15arspc submission 11

8/8/2019 15arspc Submission 11

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BUSH HERITAGE AUSTRALIA AND THE PLANET ACTIONPROGRAM, GENERATING AND USING REMOTELY SENSEDINFORMATION WITHIN A NOT-FOR-PROFIT CONSERVATION

ORGANISATION.Richard MacNeill B.A.; Dip. Ed.; B. App. Sc.; M. App. Sc.

Bush Heritage Australia5/395 Collins Street, MELBOURNE, VIC 3000

(03) 8610 9110, (03) 8610 [email protected]

Abstract

Since 1991 Bush Heritage Australia, a privately funded not-for-profit

conservation organisation, has purchased properties and worked with partnersto protect and foster rare and threatened ecological communities. BushHeritage currently owns and manages 34 properties across Australia, a totalarea of almost a million hectares, and has plans to manage, either directly or inpartnership, 1% of the Australian landmass by 2025.

The Planet Action program (www.planet-action.org), a Spot image initiative,provides geographic information and technology to support projects across theworld acting on climate change-related issues. Bush Heritages involvement inthe program began in 2008 and was prompted by the opportunity to investigateremotely sensed imagery and systems until recently beyond the resources andfinances of the private not-for-profit sector. This involvement involved (1)investigating the use of SPOT imagery to delineate and characterize change inthe state and activity of plant communities by analysing and comparing NDVIvalues, and (2) integrating remotely sensed information into ecologicalmanagement planning and field-work carried out on Bush Heritage reserves.

This presentation will describe the results of this work within two areas ofsouthwestern Western Australia: Eurardy Station, a Bush Heritage reserveNorth of Geraldton and Gondwana Link, a partnership area within theSouthwestern Botanical province, one of the worlds biodiversity hot-spots.

Introduction

Bush Heritage is a not-for-profit organisation that protects rare and threatenedplant and animal communities through a combination of direct land purchaseand pastoral, Indigenous and business partnerships. Since its inception in 1990,it has purchased some 31 properties totaling close to 1 million hectares.

The properties owned and managed by Bush Heritage range in size from blocksof less than 10 hectares to immense pastoral properties over 230,000 hectaresin size. The ecological values within these reserves range from the cooltemperate forests of its Tasmanian reserves to the dune fields of Southwestern


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Queensland. The most remote of Bush Heritages properties is located a fourhour journey from the nearest neighbour, provided that the tracks are open.

Bush Heritage faces a number of unique challenges in providing effectivemanagement for its reserves. These include (1) its status as a not-for-profitorganisation, with its consequent financial and resource related uncertainties;(2) the need to manage the complex and subtle ecologies on all its reservealongside the responsibility for managing the facilities and infrastructure of apastoral station within a pastoral community; and (3) the need to continuallymonitor and report on management and activities specifically intended topromote change as the ecology of the property is fostered and restored.

This last challenge is of particular importance, because Bush Heritageacknowledges that its central responsibility is to protect and foster threatenedeco-systems, and it is to this that it is held accountable.

This paper will describe principles, methods and systems used during one yearof work using imagery supplied by SPOT image to delineate and characterizechange related processes, and will present two examples illustrating the resultsof this work. Because the methods used, technology adopted and informationsynthesized must represent practical opportunities for a not-for-profit agency oflimited means to adopt in the longer term, this paper will conclude with anassessment of the practicality, cost-effectiveness, rigour and sustainability ofthe results of the project and the methods used to obtain them.

The Southwest Australia Climate Change project

In February 2008, SPOT Imageoffered BushHeritage the opportunity to participate in thePlanet Actionprogram by developing the firstof its projects in the Australasian region. Spot

Image, a leading supplier of satellite imageryand geo-information and ESRI, a leading GIStechnologies provider, launched the PlanetActionprogram in 2007. The purpose of theprogram is to support local Climate Change-related projects undertaken by NGOs,universities and research centres by providinggeographic information and technology.SPOT Image has supported the SouthwestAustralia Climate Change project by promptlysupplying satellite imagery and technicaladvice. Further technical advice and

assistance was kindly provided by Dr IsobelCoppa of the Cooperative Research Centrefor Spatial Information, and by JeremyWallace (CSIRO, Western Australia). Bush Heritage Australia acknowledgesand thanks these and others who have provided technical advice andassistance.

Figure 1: project areas of interest


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Bush Heritage selected two areas in southwestern Australia (figure 1) as thefocus for the use of satellite imagery to derive plant growth and condition valuesacross a temporal series of images and to analyse and assess changes inthese values. Both these areas lie within the Southwest Botanical Province, aninternationally recognised ecological hotspot characterized by localized,

complex and subtle combinations of ecological communities threatened bypastoral land use, population expansion, erosion and wildfire.

