GPU光学与微波卫星数据与地理系统综合处理.pdf

Blending satellite observations for automated monitoring of flood events Ackland R Gouweleeuw B Ticehurst C Thew P Raupach T and Squire G 2012 Blending satellite observations to provide automated monitoring of flood events in Grove J R and Rutherford I D eds Proceedings of the 6 th Australian Stream Management Conference Managing for Extremes 6 8 February 2012 Canberra Australia published by the River Basin Management Society p p 1 10 1 032 Blending satellite observations to provide automated monitoring of flood events Ross Ackland1 Ben Gouweleeuw2 Catherine Ticehurst2 Peter Thew1 Tim Raupach2and Geoff Squire1 1 CSIRO ICT Centre CSIT Building 108 ANU Campus Canberra ACT 2601 Email Ross Ackland csiro au 2 CSIRO Land and Water Clunies Ross St Canberra ACT 2601 Key Points Remotely sensed data is becoming increasingly useful for environmental monitoring There are many data sets available all of which have strengths and weaknesses depending on the application requirements The challenge in providing a service to continually monitor flood events is not lack of data and models but in making the appropriate choices from the wealth of data available Blending data sets can minimize the disadvantages and exploit the advantages of each The ability to map flood events in near real time provides significant benefits to better manage and respond to these events Abstract Increasingly remotely sensed data sources are being used to monitor and manage water resources Satellite derived observations offer the advantage of continental scale coverage at relatively low cost The increasing availability of these data sources and the increase in spatial and temporal resolution being provided by improved sensor technologies both current and planned is creating new opportunities to exploit these data sources This paper examines the application of near real time acquisition of remotely sensed data for the purpose of flood monitoring in terms of measuring flood extent and estimating flood volume at continental scales It provides an assessment of the suitability of three classes of satellite data sources optical passive microwave and radar and how these can be combined in such a way to overcome the limitations of each Additionally we provide an analysis of the timelines of the data that is how close to real time does the data have to be in order for it to be useful for monitoring flood events The spatial and temporal resolution as well as the latency in data acquisition and processing is key to establishing the usefulness of a system that can automatically monitor a large region of interest Keywords Remote sensing optical radar data blending flood extent flood volume near real time Introduction After more than a decade of severe drought the extreme flooding events of 2010 11 in Australia demonstrate the major challenges we face to improve the management of our water resources The need to accurately predict monitor and assess flood events is becoming increasingly important as climate change is shifting the availability of water across the continent both in terms of volume and location While these changes affect our ability to manage environmental sustainability they have significant financial physical and social consequences as evidenced with the Queensland floods which are likely to be the most costly natural disaster in Australia s history In this paper Section 1 provides a brief overview of approaches to flood monitoring and the limitations associated with these approaches it then lists some use cases where satellite data is appropriate or otherwise for flood monitoring applications Section 2 discusses the types of data Blending satellite observations for automated monitoring of flood events Ackland R Gouweleeuw B Ticehurst C Thew P Raupach T and Squire G 2012 Blending satellite observations to provide automated monitoring of flood events in Grove J R and Rutherford I D eds Proceedings of the 6 th Australian Stream Management Conference Managing for Extremes 6 8 February 2012 Canberra Australia published by the River Basin Management Society p p 1 10 2 and processing required in order to provide flood extent and flood volume estimates in near real time approximately one day latency Section 3 provides some discussion based on our experience developing an experimental system that has been running continuously for the last 6 months with some suggestions for future improvements Using satellite data for flood monitoring Approaches to flood mapping and monitoring Traditional approaches to understanding areas at risk of flooding are based on historical data and records collected from observations made by the agencies responsible as well as anecdotal evidence Before the early 1970 s these observations would be local made on the ground during floods or perhaps inferred from occasional aerial photographs Since then the advent of the satellite technology has meant that floods can be mapped continuously and over large areas as well as going back in time using satellite archives With the availability of terrain height maps or digital elevation models DEM s estimating areas of potential inundation can be identified as the area below a certain elevation which would be inundated if river or sea level reached a certain height This technique referred to as the inundation modeling approach is simplistic and easy to implement but does not take into account flow dynamics which is essential to understanding flood response over time and may be in the order of hours days or weeks Hydrodynamic flood modeling based on flow equations take into account the elevation of the terrain as well as other important factors such as river channel shape roughness of the surface obstacles requiring more effort in calibration and significant computational power These models are well suited to flood risk reduction in providing inundation information such as time location and severity of a potential flood scenario but are difficult to apply