Use of digital aerial photography for sub-alpine vegetation mapping: Acase study from the Krkonoše Mts., Czech Republic

Jana MüllerováInstitute of Botany, Academy of Sciences of the Czech Republic, 25243 Pr°uhonice, Czech Republic; (tel:+420271015278; fax: +420267750031; e-mail address: mullerova@ibot.cas.cz)

Received 23 July 2002; accepted in revised form 12 March 2004

Key words: Digital image processing, Field survey, Methodology assessment, Multispectral photography, Re-mote sensing, Vegetation analysis

Abstract

Multispectral aerial photographs from 1986, 1989 and 1997 were used for mapping and assessment of changes insub-alpine vegetation of the Krkonoše Mts Plateau �1300 to 1400 m a. s. l.�, Czech Republic. Scanned aerialphotographs were digitally processed by various remote sensing methods, such as histogram equalization, prin-cipal component transformation, Normalised Difference Vegetation Index, and classification �both unsupervised,i.e., Iterative Self-Organizing Data Analysis Technique, and supervised, i.e., Parallelepiped and Maximum Like-lihood algorithm�. The results were geometrically corrected by registration into orthophoto images. Field datafrom 1991 assisted in class labelling and vegetation description. A detailed vegetation analysis was conductedusing 1989 multispectral photographs since the field-verification and comparison to ground-based vegetation mapcould be done with only two-year difference. The unsupervised classification resulted in six classes �three ofthem mixed� with a 60.6% overall accuracy of unmixed classes. Some important vegetation types could not beseparated. The supervised classification resulted in nine classes with the overall accuracy of 81.1%. The mostcommon vegetation types were: mat-grass communities �40.4% of the total area� and tall grass and herb com-munities �23.1%�. Pine stands, the best separated of vegetation types, were evaluated on the multispectral pho-tographs from 1986 and 1997 and the P. mugo stands doubled in area over 10 years. The aerial-photograph basedmaps were compared with an available vegetation map obtained by a field survey. The correspondence betweenboth ranged from 24 � 75%, with the highest values obtained for pine stands and mat-grass communities. Incomparison to the field survey, aerial photography provided less detail and vegetation was characterized mostlyby dominant species. The approach failed to map small patches of heterogeneous or atypical vegetation and todifferentiate vegetation types with dominants of similar appearance. Nevertheless since this method is compara-bly less time- and labour-consuming, it seems suitable for mapping distinct vegetation types, surveying largerareas and identifying changes over time.

Introduction

Traditional field methods of vegetation mapping �ac-cording to the Braun-Blanquet floristic approach,Braun-Blanquet 1965� provide with a possibility todescribe vegetation in a large detail but are time/la-bour intensive and are biased by the subjectivity ofthe interpreter �Congalton 1991�. Remote sensing

�RS� can partly eliminate these problems but the na-ture of resulting information differs �Mosbech andHansen 1994�. Instead of focusing on communitystructure and floristic composition, RS is based ondominant species, their biomass �i.e., chlorophyllcontent and leaf area�, water content, physiognomy�e.g., height of plants, width of leaves and their posi-tion�, canopy structure and its character �e.g., canopy

Plant Ecology 175: 259–272, 2004.© 2004 Kluwer Academic Publishers. Printed in the Netherlands.

259

closure – a ratio of bare ground visible through thecanopy�, and soil conditions �Graetz 1989; Franklinet al. 1994�.

The relatively high spatial resolution of multispec-tral aerial photography makes this tool useful formapping vegetation composed of a mosaic of smallunits. The digital processing employed in the presentstudy has several advantages and disadvantages com-pared to the manual one. Manual vegetation mappingis not only time/labour intensive, hence expensive,but also subjective. Although a higher number ofclasses �some very small and unique� can beachieved, the classification is not reproductive.Labour intensity of manual interpretation usuallylimits the spatial extent of databases and the manualclassification accuracy is not readily assessed �Carmeland Kadmon 1998�. Digital RS classification is morehomogeneous and consistent. It often distinguishesfewer classes, but their number increases as the op-erator gets more experienced, the software/classifica-tion more sophisticated, and the amount of input data�multitemporal� or quality of input data �from multi-spectral to hyperspectral� increases. Computer pro-cessing must deal with changes in illumination acrossthe scene and other data irregularities. Yet it enablespartial correction of geometrical distortions, imageenhancement, filtering and other pre-processing tech-niques, statistical evaluation, visualisation of resultetc. �Pope et al. 1996�.

The present study examined the application ofmultispectral aerial data to the mapping of sub-alpinevegetation that is vulnerable to human-induceddisturbances and extremely valuable from the conser-vation point of view. The usefulness of digital inter-pretation of analogous aerial photography for vegeta-tion cover-type mapping, that would adequatelyaddress the needs for conservation purposes in thearea, is evaluated. The study was aimed at finding amethodology that could be applied to future aerialdata of the area so as to assess and monitor temporalchanges in the area �mainly due to human disturb-ances�.