The complexity and localized specialization of plants and plant communities inthis area is related to the long term stability of the landscape of the SouthwestBotanical Province and the conditions affecting it. It is this stability that led tohopes that the often subtle and progressive effects of climate change inAustralia could be more readily isolated in a region where seasonal variation isless marked and the clearances, invasions and broad changes that haveaffected other areas of Australia have been, largely, absent.

The project comprised two complementary stages, both focussing on the use ofsatellite imagery to derive Normalised Difference Vegetation Index (NDVI)

values providing information relating to the condition, growth patterns andresource usage of plant communities. The first stage reviewed a temporalseries of images to present indicators of change in a graphical form able torepresent characteristics of processes of change. .

The objective of the second stage of the project was to generate specificallyspatial information relating to the character of temporal change across an area.As with the first stage, NDVI values formed the basis of this work. Lessonslearned in the first stage were applied when correcting and aligning imageseries in the second.

Principles

The potential for satellite imagery to provide spatial data relating to changingconditions and characteristics of vegetation across the landscape has beenrecognised and regularly applied over of the last decade. A brief survey ofactivity notes the use of imagery to define and classify characteristics of changethat relate to a known or expected phenomenon or process, for instance landdegradation, across a broad area (Thompson et. al 2009), to augment theprocess of monitoring vegetation condition (Wallace, Behn and Furby 2006);Wallace et. al. (2004), and to investigate methods of integrating diverse sourcesof remote sensing data into a temporal series of maps (Petit and Lambin, 2001).

A theme that runs through many of these sources is the development of staticderived information, whether digital maps or classified images. This studyassumes that the product need not be static, but can be reviewed and thetechniques used to develop and display it altered according to the situation andcontext. Spatial systems able to rapidly and intuitively integrate and analysedata in or close to the field have recently become more available and cost-effective, and have dramatically reduced the distances between analysis andresults, data and information and the laboratory and the field. Visuallyreferenced spatial information, then, is becoming an extension of visual


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information available to ecologists and other land managers as they plan forand undertake their work in the field.

In choosing techniques for generating, displaying and using information, thisproject adhered to three principles:

1. Information generated must extend the range of information currentlyused by ecologists and land managers and be essentially heuristic inthat it guides the user to a conclusion rather than proposing one

2. Techniques and resources used must be practical, cost-effective andsustainable for a not-for-profit agency of limited means.

3. The horse must lead the cart. The results must aid the assessment,and so must be acceptable within existing structures and processes,and by ecologists and other practitioners.

Statistics, techniques and software

In planning this project, the first considerations were: did Bush Heritage have

the capacity and resources to complete it, and what level of investigation wouldprovide the best and most sustained use to the organisation, while remaininginnovative and relevant to the broader ecological and conservation community?

Systems within Bush Heritage that supported land management planning andoperations already included current versions of GIS and image analysissoftware capable of sophisticated management and analysis tasks. Inaddressing these considerations, the project needed to look most closely at howresults would be used, and how best to ensure that results continued be ofbenefit into the future.

To ensure that Bush Heritage was able to complete the project, provideinnovative and practical results, and be able to make use of them over the

longer term, the project selected methods and algorithms for their practicality,ease of use, familiarity of process and degree of support in existing and readilyaccessed software. These comprised (1) NDVI values: essentially ratios ofmass against chlorophyll content, (2) variance: a basic and familiar exploratorystatistic and (3) Principle Component Analysis: a familiar and long-used meansof deriving hierarchies of change.

NDVI

The decision to focus on characteristics of plant communities relating to theratios of moisture/chlorophyll to mass, rather than more visible characteristics,was made in order to extend the range of information available to the userbeyond that available to the naked eye. While vegetation index is a widely used

and readily computed value, it appeared more likely to provide information thatwas either already known by an individual familiar with the locality and itshistory, or capable of being gathered in ways already in use.

NDVI has been widely used to support analysis of variation in plant relatedcharacteristics within an image. Examples of this analysis include Pettorelli et.al (2009) and Thompson et. al. (2009). NDVI supports research into an


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increasing range of more complex and refined indices. Examples of recentresearch are Roderick et. al. (2001), Berry et. al. (2007) and Keith et. al. (2010).

This project used NDVI values as indicators of plant qualities relating tocondition and growth patterns. NDVI was selected because (1) its shortcomingsand uses are clearly described and a large body of case studies exists allowingit to be used effectively and results interpreted with appropriate caution, (2) thealgorithm is straightforward and can be performed from first principles in moststandard image analysis modules and (3) an underlying principle that allinformation is used heuristically means that, provided the limits of the processwere known, the simpler and more readily performed processes are preferable.