to large geographic regions due to the complexity of calibration and the need for actual water level measurements to predict from They are well suited to urban flooding where land use planning is influenced by flood hazard Limitations of models and data Flood modelling requires very detailed knowledge and data of the terrain and hydrology to the extent that it is often not available Satellite data is increasingly being used along with ground based observations to provide situation awareness of flood progressions and in retrospective analysis to improve model calibrations Satellite data can provide useful information but cannot assess risk beyond the most extreme event in the satellite image archive Each of these methods modeled or observed or combinations of both have strengths and weaknesses Terrain height maps are sometimes of little use in areas with relatively steep river beds or in flatland areas with very little topography where sheet floods can cover large areas There are also different strengths and weaknesses between satellite data types There is an inevitable trade off between spatial detail and frequency of measurement Some data types go back in time further than others Satellite measurement methods also have different characteristics The first routine flood mapping has been done using optical satellite images from the NASA s Landsat Thematic Mapper instrument resolution 30 80 m measurement frequency every 8 to 16 days but only on cloudless days data since early 1970 s Radar satellite instruments have helped to obtain high resolution information during floods Their main advantage is that they can see through cloud cover and can be tasked to obtain detailed images They do not collect data routinely however The launch of MODIS resolution of 250 500 m twice daily since 2000 and similar optical satellites means that the whole globe is now observed much more frequently although with somewhat less detail and clouds still affecting the image Passive microwave satellite instruments also provide information on flooding regardless of weather conditions but at a much coarser scale km The former has the advantage of being all weather whereas the Blending satellite observations for automated monitoring of flood events Ackland R Gouweleeuw B Ticehurst C Thew P Raupach T and Squire G 2012 Blending satellite observations to provide automated monitoring of flood events in Grove J R and Rutherford I D eds Proceedings of the 6 th Australian Stream Management Conference Managing for Extremes 6 8 February 2012 Canberra Australia published by the River Basin Management Society p p 1 10 3 latter is so frequent that weather permitting even rapid flood events can be observed if they are large enough The most suitable flood mapping method in terms of data and models used depends on the requirements of the application Benefits of remote sensing The use of blended satellite data combined with a hydrologically enforced digital elevation model DEM presents an opportunity to develop flood extent mapping and flood volume estimation across large geographic regions This provides flood extent information in areas both with and without ground based observation networks with the latter adding to the information mix where available Possible use cases where this approach could be useful or otherwise 1 Satellite data is available from archival databases 10 years or more depending on the satellite making it suitable for historical analysis and review to assess and improve flood management plans 2 Satellite data may be near real time but at best likely to be more than 2 3 hours to days from observation This makes currently available data unsuitable for flash flood forecasting and early warning systems where observation data is required on a minute by minute basis 3 Satellite data can cover large geographic extents making it useful in regions where no ground based observations are available ungauged basins regional and rural areas High spatial resolution 0 2 and OWI GVMI EVI when EVI 0 2 determined empirically was adopted as water 2 3 Blending satellite observations for automated monitoring of flood events Ackland R Gouweleeuw B Ticehurst C Thew P Raupach T and Squire G 2012 Blending satellite observations to provide automated monitoring of flood events in Grove J R and Rutherford I D eds Proceedings of the 6 th Australian Stream Management Conference Managing for Extremes 6 8 February 2012 Canberra Australia published by the River Basin Management Society p p 1 10 5 Cloud interference is automatically removed using a quality band that comes with the imagery Production of AMSR E daily surface water images To help increase the number of daily flood images particularly when cloud cover is most apparent the AMSR E passive microwave instrument is used Ashcroft and Wentz 2006 Being a microwave instrument it can collect data day or night and while it is affected by rain it is independent of cloud cover A method described in Ticehurst et al 2009a was used to produce flood maps resulting in a pixel size of 10 km x 10 km This method is based on the polarization ratio PR of the horizontal and vertical polarizations of the 37 GHz microwave band Following a correction due to the influence of vegetation it was regressed against a MODIS OWL image to provide a direct relationship between PR and proportion of water within an AMSR E pixel Any AMSR E pixels containing proportion of water less than 12 determined through multiple MODIS AMSR E water surface comparisons is mapped as having no water due to the confusion between inundated pixels and wet soil as well as the noise in the data Due to the large pixel size a method based on historical MODIS flood extent data was developed to distribute the water within each AMSR E pixel allowing it to provide nearly as much detail as the MODIS data Using OWL flood extent images generated from 8 day composite MODIS data MOD09A1 from 2000 until May 2011 an image was generated where each pixel value was the