The study was designed to answer the followingquestions: �1� Can aerial multispectral data be a use-ful tool in classifying sub-alpine vegetation? �2� Towhat extent can be different vegetation types sepa-rated on this kind of imagery? �3� Are the results ofaerial data analysis comparable with those obtainedin the field? �4� To what extent it is possible to detecttemporal changes in vegetation using time series ofaerial data?

Study Area

The Krkonoše Mts �Giant Mts, Riesengebirge� lie inthe northeastern part of the Czech Republic. Thestudy site �Figure 1� is located on the western of thetwo sub-alpine plateaus �1300 to 1400 m a. s. l, 15°33� E; 50° 45� N� belonging to the uppermost areasituated above timberline. It represents a particularlandscape system, often called arcto-alpine tundra�Soukupová et al. 1995�, with affinities to both sub-arctic and high-mountain regions and subjected toextreme climate conditions, such as exposition tostrong northwestern winds. Average temperature is 0to �1 °C, snow cover lasts more than 180 days, an-nual precipitation is 1400 mm, and there is periodicalsoil regelation and deflation. The plateaus are formedby crystalline metamorphic rocks, covered by pooracidic soils of the alpine sod podzol type, patches ofpeat bogs, raised bogs, polygonal and patterned soils.The treeless summits of the Krkonoše Mts harboursome unique physiographic components, such as relicand endemic populations and community types�Soukupová et al. 1995�.

The study area is heterogeneous in terms of veg-etation and abiotic environmental factors such asgeomorphology, texture, soils, microclimate and dis-turbance regime. Vegetation cover is composed ofgrassland, scrubland, fens and peat bogs. The mosttypical plant species include Pinus mugo �forming al-liance Pinion mughi and Oxycocco-Empetrion her-maphroditi�, creating large dense krummholz; Nardusstricta, a typical grass dominant of dense uniformstands of alliance Nardo-Caricion rigidae at the lo-calities of high snow cover and deeper humus podzolsoils based on granites; Molinia caerulea andCalamagrostis villosa forming acidophilous commu-nities of tall grasses and flowering herbs �allianceCalamagrostion villosae� on wet, wind-protectedsoils with long-lasting snow cover; Deschampsiacespitosa �a dominant of alliance Poo chaixii-Deschampsion caespitosae�, found on mezotrophic toeutrophic deeper, permanently humid soils; peatmosses; and Baeothryon cespitosum subsp. cespito-sum forming oligotrophic communities of boreoarcticand alpine raised bogs with prevalence of shrub formand peat mosses �alliance Oxycocco-Empetrion her-maphroditi�. For details see Table 4, where the fieldbotanical survey according to the Braun-Blanquetfloristic approach �Fišerová 1991� is summarized andthe percentage of the area covered by each vegetationtype given.

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The study site situated on the Western Plateau cov-ered an area of ca 2 � 3 km. It was selected accord-ing the available field vegetation maps and aerial MSphotography. The plateaus themselves are relativelysmall �Western Plateau: 18.7 km2; Eastern Plateau: 29km2� and the study site therefore covered 30% of theWestern Plateau and 8% of the whole sub-alpine re-gion.

Material and methods

The following material was used in the analysis:– multispectral aerial photography of 1986 �acquired

20.9 1986; scale � 1:22000; channels � 0.48,0.54, 0.66, 0.84 �m�, 1989 �acquired 18.9 1989;scale � 1:22000; channels � 0.54, 0.60, 0.66,0.84 �m� and 1997 �acquired 12.9 1997; scale1:15000; channels � 0.54, 0.60, 0.66, 0.84 �m�;created and provided by the Agency for NatureConservation and Landscape Protection in Prague;

technical parameters – camera MSK-4, focal length125 mm, film material – FOMA �visible spectrum,1986 and 1989�, Aviphot Pan 200PE1 �visiblespectrum, 1997�, I-840 �NIR channel, 1986 and1989�, Kodak Aerographic Infrared Film 2424�NIR channel, 1997�

– panchromatic orthophotographs �1997; createdfrom scanned aerial panchromatic photographs ofscale 1:22500 with overlap of 60%, using a digitalterrain model created by vectorization of topo-graphic maps 1:10000, orientation points of imagesidentified by analytical aerotriangulation in systemORIMA; orthorectification performed on digitalphotogrammetric station Leica-Helava DPW 770,module Mosaic, final pixel resolution 0.5 m�, cre-ated by Geodézie Krkonoše �private company�,provided by the Krkonoše National Park Authority

– field botanical survey �Fišerová 1991; scale1:5000� – vegetation units drawn into 1989 aerialphotographs.

Figure 1. Location of the study site.

