15arspc Submission 184

8/8/2019 15arspc Submission 184

1/10

1

SEGEMENTATION OF MULTI TEMPORAL RADAR FORWETLAND INVENTORY

Geoffrey Horn

School of Biology, Earth and Environmental Science, UNSW

209 Cobra St, Dubbo, NSW, 283002 6841 7515

[email protected]

Abstract

The Wetlands Policy of the Australian Government states the need to identifyand complete national wetland inventories for all wetlands in Australia. Animportant part of inventory is the mapping of wetlands. However, the wet/dry

tropical climate in the north of the continent has yet to be mapped to a baselinelevel with any reasonable accuracy. The highly complex seasonal cycling in theregion means that any attempt at mapping the location of a particular wetlandcomponent must account for the potential change throughout the seasonalcycle.

Optical remote sensing systems are non optimal for the wet/dry tropicalwetlands of Northern Australia because the area is almost continually coveredby either clouds or smoke. Radar imagery is insensitive to both smoke andcloud. A multi-temporal approach to the situation is well suited to the highlydynamic nature of the wet/dry tropics of northern Australian and Kakadu

National Park in particular.This paper presents results regarding how multiple date radar data can beprocessed to gather information about the extent of wetland inundation, theperiod of wetland inundation and the pattern of drainage. The speckle problemsassociated with radar imagery were avoided by using a customisedsegmentation program written specifically for this research. Analysis of thesegments to extract information for wetland inventory was achieved by usingrobust thresholds for each distinct wetland component and inventoryrequirement. Once defined, these thresholds were applied and the resultsassessed to determine the accuracy of the results.

The research is significant because it creates new ecological information for theregion. The application of a new segmentation technique and wetland inventoryinformation based on segment analysis are the key results of this research.

Introduction

Wetlands have been variously defined by different organisations, from differentperspectives or for different purposes, which has led to some confusion. Thedefinition of wetlands used in this research is from the Ramsar Convention,which states wetlands are Areas of marsh, fen, peatland or water, whethernatural or artificial, permanent or temporary, with water that is static or flowing,fresh, brackish or salt, including areas of marine water the depth of which at lowtide does not exceed 6 metres (Ramsar Convention Secretariat, 2006).


2/10

2

Globally, little information has been compiled for wetland areas, particularlyfrom an inventory perspective (Finlayson et al. 2001; Phinn et al. 1999). Thefundamental components of a wetland inventory are wetland area, boundary,location and geomorphic setting, all of which can be captured from remotely

sensed imagery (Phinn et al., 1999; Finlayson & von Oertzen, 1993).One of the major goals of the Wetlands Policy of the Australian Government isto conserve, repair and manage wetlands wisely (Commonwealth Governmentof Australia 1997). As an implicit part of this policy, accurate and up-to-dateinformation about existing wetlands in Australia is required. One of the majorstrategies of the policy is to Develop and maintain a comprehensive inventoryof wetlands on Commonwealth lands and waters, areas leased by theCommonwealth agencies and areas under co management arrangementsinvolving the Commonwealth.(Commonwealth Government of Australia 1997).

Developing wetland inventories in tropical northern Australia is difficult for two

main reasons. Firstly the area is blanketed by either cloud or smoke cover mostof the year, making cloud free optical images rare. Secondly, the regionundergoes an annual cycle of wet to dry conditions, with the timing and extentof the cycle varying from year to year. The wetlands are part of this dynamicsystem, changing rapidly with the onset of the wet season, and so a multi-temporal approach is required in their mapping. These problems can beovercome by the use of radar imagery, which is not affected by clouds orsmoke, and is available throughout the year.

However, Radar data is intrinsically noisy and is difficult to use with manystandard classification techniques. Radar noise, termed speckle, is

encountered in all radar imagery as a result of backscatter from severalstructural elements interacting in a multiplicative fashion, leading to additive anddestructive interference in the returned wave of each imaging cell (Ulaby et al.1986). Applying standard classification techniques to radar imagery producesnoise affected results (Crawford & Ricard 1998). Preprocessing the radarimagery, through segmenting areas of similar pixel values and textures reducesnoise and improves classification results (Haralick & Shapiro 1992; Caves &Quegan 1998).

