AVHRR-based forest proportion map of the Pan-European area

Tuomas Hamea,*Pauline StenbergbKaj Anderssona

Yrjo RausteaPamela KennedycSten FolvingcJanne Sarkealad

aVTT Automation, Box 1304, FIN-02044 VTT, FinlandbUniversity of Helsinki, Helsinki, Finland

cSpace Applications Institute, Joint Research Centre, ItalydStora Enso Forest Consulting Ltd., Finland

Received 30 December 1999; received in revised form 23 January 2001; accepted 27 January 2001

Abstract

A methodology was developed and applied to estimating forest area and producing forest maps. The method utilizes satellite data and

ground reference data. It takes into consideration the fact that a pixel rarely represents any single ground cover class. This is particularly true

for low-spatial-resolution data. It also takes into consideration that the spectral classes overlap. The image was first classified using an

unsupervised clustering method. A (multinormal) spectral density function was estimated for each class based on the spectral vectors

(reflectance values) of the cluster members. Values of the target variable — the proportion of forested area — were determined for the

spectral classes using sampling from CORINE (Coordination of Information on the Environment) Land Cover database. Each pixel was

assigned class membership probabilities, which were proportional to the value of the density function of the respective class evaluated at the

spectral value of the pixel. The estimate of forest area for the pixel was finally computed by multiplying the class membership probabilities

by the class forest area and summing over all the classes. The method was applied over a mosaic of 49 Advanced Very High Resolution

Radiometer (AVHRR) images acquired from the National Oceanic and Atmospheric Administration (NOAA)-14 satellite. The estimated

forest areas were compared with those extracted from the full-coverage CORINE data and with official forest statistics reported to the

European Commission’s Statistical Office (EUROSTAT). The forest percentage (proportion of forest area of the total land area) of 12

countries of the European Union was underestimated by 1.8% compared to the CORINE data. It was underestimated by 4.2% when

compared with EUROSTAT’s statistics and 6.0% when compared to United Nations Economic Commission for Europe/Food and

Agricultural Organization (UN-ECE/FAO) statistics. The largest underestimation of forest percentage within a country (compared to

CORINE) was in France (5.9%). The largest overestimation was found in Ireland, 15.6%. D 2001 Elsevier Science Inc. All rights reserved.

1. Introduction and objective

The forestry land in Europe is around 195 million ha.

This comprises 77% of forestland (sites capable of growing

closed stands) and 23% other wooded land (sites capable of

growing only scattered and stunted trees and bushes).

Forestland amounts to approximately 149 million ha, of

which 133 million ha are classified as being exploitable (i.e.,

where wood production is the major function). Unexploit-

able forest amounts to about 16 million ha and consists

largely of national parks, other nature conservation areas,

and areas in the mountains where wood production is not

profitable. The area of other wooded land is 46 million ha,

of which 36 million ha are located in the Mediterranean

region (Kuusela, 1994). It is predicted that Europe’s forest

area could increase by a further 5 million ha in the coming

20 to 30 years due to afforestation (e.g., on abandoned

agricultural land) (Kuusela, 1994).

Long-term economic utilization of forests requires that

they should be sustainably managed. Apart from being a

primary source of raw material, there is an increasing need

for geo-referenced information to monitor the condition and

function of forests and their role in carbon dynamics

(Kauppi, Mielikainen, & Kuusela, 1992). Information on

forest habitats should be improved, in order to identify

priority actions for protecting biodiversity at regional levels.

The forestry statistics archived by the European Com-

mission’s Statistical Office (EUROSTAT, 1998) and by

United Nations Economic Commission for Europe/Food

and Agricultural Organization (UN-ECE/FAO) are derived

0034-4257/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved.

PII: S0034 -4257 (01 )00195 -X

* Corresponding author.

E-mail address: tuomas.hame@vtt.fi (T. Hame).

www.elsevier.com/locate/rse

Remote Sensing of Environment 77 (2001) 76–91

from national enquiries but using independent procedures.