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Aerial photography was chosen instead of the morecommonly used satellite data although it requires amore complicated rectification then the satellite dataand here is a complicated illumination effect acrossthe area combined with the variation in the movementof the aircraft. This approach was adopted since suchdata at a high spatial resolution �0.5 to 1 m� wereavailable �provided by the Krkonoše National Parkconservation authorities�. Analogous aerial photo-graphs �i.e., negatives� were transformed to digitalform by scanning the negative of each channel sepa-rately in resolution 1000 dpi to allow the computerprocessing. The digital type of processing wasselected since digital analyses can easily be repeatedon multitemporal data to evaluate changes over time.Scanned image channels were co-registered byimage-to-image registration and then geo-referencedusing the orthophotographs. Forty to fifty groundcontrol points distributed along whole rectified pho-tograph were collected. The second order of transfor-mation was applied. To minimize the errors caused bymis-registration the final resolution was coarsened topixel size 1 � 1 m. The effect of atmospheric trans-mittance and the angular effect, which would requireadditional data correction, were ignored, as well asthe terrain correction, since the area was generallyflat.

The detailed vegetation analysis was carried out onthe multispectral photographs of 1989. From the fourchannels available �0.54, 0.60, 0.66, 0.84 �m�, Redand Near-Infrared channels proved to be most useful.The digital form provided an opportunity to enhancethe visuality of vegetation types by histogram equal-ization �proved to by particularly useful�, principalcomponent transformation �PCA� and NormalizedDifference Vegetation Index �NDVI�, and to applyunsupervised and supervised classification methods.In the unsupervised approach the Iterative Self-Orga-nizing Data Analysis Technique �ISODATA� was em-ployed with following parameters � 20 clusters, 20iterations. The number of clusters �20� was selectedafter the clustering process from 2 to 30 clusters wasexamined. Higher number of clusters made their in-terpretation comparatively difficult. Resulting clusterswere grouped together according the field verifica-tion. In the supervised approach, Parallelepiped clas-sification �non-parametric method defining classes onthe basis of the lowest and the highest value in everyspectral band� was applied at first to pre-process thedata, to ease the training area selection and to definethe desired land-cover categories possible to distin-

guish on the image. The a priori probability of theclass occurrence determined by the Parallelepiped al-gorithm was then used in the Maximum Likelihoodanalysis, resulting in the final classification map. AMinimum-distance classifier was attempted but re-jected due to the poor results. Descriptions of themethods can be found in Jensen �1996�, Lillesand andKiefer �1999� or Mather �1999�. The digital analyseswere carried out using the Chips software �Chips De-velopment Team 1998�.

The resulting classes of both supervised and unsu-pervised approaches were examined using Chipssoftware to produce scattergrams to determine theconsistency of reflectance values and spectral differ-ence of the class signatures. The Jeffries-Matusitadistance measure of separability �Jensen 1996� wasused to assess the separability of pairs of classes �andtraining sites for supervised classification� on the ba-sis of their signatures.

An accuracy assessment of the 1989 results wascarried out using descriptive and analytical statistictechniques as described in Congalton �1991�. Evalu-ation areas used for the final assessment were posi-tioned randomly over the area and were independentof the training sites used for the supervised classifi-cation. Stratified random sampling was used to ensureall strata are included in the sample �Congalton andGreen 1998�. An overall accuracy as well as ‘user’saccuracy’ �the measure of commission error� and‘producer’s accuracy’ �the measure of omission error�of individual classes were calculated from the confu-sion matrix. The discrete multivariate technique usedin the accuracy assessment was the KAPPA coeffi-cient �Bishop et al. 1975�. After the validation the fi-nal results were smoothed using a 3 � 3 mode filter.Synoptical maps of the study area were thenproduced.

The RS results of 1989 were compared with thefield survey data �FS� of Fišerová �1991�. The FS in-corporated field observation and visual interpretationof the same aerial photographs, classifying the veg-etation at the level of communities or their transitionsaccording to the Braun-Blanquet floristic approach�Braun-Blanquet 1965�. The FS map was digitisedusing the CartaLinx software �Clark Labs 1998�. In-formation contained in the map was utilized for thetraining area selection and classification interpretationof the RS analysis. The RS and FS maps were com-pared in terms of the number of distinguished vegeta-tion types, their distribution, extent, and the mappingaccuracy. The level of correlation between ground

262

and aerial-based vegetation maps was evaluated. RSand FS approaches were compared in regard of thearea covered by each vegetation type as well as onthe cell-by-cell bases in a GIS environment usingArcView GIS �Environmental Research Inst. 1999�.The percentages of corresponding cells and the mostcommonly confused classes were identified in thecorrespondence matrices and it was possible todeduce likely reasons for the differences. To test thepossible distortion of RS/FS comparison caused byregistration errors, the pixel dimension was coarsenedfrom 1 � 1 to 2 � 2, 3 � 3 and 4 � 4 m and thenew RS/FS correspondence matrices were generated.