Segmentation of radar imagery is relatively complex with the mathematical logicfor segment boundary placement difficult to reproduce consistently (Nussbaum& Menz 2008). The use of a robust and time-independent technique is requiredto allow the valid comparison of time series data. Others have developedtechniques that require varying segmentation parameters by image acquisitiondate, although this introduces a subjective component giving a less reliableresult (Nussbaum & Menz 2008). This paper presents a method that avoidssubjective segmentation parameters. When applied to multi temporal radarimagery this method allows the extraction of wetland inventory information fromthe resulting segments. As Radarsat-1 data has a 24-day repeat cycle andconsistent viewing geometry change detection can be easily performed.

MethodSegmentation can be used to reduce the impact of speckle on radar imagery,

and can be used to identify sub-populations of backscatter within a previously


3/10

3

continuous distribution. Segmentation takes multiplicative noise into account, byusing the statistics of a small region to determine its association with itssurrounding neighbours. If the segment is statistically the same as itsneighbours it is joined with them to produce a bigger segment. However, if the

statistics of two neighbouring regions are dissimilar, a boundary is drawn andthe two regions are kept separate. For each segment it is the statistics of thethat are recorded in the final output, namely that of the segment mean and itsstandard deviation, which is a measure of texture. A custom segmentationsoftware routine was written in C++ (Borland C++ Builder) to take an input radarimage and divide it into a number of adjoining segments where each segmentmember pixel is statistically similar to other member pixels. A full description ofthe method is detailed in Horn (2009).

Once image noise is reduced through segmentation the images can beclassified using standard thresholding. Thresholds can be based on an

understanding of the way radar physically interacts with different wetlandcomponents. Both open water and flooded forest are readily identifiable in radarimagery due to two distinctly different interactions with incident radiation.

Open water is relatively smooth with respect to incident radar beams and soscatters most of the energy away from the receiving antenna, giving a very lowreturned value in the subsequent imagery.

Flooded forests (e.g. Melaleuca or paperbark swamps) are areas inundatedwith freshwater for long periods and have extensive areas of emergent trees.These areas typically occur at the upland margin of the floodplain surfaces. Theincident radar beam reflects from the water to the trunk and back to the receive

antenna at a very high fraction of the original energy, resulting in an extremelyhigh backscatter response (Richards et al, 1999). This response is typicallymaintained while the water level remains high, and has a uniform texture (lowstandard deviation) in areas of almost permanent inundation. Backscattervalues of above -8 dB are typical of these areas after segmentation.

In order to determine wetland extent, thresholds can be applied to the mean ofthe time series. Inundation period could be determined by counting the numberof instances at which a location was within the wetland threshold.

Because the defined thresholds have a physical justification (such as specularreflection for open water or a double bounce for flooded paperbark swamps)

they should be consistent with time after calibration of the imagery. For a givenseason, the same threshold should apply to a similar season image from adifferent year.

Application

Study Site

Kakadu National Park covers an area of approximately 20 000 km2

in theNorthern Territory of Australia. The Park was established in 1979. The parkcontains a diverse range of habitats and ecological communities, from uplandEucalypt woodlands totalling 47 000 ha to vast floodplains of approximately 147000 ha (Finlayson & Moser 1991). Areas of paperbark and mangrove swamps


4/10

4

are also found in the park. (Finlayson et al. 2001; Finlayson & von Oertzen1996)

An image subset from the Munmalari area was used as a type example of thepaperbark swamp landscape element (Figure 1). The subset is dominated by alarge ENE-WSW trending paperbark swamp, surrounded to the north and southby eucalypt woodland (Figure 2). To the southwest of the image subset, is theright bank of the South Alligator River and the associated floodplain complex. Inthe NE corner is a perched window lake called Alligator Hole with associatedoutwash channel running to the north.

