"PZX-ZLES 90" Nou.rnéa. - Ta.hiti. 19 - 24 ~oV"ernbre 1990

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GIS AND RESOURCES ANALYSIS

SYSTEMES D'INFORMATION GEOGRAPHIQUE ET ETUDES DES RESSOURCES

A. PATERA (1), G. R. COCHRANE (2),

(1) CNR - National Research Council - ITALY(2) Department of Geography, University of AucklandPrivate Bag, Auckland, NEW ZEALAND

ABSTRACT

Resource management is becoming increasingly complex anddifficult. Planners and managers require improved methods forobtaining data. Remote sensing, plus Geographie InformationSystems (GIS) can provide these methods. This paper presentsexamples of some aspects of the process.

An assessment of GIS techniques for thematic mapping withSPOT XS data is given. Examples used are of two ruralenvironments. One is an estuarine area with flanking broadpastoral and forestry land use. The second is an area ofintensively managed primarily horticultural land use.

Techniques employed, problems encountered, resultsachieved and the relative merits of two processing systemsemployed, ERDAS and ARC/INFO, are given.

New Zealand SPOT data are used to demonstrate the uti1ityof such procedures for general resources roles and also forgeneral thematic mapping.

RESUME

Les problèmes de gestion de ressources sont devenus deplus en plus complexes et difficiles. Les planificateurs etaménageurs nécessitent des méthodes sans cesse améliorées pourl'obtention de données. La télédétection et les Systèmesd'Information Géographique sont désormais à l'origine de ces

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méthodes. Le présent papier offre des exemples dans cedomaine.

On présente une revue des techniques d'informationgéographique pour la cartographie thématique à partir dedonnées SPOT. Deux exemples sont présentés sur unenvironnement rural complexe. Le premier concerne une zoneestuairienne dont chaque rive est caractérisée par desactivités forestières et pastorales. Le second est relatif àune zone d'intenses activités horticoles.

Les techniques utilisées, les problèmes rencontrés, lesrésultats obtenus et les avantages respectifs de deux systèmes: ERDAS et ARC Info sont présentés. Des données SPOT deNouvelle-Zélande sont utilisées pour démontrer l'utilité detelles procédures dans l'étude globale des ressources.

INTRODUCTION

The job of natural resource management has becomeincreasingly more challenging. National, regional and localplanning aIl involve the wise management of resources. A rangeof methods, techniques and principles are implemented toprovide maximum knowledge of land from local, regional tonational scales, to facilitate current management and tooptimise future planning decision.

Usually a first stage towards good planning and managementis the acquisition of detailed, comprehensive and accurateland use maps. Satellite imagery such as LANDSAT TM and SPOTgoes a long way to providing use f uL spatial data as a firststep towards land use maps. However, regional planners usuallyrequire categories of thematic information not immediatelyachieved from satellite data. Integration of SPOT P + XSproducts partly meets this demand (Lasselin & Cochrane, 1990)but optimal use of SPOT data to provide these additionalthematic requirements of managers and planners needs newdimensions in analysis.

GIS can offer resource managers complex analysis of landand resource information that previously could not beundertaken to produce new solutions to problems. Thus themerging of spatial patterns and radiometric values (DN values,pixel brightness, colour tones) of digital satellite imageryin raster format with a range of land attribute values (landindices) and change indices in vector format can more readilyprovide the thematic maps desired by planners.

Cadastral patterns, valuation, topographie land cover,soils, vegetation and urban functional classes etc., can a Ll,be merged with the basic spatial data.

SPOTadditionalenormously

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derived landuse maps can be augmented withquantitative landscape indices to provideexpanded range of information of importance

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regional planners and resource managers. Multitemporal SPOTimagery and multidate comparisons of landscape indices providesets of change data vital in many facets of inventorymanagement and planning. The assessment of change requiresthat a comparative analysis be conducted using before andafter event imagery, and geographic information systemtechnology provides the ability to store and retrieve baselinedata as weIl as providing the comparative tools necessary.Sorne of the first stages of these digital image processes areaddressed in this paper.

METHODS

Processing was carried out using SPOT XS data of a NewZealand scene (443/426) on a SUN SPARC STATION 1+, courtesy ofEagle Enterprises Limited of Auckland.