Interpretation of multispectral data sets of 1986 and1997 focused on the best separable vegetation type –P. mugo, describing its dynamics during 10 years. Theunsupervised classification was applied since in theprevious detail analysis this simple and fast methodproved to be sufficient for such purpose.

Nomenclature of taxa follows Rothmaler et al.�1991�, that of syntaxa Moravec �1995�.

Results

Unsupervised classification

The unsupervised �ISODATA� classification �Figure2� was only moderately successful in determining themain vegetation and land-cover types, with some cat-egories mixed. Fifteen classes were identified. Sincemany classes included the same vegetation/land-cover types, they were merged into six resultingclasses �Table 1�: �1.� pine stands �Pinus mugo domi-nated stands�; �2.� mat-grass communities �Nardusstricta dominated stands�; �3.� tall grass and herbcommunities �dominated by Calamagrostis villosa orMolinia caerulea�; �4.� road-altered vegetation mixedwith tall grass and herb communities; �5.� roads ormat-grass; �6.� roads or short grasses and herbs �De-schampsia cespitosa�. The last three classes weremixed, covering 25% of the area �Figure 3�. Someimportant vegetation types were not separated, suchas mat-grass and roads in the mixed class 5.

The resulting classification was verified in the field.As classification accuracies �Table 2� could not becalculated for the mixed classes, the overall accuracyof 63% and KHAT statistics of 45% is only based onclasses 1 � 3 �covering 75% of the area�. The user’saccuracy ranged from 58 to 74% and the producer’saccuracy from 55 to 72%. The best results, 74% �us-

er’s accuracy� and 72% �producer’s accuracy�, wereobtained for pine stands. The Jeffries-Matusita dis-tance measure of separability �growing from 0 to1.41� showed the highest separability �above 1.2� forclasses 1, 4 and 6, and the value of 0.9 for classes 2/5and 2/3.

Supervised classification

Minimum distance and Maximum Likelihood algo-rithms were tested. The former did not show satisfac-tory results and was therefore rejected �such as inFuller and Parsell 1990�.

Resulting classes of the Maximum Likelihoodclassification �Table 1�:1. Pine stands – Pinus mugo, forming krummholz,

i.e., procumbent shrubs, represents dominatingwood above the timberline. Constrained polycor-mons of P. mugo �Figure 2�, inhabiting shelteredforks and patches of organic and mineral soils ex-pand to surrounding grasslands. Present communi-ties belong to the alliance Pinion mughi, associa-tion Myrtillo-Pinetum mughi and allianceOxycocco-Empetrion hermaphroditi with an en-demic association Chamaemoro-Pinetum mughi. P.mugo is partly natural to the area and partlyplanted. Plantation resulted in closed-canopystands with a regular pattern sharply contrastingwith jagged boundaries of the natural ecosystems�Soukupová et al. 1995�.

2. Mat-grass communities are dominated by Nardusstricta and belong to the alliance Nardo-Caricionrigidae �as. Carici fyllae-Nardetum�. N. stricta re-presents a prevailing dominant of local grasslands�Figure 2� and has a low stress tolerance and highvulnerability to trampling and eutrophication. It isendangered by expanding P. mugo krummholzsince these krummholz favour concurrent gramin-eous species. The class was further subdivided intothree categories �A, B and C� expressing the tran-sition to tall grass and herb communities or thatbetween mineral and peat soils �from A to C�, fromsites dominated by N. stricta �pH 4.5� to those withprevailing Molinia caerulea and N. stricta as acon-dominant �subas. Carici fyllae-Nardetum mo-linietosum caeruleae; pH 3.7–3.9�.

3. Tall grass and herb communities are acidophilousand occur on granite-based wet, wind-protectedsoils with a long lasting snow cover; they aredominated by Calamagrostis villosa, Veratrum al-bum L. subsp. lobelianum �Bernh.� Rchb. and

263

Figure 2. Comparison of the results of three vegetation mapping methods: �a� the field survey �Fišerová 1991�; �b� multispectral aerial photointerpretation – supervised classification approach �Maximum Likelihood algorithm�; �c� multispectral aerial photo interpretation – unsuper-vised classification approach �Iterative Self-Organizing Data Analysis Technique – ISODATA�.

264

Tabl

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the

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alph

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raph

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ficat

ion,

both

unsu

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ised

�ISO

DA

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and

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d �cl

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ion

appr

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es.

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uper

vise

dcl

assi

ficat

ion

�ISO

DA

TAal

gori

thm

�Su

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clas

sific

atio

n�M

axim

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ikel

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dal

gori

thm

�

No

Cla

ssN

ame

Cla

ssch

arac

teri

stic

s%

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eaN

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lass

Nam

eC

lass

char

acte

rist

ics

%of

area

1pi

nest

ands

Com

pact

stan

dsof

Pin

usm

ugo

proc

umbe

ntsh

rubs

17.1

%1

pine

stan

dsse

eIS

OD

ATA

clas

s1.