Figure 1. Image subset location within Kakadu National Park


5/10

5

Figure 2. Paperbark image subset, Landsat 3,2,1 as R,G,B

A series of 11 Radarsat-1 (C-HH) scenes were collected from January toOctober 1998 in a 24 day repeat cycle. Each image was collected in the samestandard 4 beam mode with an incidence of 34-40

o. The image data covered a

100 km by 100 km footprint with a pixel size of 12.5 m. Each image wasrectified and converted to backscatter coefficient (

0). After the images had

been rectified and calibrated, they were segmented using the customsegmentation software described earlier resulting in a two band image(backscatter mean and standard deviation). The segmentation results werethen stacked into a single image and the mean and range for the entire serieswas calculated.

Primary thresholds for the main landscape elements were determined from on-ground investigations (Table 1). The thresholds were then applied to generatelayers for each landscape element, from which the wetland extent andinundation maps were derived.


6/10

6

Landscape element Mean (dB) Range (dB)

Open Water < -15 any

Floodplain -8 > 7.5

Paperbark > -8 7.5

Woodland > -12 and < -8 any

Table 1. landscape element thresholds.

Accuracy assessment

An accuracy assessment was undertaken using a stratified random distributionof 10 000 points. Wetland extent was compared with an existing structuralvegetation classification (Environmental Data Directory, DEHA 2008). For thepurposes of the analysis each of the map categories was grouped into awetland or non wetland category. Stratification was performed proportional tothe extent of each reference class.

Results

The subset image shows a good relationship between the identified wetlandclass and the reference dataset (Figure 3). Some commission errors areevident in the open forest and woodland classes. The floodplain and paperbarkswamp classes are well matched.

class Users Accuracy Producers Accuracy

wetland 0.526 0.739

non wetland 0.915 0.808

Table 2. Users and producers accuracy for wetland extent layer.

The Kappa statistic was 0.479 which indicates that the wetland extent layer hasa moderate agreement with the existing vegetation layer. Users accuracy isrelatively low (0.526) for the wetland class due to the confusion with the openforest-woodland and pandanus savannah classes (Table 2). Producersaccuracy for the wetland class is higher (0.739) as the measure is not sensitiveto omission errors. Using simple thresholds to determine wetland extent workedwell for some extreme landscape elements such as paperbark swamp andopen water and less well for more similar elements such as woodlands and midfloodplain classes.


7/10

7

Figure 3. Wetland extent for the Paperbark Subset


8/10

8

Figure 4. Paperbark subset inundation period.


9/10

9

Inundation period was ascertained by the number of times a pixel was classifiedas open water or paperbark swamp. By counting the number of times an areawas classed as inundated, the period of inundation in 24-day increments wasmapped. Inundation ranged from 0 to 11 occurrences across the 11 image

dates. These occurrences were multiplied by the number of days betweenimages (24) to create an inundation period in days.

The subset image shows a strong relationship between inundation period andpaperbark vegetation (Figure 4). The paperbark swamp areas are dominated byextended water residence from 145 to 240 days. This is a definingcharacteristic of the class and indicates the ease with which double bouncecharacterises flooded forest. The floodplain area in the Southwest of the imagehas a good agreement with inundation period expected from the floodplaintopography, with a general increase in water residence with increasing distancefrom river channel.

Conclusion

Tropical northern Australia presents unique difficulties for establishing wetlandinventories. Smoke and cloud coverage and the dynamic landscape require acombination of longer wavelength imaging and multi temporal sampling. Thespeckle problems inherent with radar data have been overcome through asegmentation routine, and information has been extracted for the resultingsegments using robust thresholds.

This paper presents the initial results for wetland extent and inundation periodfrom a subset of the full dataset. Further research is required to assess theaccuracy of the inundation period results and fine tune the segmentationroutine. The extraction of wetland extent and inundation period maps frommultiple radar images shows a great deal of promise for wetland inventory.