NDVI analysis is available in ERDAS imagine and ERmapper as a self-contained module that will select appropriate bands from an image thatcorrespond to the satellite type entered by the user. Importantly, the algorithmcan be entered manually using the ArcGISmap-algebra suite. Readily availablefree-ware image analysis packages such as Copenhagen Image ProcessingSystem(University of Copenhagen) can also support NDVI analysis.

Variance

Variance is a basic statistic whose character needs no introduction. This projectused variance as an immediately accessible summary spatial statistic. Clearly, itcannot provide statistically meaningful values for the limited numbers of imagesused. However, it allows an immediate representation of variation that can bereproduced in most image analysis modules and is present, either as the varfunction or the square of the STDfunction in map-algebra compilations.

Principle Component Analysis

Principle Component Analysis (PCA) is an established method of compressingimages and identifying hierarchies of elements influencing variation within adata set. Smith (2002) provides a detailed but clear explanation of theunderlying theory and mathematics. PCA is present in the ArcGIS suite ofmultivariate functions as the princompfunction. The output for this function is amulti-band image whose number of images correspond to the number ofcomponents selected by the users or the number of images selected for theanalysis.

Because PCA analyses all elements of variation, it was expected that the mostobvious sources of variation will be apparent in the primary levels of thehierarchy, corresponding to the first bands of the output image. These sourcesincluded landform, vegetation structure and area based features. The mostuseful aspect of PCA is that it allows these more obvious sources of variation to

be stripped off, and the focus of the spatial analysis and review to relate to themore subtle aspects of vegetation across the landscape, reflected in NDVIvalues corresponding to growth and condition.

Software

This project used four software systems, ArcGIS (ESRI Corp), Vegmachine(CSIRO), ERDAS Imagine (ERDAS Inc.) and the ERDAS ATCOR module(Geosystems GmbH).


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ERDAS Imagine contains a broad range of tools supporting image correctionand analysis. These range from spectral and radiometric enhancement andcorrection to generating and interpreting imagery. NDVI is included as aspectral enhancement index.

The ATCORmodule of ERDAS Imagineprovides the tools necessary to correctthe image for atmospheric effects and sensor bias, leaving values thatcorrespond to spectral characteristics differentially reflected from the landscape.

Vegmachine, developed and provided through the CSIRO, Perth, comprisestools for reviewing imagery, delineating areas of interest and retrieving valuesacross a temporal series of overlaid images. Vegmachinepresents these valuesas a series of lines on a graph, each line corresponding to an area of interest.The results can be exported to a spread-sheet for more detailed analysis.Vegmachine is a highly useful means of efficiently reviewing and comparingvalue trends.

ArcGIS is a widely used tool for generating, managing and producing spatialinformation. The software allows images to be generated and overlaid with arange of spatial data. ArcGIS is the standard spatial system within BushHeritage, and was used to spatially align image series, generate and representspatial summaries of change based on PCA and variance and present resultsoverlaid with local landscape features and field observations.

Examples

This project summarised and interpreted change in two ways, graphical andspatial. The first made use of a spread sheet to collate and represent values(figure 2). The second summarised and represented temporal change across anentire image, able to be interpreted by overlaying a range of spatial data.

Stage one exampleThe first stage of the project focussed on Bush Heritages Eurardy reserve(figure 1), and investigated the potential of temporal overlays of NDVI imageryto provide information relating to the effects of conditions, events and processeson rates and levels of growth. No attempt was made to use this imagery todetermine the identity of the vegetation communities; it was assumed thatsufficient ecological knowledge existed to establish this independently.

Eurardy reserve, located 45k inland and to the North of Geraldton, WesternAustralia, lies on the fringe of the South-western wheat-belt. The property, awheat farm purchased by Bush Heritage in 2005, includes large areas of nativevegetation that, while historically grazed, were not considered appropriate for

crops. These areas include threatened York Gum dominated woodlandcommunities.

Because this property was farmed for over 2 decades, knowledge andinformation relating to the character and features of the area is relativelyextensive.

This method assembled a temporal series of satellite images using the SPOTimagecatalogue. Images supplied by SPOT imagewere then spectrally refined


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virtually no recovery (figures 4, 5 and 6). In addition, the change in valuesappears to have occurred before the recorded fire. It is probable that the fireoccurred in late 2003, rather than 2004.