18.6

%

2m

at-g

rass

com

mun

ities

Nar

dus

stri

cta

dom

inat

edst

ands

,pr

evai

ling

gras

slan

dty

pe37

.6%

2m

at-g

rass

com

mun

ities

see

ISO

DA

TAcl

ass

2.40

.4%

2.1

mat

-gra

ssco

mm

uniti

esA

site

sdo

min

ated

byN

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rict

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H4.

5 �on

min

eral

soils

14.2

%

3ta

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ass

&he

rbco

mm

uniti

esac

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usco

mm

uniti

esof

tall

gras

ses

and

flow

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ghe

rbs

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min

ated

byC

alam

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20.3

%2.

2m

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com

mun

ities

Btr

ansi

tion

betw

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7.9%

2.3

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mm

uniti

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site

sw

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eval

ence

ofM

olin

iaca

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ant

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3.9 �

onpe

atso

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tran

sitio

nto

tall

gras

s&

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com

mun

ities

18.3

%

4ro

ad-a

ltere

dve

geta

-tio

nan

dta

llgr

ass

&he

rbco

mm

uniti

es

mix

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ass

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hing

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uild

ing

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mm

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tall

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com

mun

ities

see

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DA

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ass

3.23

.1%

4ro

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ltere

dve

geta

tion

see

ISO

DA

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ass

4.11

.5%

5ro

ador

mat

-gra

ssco

mm

uniti

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ixed

clas

s3.

5%5

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sha

rden

edby

dolo

mite

and

mel

aphy

regr

avel

,so

me

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red

byas

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t4.

8%

6ro

ador

shor

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asse

s&

herb

sm

ixed

clas

sof

road

san

dco

mm

uniti

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min

ated

byD

esch

amps

iace

spit

osa

6.1%

6w

ater

pool

san

dw

ater

logg

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pres

sion

s0.

3%

7w

etar

eas

peat

bogs

,st

ream

allu

vium

,su

rrou

ndin

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Pin

usm

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shru

bs0.

7%

8un

clas

sifie

d0.

6%

Ove

rall

accu

racy

ofun

mix

edcl

asse

s�

63.1

%;

Kh

atof

unm

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�44

.5%

;O

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78.8

%;

Kh

at�

74.6

%

265

Senecio hercynicus Herborg �alliance Calamagros-tion villosae, as. Crepido-Calamagrostietum villo-sae�, or Molinia caerulea stands of subas. Caricifyllae-Nardetum molinietosum caeruleae.

4. Road-altered vegetation occurs along roads pavedand hardened by allochtonous alkaline buildingmaterial. Alkaline ions are continuously washedinto the originally nutrient poor environment, sup-porting tall herbs and grasses growing on soilsricher in nutrients �this vegetation can be partlyclassified as Bistorto-Deschampsietum alpicolae orCrepido-Calamagrostietum villosae associations�with a high amount of ruderal species �Urtica dio-ica, and indigenous Rumex alpinus�, species oflower altitudes �Senecio ovatus �Gaertn., B. Mey.et Scherb.� Willd., Hypericum maculatum and Cir-sium arvense� and expansive local species �e.g.,Senecio hercynicus, Cirsium helenioides, Veratrumalbum subsp. lobelianum, Deschampsia cespitosa,Calamagrostis villosa�.

5. Roads were hardened by dolomite and melaphyregravel, some covered by asphalt.

6. Water bodies are small pools and waterlogged de-pressions; their size and overgrowth by Sphagnumsp. differ according to fluctuating precipitation.

7. Wet areas are peat bogs, stream alluvium and sur-roundings of Pinus mugo shrubs.

The separability of supervised classification trainingclasses measured by the Jeffries-Matusita distance�growing from 0 to 1.41� showed values above onefor non-vegetation training classes and for the pineclass. Tall grass and herb communities trainingclasses showed possible confusion with mat-grass�0.98� and road-altered vegetation �0.74�, similarseparability was achieved for the resulting classes.

An NDVI analysis was carried out on the 1989photo only. It was used mainly to enhance the visu-ality of classes prior the classification and to enablethe selection of classes possible to discriminate on thephotograph. NDVI values were higher because of ra-diance used instead of reflectance values since an at-mospheric correction of the image was not performed.Vegetation classes of highest mean NDVI were pinestands, tall grass and herb communities and road-al-tered vegetation, while non-vegetation structures re-ceived NDVI values under zero.

The most common classes were mat-grass commu-nities �the most frequent sub-class C�, tall grass andherb communities and pine stands �Figure 3�. The us-er’s accuracy ranged from 62 to 87% and the produc-er’s accuracy from 70 to 88% with the overallaccuracy of 79% and KHAT statistic of 75%. The bestaccuracy was achieved for pine stands and mat-grasscommunities �Table 3�.

