Mapping Mangrove Extent and Change: A Globally Applicable Approach

This study demonstrates a globally applicable method for monitoring mangrove forest extent at high spatial resolution. A 2010 mangrove baseline was classified for 16 study areas using a combination of ALOS PALSAR and Landsat composite imagery within a random forests classifier. A novel map-to-image change method was used to detect annual and decadal changes in extent using ALOS PALSAR/JERS-1 imagery. The map-to-image method presented makes fewer assumptions of the data than existing methods, is less sensitive to variation between scenes due to environmental factors (e.g., tide or soil moisture) and is able to automatically identify a change threshold. Change maps were derived from the 2010 baseline to 1996 using JERS-1 SAR and to 2007, 2008 and 2009 using ALOS PALSAR. This study demonstrated results for 16 known hotspots of mangrove change distributed globally, with a total mangrove area of 2,529,760 ha. The method was demonstrated to have accuracies consistently in excess of 90% (overall accuracy: 92.293.3%, kappa: 0.86) for mapping baseline extent. The accuracies of the change maps were more variable and were dependent upon the time period between images and number of change features. Total change from 1996 to 2010 was 204,850 ha (127,990 ha gain, 76,860 ha loss), with the highest gains observed in French Guiana (15,570 ha) and the highest losses observed in East Kalimantan, Indonesia (23,003 ha). Changes in mangrove extent were the consequence of both natural and anthropogenic drivers, yielding net increases or decreases in extent dependent upon the study site. These updated maps are of importance to the mangrove research community, particularly as the continual updating of the baseline with currently available and anticipated spaceborne sensors. It is recommended that mangrove baselines are updated on at least a 5-year interval to suit the requirements of policy makers

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Earth resources: A continuing bibliography with indexes (issue 55)

This bibliography lists 368 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1987. Emphasis is placed on the use of remote sensing and geographical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Temporal stability of soil moisture and radar backscatter observed by the advanced Synthetic Aperture Radar (ASAR)

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R-2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments

ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

Urban morphology analysis by remote sensing and gis technique, case study: Georgetown, Penang

This paper was analysed the potential of applications of satellite remote sensing to urban planning research in urban morphology. Urban morphology is the study of the form of human settlements and the process of their formation and transformation. It is an approach in designing urban form that considers both physical and spatial components of the urban structure. The study conducted in Georgetown, Penang purposely main to identify the evolution of urban morphology and the land use expansion. In addition, Penang is well known for its heritage character, especially in the city of Georgetown with more than 200 years of urban history. Four series of temporal satellite SPOT 5 J on year 2004, 2007, 2009 and 2014 have been used in detecting an expansion of land use development aided by ERDAS IMAGINE 2014. Three types of land uses have been classified namely build-up areas, un-built and water bodies show a good accuracy with achieved above 85%. The result shows the built-up area significantly increased due to the rapid development in urban areas. Simultaneously, this study provides an understanding and strengthening a relation between urban planning and remote sensing applications in creating sustainable and resilience of the city and future societies as well

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

Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this fiel