The results of the first stage presented information related to an event and itseffects as a spreadsheet comparing progressive NVDI values for a locality anda range of reference and control areas. This example demonstrates thisprocess and confirms that this method can provide information able tocharacterise patterns of change underlying apparent or inferred events.

Stage 2 example

The method used for the second stage of theproject produced spatial distributions of statisticssummarising temporal change across a broadarea. In contrast to the first stage, thisinformation was generated independent of anychoices and selections from the observer.

Validating this source and character ofinformation required detailed field-work designedto assess the relationship between thisdistribution and features, events and processesvisible in the landscape.

The study area for the second stage includedthe catchment of the Pallinup creek, betweenthe Stirling range and Fitzgerald National Park inSW of Western Australia, and focussed on oneof Bush Heritages reserves: the Chereninupreserve (figure 7).

Chereninup reserve lies within an area that has been increasingly cleared andfarmed over the past decades, but remains intact. Disturbance over the lastdecades has been limited to the clearance of a fire break in the 1980s.

The second stage of the project used a temporal series of SPOT imagery,corrected and aligned in the same way as for the first stage, to generate asingle summary image able to provide information on the variations in the extentand character of change across the area of the entire image.

Figure 4: satellite imagery1986(SPOT)

Figure 5: satellite imagery2004(SPOT)

Figure 6: satellite imagery2009 (Landsat)

Figure 7: stage 2 focus area


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In order to review the more subtle aspects of change, summarised within asingle image, the effects of other more dominant sources of variation in theimage sequence must be minimised. Incremental and event related changecannot be predicted and so accounted for. However, the general pattern ofseasonal change can be determined by examining annual rainfall records and

the effects compensated for by undertaking the same analysis for contrastingseasons.

A review of rainfall figures over the last 92 years revealed a general differencebetween higher rainfall in the months between and including May and August,and lower rainfall in the months between and including November and February(figure 8). All analysis for the second stage was repeated for these twoseasons, resulting in two complementary summary images: wet season anddry season.

Summaries of changes in NDVI images across the available imagery usedvariance and Principle Component Analysis, discussed above (figures 10,11,12and 13) as the basis for deriving images. These images were reviewed foranomalies in the distribution of change related values. Geo-registered digitaldata providing details of the distribution of vegetation structure and dominantspecies, elevation, local infrastructure and aerial photography was overlaid toestablish areas for subsequent field-inspection.

An example of this approach, the theorising behind the selection and themanner in which results added to the appreciation of an area and a process thatmay not otherwise have been observed is area 2 (figure 9). The anomaly wasmost apparent on the two variance images as an area of higher variance for thewetter months and an area shifted slightly to the south for the drier months.

A review of the aerial photography for this area indicated an open area fringedto the south by vegetation that was more uniform in canopy and denser thanother nearby vegetation. The vegetation along the southern boundary of thisopen area corresponded in size and general shape to the anomaly present inthe winter variance image, while the less obvious anomaly present in thesummer variance image corresponded in general to the open area.

Ongerup (G-Link) - rainfall stats

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

mm

Mean Median St Devn

Figure 8: rainfall levels, Ongerup


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Field inspection revealed an open patch of exposed and deflated yellow sandyloam above a granite base situated on the level top of a broad ridge descendingto the southeast. The vegetation along the southern edge of the patch is densemelaleuca hamatashrubland, contrasting in density and dominant species withdry Allocasuarinawoodland to the east and Kunzea baxteriito the west.

Figure 9: anomaly area 2.


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Figure 10: dry season variance Figure 11: dry season PCA

Figure 12: wet season variance Figure 13: wet season PCA


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Over the 4 year summer and 6 year winter span of the temporal imagesequence, change has been more apparent in winter. This change coincidesspatially with the extent of the melaleucashrub-land extending downhill from thesouthern boundary of the open patch for 30 to 40 metres. During the driersummer months the change, while less variant, corresponds more to theboundaries of the patch itself. While any attempt to relate spatialcorrespondence to cause and effect is risky, fieldwork was able to establish thatthe open patch collected water that could be conserved on the granite underlay,drain downhill and provide resources for the melaleuca shrub-land along thesouthern boundary. The resulting periodic uptake of this water was reflected inthe variation of local NDVI derived values.

While this conclusion is subject to confirmation, it provides an example ofmaking use of NDVI based information to identify a process involving thetransfer of growth related resources that supplement visual information capableof identifying the features themselves.

Conclusions

This paper has outlined two examples of the use of contrasting techniques ofcompiling, representing and evaluating change in values relating to the health,growth patterns and resource usage of plant communities. Both refer to spatialinformation. Both present information relating to areas and features of thelandscape. The final part of this paper presents an assessment of thepracticality and effectiveness of the processes used by these two examples fora not-for-profit agency of limited means.