Figure 3. Diagrams showing the distribution of classes in both RS data �ISODATA and Maximum Likelihood� and the field survey �FS�.Numbers and letters representing class names are explained in Figure 2 �RS�, and in Table 4 �FS�.

Table 2. Unsupervised classification accuracy, confussion matrix. Numbers correspond to the number of pixels.

ISO classification Pine stands Mat-grasscommunities

Tall grass & herbcommunities

User´s accuracy

Pine stands 71 12 13 74%Mat-grass communities 20 72 32 58.1%Tall grass & herb communities 8 30 54 58.7%Producer´s accuracy 71.7% 63.2% 54.5% 63.1%

266

Comparison of different approaches

The field survey and the RS approach are comparedin Table 4. Eleven vegetation types were recognizedin the field map �Fišerová 1991, Figure 2�. FS veg-etation classes were related to the RS classes �seeTable 4�. Three FS vegetation types dominated by P.mugo contributed to the pine category, two FS classesdominated by Callamagrostis villosa and Moliniacaerulea contributed to the category of tall grassesand herbs. A large group of FS classes could not bedistinguished by RS means or related directly to theRS classes, i.e., Scirpo caespitosi-Sphagnetum com-pacti �classified predominantly as mat-grass or tallgrass and herb communities�, Salicetum lapponum�mostly classified as tall grass and herb communitiesor road-altered vegetation�, stands dominated byCarex rostrata �accordingly classified as tall grassand herb communities�, transitions between M. caer-ulea and Carici fyllae-Nardetum �classified accordingto the character of the transition as mat-grasscommunities or tall grass and herb communities, i.e.,M. caerulea dominated�, transitions between Caricifyllae-Nardetum and Myrtillo-Pinetum mughi �classi-fied as pine stands, tall grasses and herbs or mat-grasscommunities. These classes �covering 17.5% of thearea� were joined into a residual group.

A similar distribution of categories was obtained byusing the three RS approaches �Figure 3�, while theFS showed lower values for the tall grasses and theroad-altered vegetation in favour of the mat-grass�and the residual group�. The classes contributing tothe residual group were excluded from the correspon-dence assessment �Table 5, Table 6�. The resultingcorrespondence values �RS/FS� varied from 24% to75% with the overall correspondence of 59% for the

unsupervised approach and 55% for the supervisedone. The highest FS/RS correspondence values wereachieved for the pine stands �over 70%�.

Enlargement of the cell dimension from 1 � 1 to 2� 2, 3 � 3 and 4 � 4 m had negligible impact onoverall correspondence values and never exceeded1%.

Change detection

Due to the difficulties arising from the nature of themultispectral data �varying quality and exposure, in-accuracy of the geometric correction etc.� only thebest separable vegetation type �pine stands� was se-lected to assess the changes over the time covered byavailable data �1986-1997�. Unsupervised ISODATAclassification approach was employed. Over 11 years,the area covered by P. mugo gradually increased. Itcovered 4.5% of the area in 1986, 6.1% in 1989 and10% in 1997; this represents 40% increase in 1986-1989 and 50% increase in 1989-1997, resulting inmore then doubled area over the whole period �Fig-ure 4�. The average increase calculated for the 11years was 2.8 hectares per year.

Discussion

Aerial data analysis

The present study has shown that aerial data can pro-vide valuable information about vegetation in sub-al-pine region, with comparatively high classificationaccuracy and low class confusion. Still some vegeta-tion types were not possible to identify due to theirspectral or species similarities or small size of

Table 3. Supervised classification accuracy, confussion matrix. Numbers correspond to the number of pixels.

Maximum Likelihoodclassification

Pine stands Mat-grasscommuni-ties

Tall grass &herb com-munities

Road-alteredvegetation

Roads Water Wet areas User´saccuracy

Pine stands 84 2 3 2 1 3 2 86.6%Mat-grass communities 6 95 8 3 0 0 0 84.8%Tall grass & herb com-munities

3 8 79 12 1 0 3 74.5%

Road-altered vegetation 2 6 8 75 4 0 5 75%Roads 0 2 2 5 45 3 0 78.9%Water 0 0 0 0 6 24 0 80%Wet areas 0 4 8 0 0 2 23 62.2%Producer´s accuracy 88.4% 81.2% 73.1% 77.3% 78.9% 75% 69.7% 78.8%

267

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268

patches. Yet the not discerned vegetation types werenot of main importance. Stands of uniform vegetationshowed higher classification accuracy compared tothose with heterogeneous, atypical or transitionalvegetation, which were difficult or impossible to map.As Treitz et al. �1992� pointed out, the more spectrallyunique the class is and the lower species variation itshows, the higher is its classification accuracy.

