on Landsat ETM + panchromatic of Radarsat data and different sizes of moving windows were examined. A maximum-likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land-cover classification accuracies in Amazonian environments. ..