References

Commonwealth Government of Australia, 1997, The Wetlands Policy of theCommonwealth Government of Australia, Australian Government PublishingService, Canberra.

Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E., 2004 Digitalchange detection methods in ecosystem monitoring: a review, International

Journal of Remote Sensing, vol. 25, no. 9, pp. 1565 1596.Drury, S.A., 1993, Image Interpretation in Geology, 2

nded, Chapman & Hall,

London.

Environmental Data Directory, DEHA. 2008. http://www.environment.gov.au/metadataexplorer/explorer.jsp.

Finlayson, M.J., and Moser , M., 1991, Australasian and Oceania in: Finlayson,C.M. and Moser, M. (eds.) Wetlands. International Waterfowl and WetlandsResearch Bureau, United Kingdom.

Finlayson, C.M., and von Oertzen, I., 1993, Wetlands of Australia Northern

(tropical) Australia, in D. F. Whigham, D. Dykyjova & S. Hejny (eds), Wetlands


10/10

10

of the world I: inventory, ecology and management, Kluwer AcademicPublishers, Dordrecht, pp. 195-243

Finlayson, C.M., and von Oertzen, I., (eds)., 1996, Landscape and vegetationecology of the Kakadu region, Northern Australia, Kulwer Academic Publishers,London

Finlayson, C.M., Davidson, N.C., and Stevenson, N.J., (eds)., 2001. Wetlandinventory, assessment and monitoring: Practical techniques and identificationof major issues. Proceedings of Workshop 4, 2

ndInternational Conference on

Wetlands and Development, Dakar, Senegal, 8-14 November 1998,Supervising Scientist Report 161, Supervising Scientist, Darwin

Hess, L., Melack, J., and Simonett, D., 1990, Radar detection of floodingbeneath the forest canopy: a review, International Journal of Remote Sensing,vol 11, no 7, pp1313-1325.

Horn, G.D., 2009, Segmentation of multi-temporal radar images for wetlandinventory. Unpublished Ph.D thesis, School of Biology Earth and EnvironmentalSciences, UNSW, Sydney Australia.

Kasischke, E.S., Melack, J.M., and Dobson, M.C., 1997, The use of ImagingRadars for Ecological Applications -A Review, Remote Sensing of Environment,vol. 59, pp 141-156.

Lillesand, T. and Kiefer, R., 1994. Remote Sensing and Image Interpretation 3rd

ed. John Wiley & Sons, New York, U.S.A.

Phinn, S.R., 1998, A framework for selecting appropriate remotely sensed datadimensions for environmental monitoring and management, InternationalJournal of Remote Sensing, vol. 19, no. 17, pp.3457-3463.

Phinn, S.R, Hess, L., and Finalyson, C.M., 1999. An assessment of theusefulness of remote sensing for coastal wetland inventory and monitoring inAustralia. In Finlayson C.M. & Spiers A.G. (eds). Techniques for enhancedwetland inventory and monitoring. Supervising Scientist Environment Australia,Canberra.

Ramsar Convention Secretariat, 2006. The Ramsar Convention Manual: aguide to the Convention on Wetlands (Ramsar, Iran, 1971), 4th ed. RamsarConvention Secretariat, Gland, Switzerland.

Richards, J.A., Sun, G.Q., and Simonett, D.S., 1987, L Band radar backscattermodelling of forest stands, IEEE Transactions on Geoscience and RemoteSensing, vol. 25, no. 4, pp.487-498.

Story, M., and Congalton, R.G., 1986, Accuracy assessment: a usersperspective. Photogrametric Engineering and Remote Sensing, 52, 397 399.

Ulaby, FT, Moore, RK & Fung, AK 1986, Microwave remote sensing, Active andpassive, volumes I-III, Artech House, INC. Dedham, Massachusetts.

15arspc Submission 184

Documents

Transcript of 15arspc Submission 184