In the event of a natural disaster, remote sensing is a valuable source of spatial information and its utility has been proven on many occasions around the world. However, there are many different types of hazards experienced worldwide on an annual basis and their remote sensing solutions are equally varied. This paper addresses a number of data types and image processing techniques used to map and monitor earthquakes, faulting, volcanic activity, landslides, flooding, and wildfire, and the damages associated with each. Remote sensing is currently used operationally for some monitoring programs, though there are also difficulties associated with the rapid acquisition of data and provision of a robust product to emergency services as an end-user. The current status of remote sensing as a rapid-response data source is discussed, and some perspectives given on emerging airborne and satellite technologies.
Agricultural land-use statistics are more informative per-field than per-pixel. Land-use classification requires up-to-date field boundary maps potentially covering large areas containing thousands of farms. This kind of map is usually difficult to obtain. We have developed a new, automated method for deriving closed polygons around fields from time-series satellite imagery. We have been using this method operationally in New Zealand to map whole districts using imagery from several satellite sensors, with little need to vary parameters. Our method looks for boundarieseither step edges or linear features-surrounding regions of low variability throughout the time series. Local standard deviations from all image dates are combined, and the result is convolved with a series of extended directional edge filters. We propose that edge linearity over a long distance is a more important criterion than spectral difference for separating fields, so edge responses are thresholded primarily by length rather than strength. The resulting raster edge map (combined from all directions) is converted to vector (GIS) format and the final polygon topology is built. The method successfully segments parcels containing different crops and pasture, as well as those separated by boundaries such as roads and hedgerows. Here we describe the technique and demonstrate it for an agricultural study site (4000 km 2) using SPOT satellite imagery. We show that our result compares favorably with that from existing segmentation methods in terms of both quantitative quality metrics and suitability for land-use classification.
Geological hazards and their effects are often geographically widespread. Consequently, their effective mapping and monitoring is best conducted using satellite and airborne imaging platforms to obtain broad scale, synoptic coverage. With a multitude of hazards and effects, potential data types, and processing techniques, it can be challenging to determine the best approach for mapping and monitoring. It is therefore critical to understand the spatial and temporal effects of any particular hazard on the environment before selecting the most appropriate data type/s and processing techniques to apply. This review is designed to assist the decision-making and selection process when embarking on a hazard mapping or monitoring exercise. It focuses on the application of optical, LiDAR, and synthetic aperture RADAR technologies for the assessment of pre-event risk and post-event damage. Geological hazards of global interest summarized here are landslides and erosion; seismic and tectonic hazards; ground subsidence; and flooding and tsunami
Four iterations of the New Zealand Land Cover Database have been produced from satellite imagery for nominal dates of 1996/97, 2001/02, 2008/09 and 2012/13. These data may be used to estimate changes in area for land cover classes of interest. However, these estimates are subject to uncertainty, which can be significant, particularly when change in area is small. Changes in indigenous vegetation classes are of interest for a number of applications, including monitoring threatened environments. Here we show how the combination of exhaustive sampling of change polygons with random 'truth' sampling can be used to estimate the uncertainty of area change. We demonstrate the method on five important indigenous covers: indigenous forest, broadleaved indigenous hardwoods, manuka and/or kanuka, tall tussock grassland, and subalpine shrubland. For these classes, we estimate their area in 2008/09 and the change of area between 2001/2002 and 2008/09. Areas were estimated to within plus or minus 5%. Change in areas were estimated to within plus or minus 10% of change for classes with a large change in area, and to within plus or minus 30% for the classes with a small change in area. We anticipate similar uncertainties for estimated changes in area between the other dates and for other classes. The number of random 'truth' samples required for this assessment was very high, in excess of 30 000. Many more samples would be required to further lower the uncertainties.
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