The supervised classification technique �MaximumLikelihood method�, providing higher overall accu-racy, proved to be more successful and accurate thanthe unsupervised �ISODATA algorithm�. As the unsu-

pervised approach produced mixed classes and didnot differentiate some land-cover types �e.g., road-al-tered vegetation�, it appears to be insufficient for adetailed vegetation study. However as a fast and easymethod it worked well for a simple task of Pinusmugo mapping, showing comparably high classifica-tion accuracy of 74% �user’s� and 72% �producer’s�.The overall accuracy and the KHAT statistic areover-estimated since they represent only the three un-mixed classes, two of them belonging to the mostdistinct vegetation types.

Table 5. Correspondence matrix of the aerial photography interpretation �unsupervised approach� and the field survey. Numbers correspondto the number of pixels.

ISODATA classification �RS� Pine stands Mat-grasscommunities

Tall grass & herbcommunities

RS/FS correspondence

Pine stands 40475 12612 3810 71.1%Mat-grass communities 23978 140789 15527 78.1%Tall grass & herb communities 5368 57776 20112 24.2%FS/RS correspondence 58.0% 66.7% 51.0% 62.8%

Table 6. Correspondence matrix of the aerial photography interpretation �supervised approach� and the field survey. Numbers correspond tothe number of pixels.

Maximum Likelihood �RS� Pine stands Mat-grasscommunities

Tall grass &herb communi-ties

Road-alteredvegetation

Water RS/FS corre-spondance

Pine stands 49383 16534 4726 1275 572 68.1%Mat-grass communities 19069 146528 20418 2164 114 77.8%Tall grass & herb communities 7052 63794 25515 6520 13 24.8%Road-altered vegetation 2984 18371 6929 9811 0 25.8%Water 171 0 0 0 245 58.9%FS/RS correspondence 62.8% 59.8% 44.3% 49.6% 26.0% 57.6%

Figure 4. Changes in the area covered by Pinus mugo over the period of 1986-1997 detected from multispectral aerial photographs. Theblack column gives the total area covered by P. mugo in particular years in hectares, the white column discribes its overall increase.

269

In the supervised approach, class confusion oc-curred in some cases. Mat-grass communities weresometimes misclassified as roads or as P. mugostands. Road-altered vegetation and tall grass andherb communities were confused, which is notsurprising considering that they have a similar speciescomposition. Pine stands were mis-classified as wateror wet areas; P. mugo krummholz often grows on hu-mid soils or surrounded by standing water at stem,which changes the resulting spectral reflectance.

Comparison of different approaches

The ground inventory achieved a higher number ofvegetation classes, and the vegetation units werelarger, with more homogeneous and less complextexture. Not all the categories classified by FS couldbe discriminated from aerial data and vice versa andthe mapped vegetation units differed. It was thereforenot possible to compare the aerial-based vegetationmap directly to the field survey based on phytosocio-logical methods. For example, different plant commu-nity types dominated by P. mugo could not bediscriminated on aerial photographs, because domi-nating shrubs covered under-storey grasses and herbsand contributed strongly to the resulting spectral re-flectance. Transitional classes of field mapping wererelated to photo-interpreted classes that included plantspecies engaged in transition, and could therefore beregarded as correctly classified. The term ‘transi-tional’ is subjective and such stands could hardly beclassified equally by a different analyst. Some classes,such as stands dominated by Carex rostrata and thecommunity Crepido-Calamagrostietum villosae, werenot separated on aerial photographs; however, theirprevailing inclusion into classes representing standsof higher biomass �such as tall grass and herb com-munities or road-altered vegetation� seems to be just.Some classes obtained by photo-interpretation werenot determined by the field mapping �wet areas anddivision of mat-grass communities into three sub-cat-egories�, or were delineated only partly such as theroad-altered vegetation �Bistorto-Deschampsietum al-picolae in Fišerová 1991�.

For the purpose of a correspondence assessment,the FS vegetation types that were impossible to relateto the RS classes were joined into a large residualgroup, covering over 17% of the total area. This factbiased the correspondence and supposedly loweredthe level of correlation; still it is probable that an at-tempt to make questionable relationships between all

RS and FS classes would significantly amplify the er-ror. The best correspondence values resulted frompine and mat-grass classes �both RS classificationtypes�. Higher correspondence values for the unsuper-vised approach, compared to the supervised, werecaused by a lower number of assessed classes. In theunsupervised classification only the three unmixedclasses were taken into account, two of them ratingamong the best discernible. This fact artificially en-hanced the resulting correspondence �as well as theaccuracy� of the unsupervised classification.