A decade ago this summary would have been markedly different. However, inthe intervening years the costs of imagery have dropped significantly, high-end

software is available under research and non-commercial licensearrangements, and the skills necessary to appreciate spatial qualities in dataand analysis, operate spatial software and work with digital spatial informationin a range of formats have percolated into a range of ecology related and otherprofessions.

The amount of work required to ensure that the imagery is able to supportpractical analysis is not high. Precise alignment of sequences of images isachievable using standard spatial software supporting visual comparison ofimage based features and coordinated reference points and capable ofreporting the level of precision attained.

Given that the purpose of the analysis is to allow the comparison of values

within a range of calculated NDVI indices, some flexibility exists in the level ofspectral precision capable of providing valid information. Under idealcircumstances, atmospheric effects and sensor bias should be removed as amatter of course. However, the information retrieved is filtered, ultimately,through the visual acuity and experience of the user.

The usefulness of this material in field-work depends on the purpose of thefieldwork. For this material to be used effectively, the purpose of the field-work


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must be clearly stated, whether targeted (as in reviewing and visiting knownlocations for a specific activity) or general (as in establishing the spatial extentand character of erosion, storm damage or other condition).

The use of GPS as a means of confirming location relative to vaguely definedlocality boundaries is essential, as is an effective means of transferring spatialinformation between analysis software and the GPS.

The skill base required to support this work comprises: (1) accessing relevantsatellite imagery, (2) compiling imagery into formats capable of analysis, (3)removing errors in imagery, (4) verifying and, if necessary, improving spatialalignment across image series, (5) deriving NDVI values, (6) developing changesummary images using appropriate statistics, and (7) mapping the resultsagainst local landscape features and infrastructure. Critically, the mostimportant skill is that used to relate the information produced to features,processes and phenomena in the field.

Further work is necessary to investigate cheaper and more cost-effectivemeans of correcting spectral values, or to make use of available processedNDVI image series as they become available in the future.

In general, and taking into account increasingly available resources and datasources and the expansion of spatial awareness and skills into a broadeningrange of professions, the change based imagery generated, analysed and usedin this project is a practical and cost-effective source of information for a not-for-profit agency of limited means.

References

Berry, S., Mackey, B. and Brown, T., 2007, Potential applications of remotelysensed vegetation greenness to habitat analysis and the conservation ofdispersive fauna. Pacific Conservation Biology, 13:120-127.

Coppa, I., 2006, The use of Remote Sensing Data for Broad-acre Grain CropMonitoring in Southeastern Australia, Ph. D. thesis, School of Mathematical andGeospatial Sciences, RMIT University, Melbourne Australia.

Keith, H., Mackey, B, Berry, S. and Lindenmayer, D., 2010, Estimating carboncarrying capacity in natural forest ecosystems across heterogeneouslandscapes: addressing sources of error. Global Change Biology,http://dx.doi.org/10.1111/j.1365-2486.2009.02146.x

Petit, C. C. and Lambin, E. F., 2001, Integration of multi-source remote sensingdata for land cover change detection. International Journal of GeographicalInformation Science, 15: 785-803.

Pettorelli, N., Vik, J. O., Mysterud, A, Gaillard, J. Tucker, C. J. and Stenseth, N.,2005, Using the satellite-derived NDVI to assess ecological resonses toenvironmental change. Trends in Ecology and Evolution, in press.

Roderick, M. L., Farquhar, G. D., Berry, S. L. and Noble, I. R., 2001, On thedirect effect of clouds and atmospheric particles on the productivity andstructure of vegetation. Oecologia, 129: 21-30


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Smith, L. I., 2002, A tutorial on Principle Component Analysis, University ofOtago, NZ, www.cs.otago.ac.nz/cosc453/

Thompson, M., Vlok, J., Rouget, M., Hoffman, M. T., Balmford, A., and Cowling,R. M., 2009, Mapping Grazing-Induced Degradation in a Semi-AridEnvironment: A Rapid and Cost Effective Approach for Assessment andMonitoring. Environmental Management, 43: 585-596.

Wallace. J. F., Caccetta, P. A., and Kiiveri, 2004, H. T., Recent developments inanalysis of spatial and temporal data for landscape qualities and monitoring.Austral Ecology, 29: 100-107.

Wallace, J., Behn, G. and Furby, S., 2006, Vegetation condition assessmentand monitoring from sequences of satellite imagery. Ecological Managementand Restoration, 7:31-36.

15arspc submission 11

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