percentage of time that it had water in it over this time period The proportion of water within any AMSR E pixel is distributed into the areas flooded the longest based on this historical MODIS image As a further measure to help reduce confusion with moist soil any part of an AMSR E image corresponding to pixels in the historical MODIS image which were inundated less than 0 1 of the time are automatically mapped as having no water Production of ASAR GM surface water images Interpretation of radar backscatter can be complex and requires an understanding of the land surface In areas with flooded vegetation the radar backscatter is expected to increase due to the double bounce effect radar bounces off the water surface and is partially scattered back to the sensor by the vegetation above it In contrast open water is characterized by very low backscatter An existing method for mapping water with ASAR GM Ticehurst et al 2009b relies on the latest available Enhanced Vegetation Index EVI derived from MODIS to determine the amount of vegetation in each radar pixel to know how to interpret the backscatter First the EVI was divided into classes with each showing a positive relationship between MODIS OWL and ASAR backscatter Like the AMSR E method the MODIS OWL was used to develop a relationship between water proportion and backscatter The historical MODIS image was also used to mask out regions that were unlikely to contain surface water in the ASAR GM data Combining MODIS AMSR E and ASAR data In order to produce daily maps of surface water the MODIS AMSR E and ASAR in the future water images are combined such that all valid MODIS data are used as priority Where there are nulls or cloud interference in the MODIS water map the valid AMSR E water pixels fill in the gaps In future the ASAR water map will also help fill in the remaining gaps and possibly take priority over the AMSR E water maps Digital elevation data The digital elevation model used in this study DEM Version 1 0 available from the Geoscience Australia web site is a 1 arc second 30m gridded DEM derived from NASA s Shuttle Radar Topography Mission SRTM The DEM represents ground surface topography and excludes vegetation features The dataset was derived from the 1 second Digital Surface Model DSM by automatically removing vegetation offsets identified using several vegetation maps directly from the DSM The removal of vegetation effects has produced satisfactory results over most of the continent and areas with defects are identified in the quality assessment layers Blending satellite observations for automated monitoring of flood events Ackland R Gouweleeuw B Ticehurst C Thew P Raupach T and Squire G 2012 Blending satellite observations to provide automated monitoring of flood events in Grove J R and Rutherford I D eds Proceedings of the 6 th Australian Stream Management Conference Managing for Extremes 6 8 February 2012 Canberra Australia published by the River Basin Management Society p p 1 10 6 distributed with the data The grid spacing is 1 second in longitude and latitude approximately 30 metres This data set has the advantage of continental coverage for Australia While other datasets are available at higher resolutions for regions in Australia none provide the same geographic extent in a single data set Generation of Water Depth Maps Flood extent maps generated from either MODIS AMSR E or ASAR data can be georeferenced to the DEM Gallant and Read 2009 and a water surface generated Thew et al 2010 To do this the edge of each water body in the flood extent image determines the DEM height of the water s edge A triangulation method and smoothing algorithm is used to generate the height of the water surface The water surface height is then subtracted from the DEM to produce water depth for every water pixel in the image Figure 1 It is then a simple calculation to provide instantaneous water volume for any water body in the image It must be noted that any errors in the mapped flood extent DEM heights or geometric mismatch between the flood extent maps and the DEM will result in errors propagating through to the water depth images Figure 1 Water depth maps draped on a MODIS image for a flood event in the Fitzroy River WA in 2002 Left 27th February Right 6th March Validation work All data products are undergoing a validation process to determine their reliability To date the MODIS and AMSR E flood maps have been compared to Landsat TM water maps for the Fitzroy River Western Australia and the lower Balonne floodplain Queensland When the images are classified into water 50 OWL and non water 50 OWL the MODIS water map agrees with the Landsat water map 90 5 for the Fitzroy and 86 6 for the lower Balonne region For the AMSR E water maps this agreement drops to 85 for the Fitzroy and 73 for the lower Balonne respectively Figure 2 Landsat MODIS and AMSR E flood map image of Condamine Balonne floodplain 21st January 2011 Blending satellite observations for automated monitoring of flood events Ackland R Gouweleeuw B Ticehurst C Thew P Raupach T and Squire G 2012 Blending satellite observations to provide automated monitoring of flood events in Grove J R and Rutherford I D eds Proceedings of the 6 th Australian Stream Management Conference Managing for Extremes 6 8 February 2012 Canberra Australia published by the River Basin Management Society p p 1 10 7 As expected MODIS does not provide the fine detail as seen in the Landsat water map however the general water outline is very similar There are obvious differences between the AMSR E and the MODIS including the bulkiness of the AMSR E due to its large pixel size 10km x 10km compared to MODIS 500m x 500m What is also apparent is some remnant confusion between surface water and wet soil in some of the AMSR E pixels Overall the AMSR E flood extent map compares remarkably well considering its origins When pixel water proportion from the Landsat water maps and MODIS water maps are regressed based on a 1 1 relatio