Two subscene subsets (500 lines x 500 pixels) wereselected of two areas, Al - Matakana Island, and BI - Te Puke,of the Bay of Plenty area in North Island, New Zealand. Thesewere colour combined using SPOT XS bands 1,2,3, blue, green,red, respectively. Data were rectified, enhanced and a rangeof processing techniques evaluated using ERDAS Version 7.4 andARC/INFO version 5.0.1 software.

For the unsupervised classification, three main commandswere used :

Classification:

UnsupervisedCLUSTRSTATCLISODATA

to perform sequential clusteringto perform statistical clusteringto perform isodata clustering

Supervised classification was used for subscene AI ­Matakana to define land cover categories:

old/young pine treesforest and scrubIst/2nd quality grassbare/swampy ground, and sea

BI - Te Puke subscene categories of land cover analysedwere

citruskiwifruitlst/2nd quality grassbare ground (fallow)forest

For the supervised classification, using ARC/INFO thefollowing procedures were employed :

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DIGSCRN

SIGEXT

SEED

To digitize training samples (polygons) touse for the classification with SIGEXT.to create signatures from training polygonsdigitized with DIGSCRN.to identify training cells with a seedpixel and perform alarm evaluations usingparallelepiped decision rule with sigma = 3.

A range of other commands used to create intermediateclassifications and the final map products (colour figs. Pl9Ato PI9H), are analysed in the results section.

RESULTS AND DISCUSSION

GIS procedures have been used because versatility ofcommands in these kinds of programmes are user-friendly andallow an operator to manipulate data easily. Many operationsthat are difficult to perform with an image processing systemthat works with raster data are relatively easy to do with aGIS operating with vector data, like topologie operation.

Among the advantages is the capability to displaysimultaneously on a screen a raster image and vectorial datasuch as cartographie elements, (points, lines, polygons) of acadastral map on the graphies plane. This provides a rapidsynoptic view and the easy checking of data. Furthermore it ispossible to update an old cadastral map by highlighting theareas of change or interest with different values. Additionalmeri ts are (1) the availabili ty of an extensive number oftools and commands facili tating these operations and 2) thecreation of a printed map as a final result.

One of the most successful areas of integration of dataexchange formats between image processing systems and GIS isseen in the construction of a digital elevation model (DEM).It is possible to obtain a realistic perspective view of theground useful in geologic/geomorphologic evaluations. Suchproducts are also useful in agricultural landuse, soil,transport and planning applications. Forestry applicationswi th DEM include the identifying of suitable sites for newplantations, evaluation of the real extension of plantations,monitoring logging operations, and planning of transportationroutes.

Matakana and Te Puke areas have been chosen to recognisethe different stages of growth of the pine forests (Matakana)and for identification and extension of citrus and kiwi fruitorchards (Te Puke). Sorne cover classes are easilydifferentiated from inspection of the colour composite images.On the Matakana subscene (colour fig. P19G) the distinctionbetween young and old pine trees is recognised from theirlight and dark brown tones respectively. The forest/scrubcategory shows as a yellowish-tan tone. Distinction betweenvery shallow sea water, sand and mud flats, and bare ground,aIl recorded as cyan tones, is difficult. The dark green-black

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areas represent the swampy ground. An example of swampy groundshows at the left upper (northwest) corner of the image. Thearea is utilized as high value grazing ground in summer whenpastures become dry.

On the basis of distinctive tonal values spectralirradiance of cover classes in the Te Puke subscene (colourfig. P19A), it is possible to recognise the citrus orchards(dark red) the kiwifruit orchards (grey blue), the bare ground(light cyan) and the forest (dark brown-black). The pasturehas been defined in two categories : lst quality pasture and2nd quality pasture. Nevertheless three classes have beenrecognized. These are lst quality pasture (GG1) with highervalues in the IR in comparison with the second group of thelst quality pasture category (GG2), and 2nd quali ty pasture.The latter has higher values in the visible red and greenwavelength bands.

SUPERVISED CLASSIFICATION - TRAINING CELLS

To define the signatures for each class sometimes onetraining cell has been enough, as for the kiwifruit orchards.These are characterised by a high degree of spectraluniformity throughout the area (Edwards and Cochrane, 1990).Sometimes more training cells have been used because of asubstantial range among the pixel values in the three bandsfor different objects belonging to the same class.

When more than one training sample has been chosen, theresulting signatures have been merged to create an uniquesignature for the class. This process has been the mostdelicate because of merging sorne signatures that did notrepresent weIl the mean value of the class could lead to awrong interpretation and classification with the inclusion ofpixels belonging to different classes.