As for the supervised classification and the FS, theproportion of water bodies �that are easy to discrimi-nate� was very similar, and those of pine stands andmat-grass communities were approximate �Figure 3�.Slightly larger area of pine stands �RS� seems closerto reality, as ground inventory included only largerpolygons and small individual shrubs were omitted.The RS approach gave comparably higher proportionof tall grasses and herbs �two times higher� and of theroad-altered vegetation �three times higher�. The cat-egory of tall grasses and herbs seems to be overesti-mated by the RS approach and underestimated by FS.The amount of vegetation classified as road-alteredwas more than three times larger when using RS ap-proach, compared to FS. This can be explained by thefact that human-made sites with this vegetation typewere not in the focus during FS, which resulted in itsunderestimation. Still some of the pixels included intothe road-altered vegetation RS class are located fur-ther off the road and belong to tall grass and herbcommunities that show similar characteristics andshare some plant species. Some categories were dif-ficult to compare, as they only partly overlap �e.g.transitional types�.

The results are consistent with those reported onthe comparison of ground- and remote sensing-basedvegetation mapping in arctic �Mosbech and Hansen1994, Spjelkavik 1995� and alpine regions �Frank1988�.

Problems with interpretation of aerial photography

Digital processing minimized inaccuracy originatingfrom the subjective element. Nevertheless, co-regis-tering the channels of the multispectral images priorto RS classification process was not perfect and pro-duced mis-registration error �mentioned e.g., in Cihlaret al. 1996�. As for both types of RS classification�compared in Ferguson 1991 and Joria and Jorgensen1996�, results could not have been field-verified by

270

the author at the same year the aerial data were gath-ered. Some causes of misclassification are thereforeunknown. The vegetation map of Fišerová �1991� isnot completely reliable as it was drawn into raw,non-rectified photographs and was therefore subjectto a geometric distortion. Nevertheless, since the al-teration of cell dimension did not change markedlythe RS/FS correspondence values it seems that theimpact of registration errors was not fundamental.Hereafter there is a question of the FS vegetation mapaccuracy. As Congalton �1991� stated, accuracy ofground surveys has been traditionally accepted with-out any confirmation and digital classifications areoften assessed with its reference. An assumption thatthe ground inventory is 100% correct is rarely validand can bias the digital classification assessment.

RS methods do not produce sharp boundaries be-tween vegetation types, which in reality form a con-tinuum rather then a well-defined mosaic. It can beargued that the classification error is partly caused byan attempt to place a boundary where in reality a gra-dient of change exists �Wood and Foody 1989�.Mixed pixels in transition zones contribute to the het-erogeneity of the resulting map, reflecting naturalconditions �Mosbech and Hansen 1994�.

Change detection

An analysis of vegetation changes from availablemultispectral aerial data failed to provide satisfactoryresults, because the field verification of older data wasnot possible. The results of change detection werealso distorted by problems related to the nature ofaerial data, i.e., the mis-registration and image expo-sure differences �see Bakker et al. 1995 andHolmgren et al. 1997�. Only the best recognizablevegetation type, P. mugo dominated stands, wastherefore chosen for the analysis. The ISODATA al-gorithm, previously proven to be convenient for sucha task, was employed as a fast and simple method,possible to apply over the large area of the NationalPark with satisfactory results. Since all three photo-graphs were taken at the end of the growing season�the second half of September� problems caused bythe shift of the phenological stage and consequentover/under estimation of changes were eliminated.The rapid increase of the area covered by P. mugowas caused mainly by aging of already establishedpine polycormons �growth and expansion ofbranches�.

Relevance to nature conservation

Previous dendrochronological studies �Soukupová etal. 1995� showed that under extreme climatic condi-tions the establishment of P. mugo on the Plateau is adelicate process and progressive spontaneous dis-persal of its population is slow. Still the expansion ofP. mugo represents a potential threat to local herbspecies, valuable from the conservation point of view,and to the unique geomorphologic components, suchas relic polygonal and patterned soils. These uniquesoils were developed in periglacial conditions of gla-cial periods and can be still preserved and activelyshaped if cryogenic processes �periodical soil regela-tion and deflation� continue. Such processes are dis-rupted if the surface is overgrown with shrubs. Thedescription of P. mugo dynamics is thus of an emi-nent interest to the nature conservation authorities andthe resulting maps will be used for conservation pur-poses.

Field inventory of vegetation is a time-consumingand laborious process. RS analysis, conducted in de-tail in one area, represents a methodology that can beextrapolated over larger areas covered by similarvegetation. The possibility of time-series studies ofRS data allow us to follow vegetation changes, andpossibly to separate the natural variation in vegetationattributable to natural factors from that induced byhuman impact.

Acknowledgements

I would like to thank B.U. Hansen, B.N. Rock, P. Py-šek and G. Rees for their help with the research andvaluable comments on the manuscript. Thanks to theKrkonoše National Park Authority, the Agency forNature Conservation and Landscape Protection inPrague, and D. Fišerová for providing the data. TheInstitute of Geography, Copenhagen University cour-teously made available the Chips software.

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