The two enlargements of the citrus orchard and adj acentarea have been obtained using ERDAS colour composite image(colour fig. P19E) and ARC/INFO fully classified map (colourfig. P19F). Both subscenes have been magnified 5 times.Nevertheless wi th ERDAS i t is possible to magnify an objectonly by a whole number of times (Ex: 1, 2, 3, ... ) because ofthe nature of the pixel. On the contrary wi th ARC/INFO themagnification is continuous (ex: 1.3, 2.7, 1.5) using thecommand MAPEXTENT, knowing the size of an object andcomputing simple calculations. With both the enlargements(colour figs. P19E and P19F) it easy to see the extent of thecitrus orchards. The dark brown-black stripes among theorchards are shelterbelts (colour figs. P19A and P19E) used toprotect the citrus orchards from the cool southerly winds.

The same kind of shelterbelts are used to protect thekiwifruit orchards (Edwards and Cochrane 1990) occupying mostof the right (eastern) half of the subscene (colour fig.P19E) .

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ERDAS PROCESSING

With the unsupervised classification or wi th SIGEXT weachieved medium resul ts. It has not be possible todifferentiate in B1 (Te Puke) GG1 and GG2 (lst qualitypasture). The best results have been achieved wi th the SEEDclassification. We have had the possibility to differentiateboth subscenes in weIl distinguished classes.

Al - Problems to merge shallow water and deep water : inclusionin the signature of swampy ground. Therefore two differentsignatures.

B1 - Problems to merge GG1 and GG2 : inclusion in the resultingsignature of part of 2nd quality pasture. Therefore twodifferent signatures.

Using SEED the training polygons and a DIG file containingthe polygons information is created automatically.

SIGMAN has allowed processing to perform a quick alarmusing the parallelepiped decision rule with sigma = 3 and tomerge and delete uncorrect signatures. We have had sorneproblems with the subcommands MERGE, DELETE and RENAME.

Enhancement

Automatic stretching has been used with sigma = 2 (95%pixels classified). With manual stretching (WFM has been usedalso to overlay the graph for the images and to perform linearand nonlinear spectral enhancement) we have not obtained avisible enhancement in comparison with automatic stretching.

BSTATS has been used to generate a statistic file usedautomatically by the software for the automatic stretching.

Classsification:

Unsupervised : CLUSTRSTATCLISODATA

to perform sequential clusteringto perform statistical clusteringto perform isodata clustering.

Supervised

Check

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DIGSCRN to digitize training samples(polygons) to use for theclassification with SIGEXT.

SIGEXT to create signatures frompolygons digitized with DIGSCRN.

SEED to identify training cells with aseed pixel and perform alarmevaluations using parallelepipeddecision rule with sigma = 3.

ELLIPSE used to view sample scatterplots ofsignatures data. Two different kinds

of bare ground have been identified asbelonging to the same class withELLIPSE commando

CMATRIX used to perform the contingencymatrix evaluation using Mahalanobismethod.

SIGDIST used to calculate distance betweensignatures.

DIVERGE used to compute signature divergenceusing Jeffrie-Matusita distance.

AlI four commands have been used to check the signaturesand to decide to merge or deIete them before classifying theimage using the command MAXCLAS.

MAXCLAS has been used to classify the resulting signaturesusing the training polygons in the DIG file and aIl the threebands, using the maximum likelihood decision rule. MAXCLAScreates automatically a GIS file. The resul ting GIS file hasbeen converted with CVT73 from the new format of ERDAS version7.4 to the previous format of ERDAS version 7.3 because theversion 5.0.1 of ARCjINFO does not support the ERDAS 7.4format.

ARCjINFO PROCESSING

Using ARCjINFO complex modifications of the topology anddatabase are easy to perform. It is easy to alter thecodification of attributes, to modify the features of acoverage, to merge polygons, to mosaic coverages, to overlaydifferent layers containing different kinds of information.These forms of readily acquired data are the improvedtechniques desired by planners and managers. One of the bestperformance of a GIS is however the final product : the map.It is possible to fill polygons with different shades, usuallyuser-defined, to use one's own formats, symbols, colours,lines, etc. Thus the increasingly complex and difficult roleof resources management is greatly facili tated by the speed,versatility and utility of these processing procedures.

GIS can provide a range of thematic maps addressing aspecifie problem, diverse maps for comprehensive coverage, forupdating patterns, for mapping of change, maps of economic orenvironmental facets, or fully classified complex maps.

The ARCj INFO command ERDASSVF has been used to convertERDAS GIS file in a SVF file. To convert data from a rasterformat to a vector format GRIDPOLY command has been used. TheMatakana and Te Puke ARCjINFO coverages have been submitted toa process of elimination of the clumps with an area smaller orequal to 800m2 = 2 pixels with the command ELIMINATE. Afterthe classification and smoothing functions have eliminated theclumps, the area previously occupied by them is assigned tothe widest adjacent polygon (see fig. 1).

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A higher value in the elimination process would havecarried an abnormal increase in the area of the forest andtherefore an area of 800m2 has been believed suitable.

AlBI

Initial1111119759

Eliminated7656

14014

Final34555745

For both the subscenes the command DISSOLVE has allowedremoval of boundaries of adjacent polygons with the same classcode but wi th different value of signatures as forshallow/deep sea class in the Matakana subscene and similarlyfor GGl/GG2 in the Te Puke area.

Minor modifications have been performed using the ARC/INFOmodule ARCEDIT.

The resulting landuse map has been created using themodule ARCPLOT and the map composition commands.

Future steps:

Digital elevation model (DEM) of the images and overlayingof cadastral and landuse maps.

Hardware and Software characteristics

Computer: SUN SparcStation 1+12 Mb memory1400 Mb disk (mostly ARC/INFO data)1/4 inch 150 Mb tape driveRunning SunOS (UNIX) version 4.1.

Monitor

Network

19 inches colour monitor, 256 colours display.

The SparcStation was connected to anotherSparcStation 1+, 2 PCs, a SUN 3/160 and a PRIME750/

ERDAS version 7.4 (SUN 4)ARC/INFO version 5.0.1 (SUN4)

ACKNOWLEDGEMENTS

Thanks are due to Brian Hurst, University of Auckland ­Department of Geography photographer. Financial researchsupport to Dr. Antonio Patera in the form of an ItalianNational Council of Research fellowship and a research grantby the University of Auckland Research Committee to Prof. G.Ross Cochrane are both gratefully acknowledged.

The authors also gratefully appreciate and acknowledge theresearch support from Eagle Enterprises Limi ted of Auckland,New Zealand distributors of ERDAS and ARC/INFO, for providingaccess to image processing facili ties. Epecially thanks are

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due to Kim Ollivier for his early assistance and continuedguidance in the operation of ERDAS and ARCjINFO and to KenFortune for generous technical help.

REFERENCES

EDWARDS M., G.R. COCHRANE, 1990. The role of SPOT formapping intensive small cropjhorticulture. "PIX'ILES 90"Conference paper.

LASSELIN D., G.R. COCHRANE, 1990. Ville d'Auckland."PIX'ILES 90" Conference paper.

FOREST [] FOREST

Before: ELIMINATE After: ELIMINATE

Figure l Clumps elimination process with ELIMINATE function.

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P19

P19B : Te Puke suscene showingtheming process and po1ygonsby ARC/INFO Red = lst qua1itypasture - Ye110w = 2nd qualitypasture Polygon pattern readilyvisible.

P19A : ERDAS colourcomposite image of B1 TePuke subscene of SPOTimage (443/426). Scale1:110,000. C CNES/distSPOT IMAGE.

P19C : Te Puke subscenefully c1assified sho­wing polygon overlay.

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p19

PI9E : Part of Te Puke subsceneenlarged X 5 - ERDAS process.

PI9D : Te Puke subscenefully classified byARC/INFO.

PI9 F : Part of Te Pukesubscene enlarged X 5 ­ARC/INFO process fullyclassified. See colourfig. PI9D for key andcolour codes

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P19

P19G : ERDAS colour composite of Matakana subscene of SPOTimage (443/426). Scale 1:50,000

P19 H Matakana subscene fully classified by ARC/INFO process

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"PIX'ILE 90"

Journées internationales tenues à Nouméa· Nouvelle-calédonieetà Tahiti· Polynésie Française

19/24 novembre 1990

International workshop held at Noumea • New caledoniaand Tahiti French· Polynesia

November19/241990

ilt:EAfER

© ORSTOM, Nouméa. 1992

Irnprimo par le Centre ORSl DMde Nouméa

Septembre 1992

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