Data are collected from national correspondents by means

of a questionnaire and adjusted to approximate interna-

tional definitions.

A methodology to produce satellite image-based maps on

forests at a European level was developed in the Coordina-

tion of Information on the Environment (CORINE) Land

Cover Project that started in 1985 (EEA Task Force, 1992).

The CORINE Land Cover, obtained from satellite data

[mainly Landsat Thematic Mapper (TM)], is a digital geo-

graphical database describing vegetation and land use in 44

classes in vector format at an equivalent map scale of

1:100,000. Today, the CORINE Land Cover database covers

31 countries across Europe and North Africa. The next

CORINE mapping will be made during the period 2000–

2003. The CORINE database includes three classes of forest:

coniferous, deciduous, and mixed forest. The database has

been generalized so that areas smaller than 25 ha are not

considered but merged to the dominant land cover class.

The utility of low spatial resolution satellite data, partic-

ularly those acquired by the Advanced Very High Resolu-

tion Radiometer (AVHRR), for land cover mapping at

continental to global scales has been widely demonstrated

(e.g., Cihlar, Ly, & Quinghan, 1996; Eidenshink, 1992;

Lambin & Erlich, 1996; 1997; Malingreau & Belward,

1994; Townshend, 1994).

The most commonly used spectral feature in AVHRR-

based vegetation and forest mapping has been the Normal-

ized Difference Vegetation Index (NDVI) or the simple ratio

of near infrared (NIR) to red. The seasonal trajectories of

NDVI have also been applied in forest and land cover

classification (e.g., DeFries & Townshend, 1994; Gaston

et al., 1994; Lambin & Ehrlich, 1996; Loveland, Merchant,

Ohlen, & Brown, 1991; Myneni, Keeling, Tucker, Asrar, &

Nemani, 1997; Nemani & Running, 1996; Roy, 1997; Roy,

Kennedy, & Folving, 1997; Running, Peterson, Spanner, &

Teuber, 1986).

In the United States, forest vegetation has been mapped

into 25 forest type groups using Landsat TM and AVHRR

data (Zhu & Evans, 1994). The forest area was mapped by

computing regression models in which the AVHRR spectral

bands were the predictor variables and Landsat-derived

forest/nonforest maps were the predicted variables (Iverson,

Cook, & Graham, 1989). The mapping was made in a

stratified manner, using 15 physiographic regions covering

the Unites States.

Awall-to-wall map and digital database of forest cover of

the tropical belt are key products from the Tropical Ecosys-

tem Environment observation by Satellite (TREES) Project

(D’Souza & Malingreau, 1994; Malingreau et al., 1995). In

other studies of tropical forests (e.g., Malingreau & Tucker,

1988; Skole & Tucker, 1993), emphasis has been more on

monitoring deforestation, whether caused by logging or

forest fires (e.g., Belward, Kennedy, & Gregoire, 1994;

Kaufman, Setzer, Justice, Tucker, & Fung, 1990), rather

than on forest mapping.

A map from the Pan-European forests was prepared to

promote the International Space Year in 1992. This product,

derived from the National Oceanic and Atmospheric

Administration-AVHRR (NOAA-AVHRR) data, had a

reported classification accuracy of 82.5% (Hausler, Sara-

deth, & Amitai, 1993). Another map of Europe’s forests

forms part of the International Geosphere–Biosphere Pro-

gramme’s (IGBP) database, an exercise carried out to map

the land cover of the globe (Townshend, 1992).

More recently, methodologies for forest mapping at

continental scales have been within the framework of the

Forest Monitoring in Europe with Remote Sensing Project

(FMERS). Imagery from several instruments including

Landsat TM, SPOT, Indian Remote Sensing Satellite–Wide

Angle Field Sensor (IRS-WiFS), and European Remote

Sensing Satellite-Synthetic Aperture Radar (ERS-SAR)

were tested (Hame, Andersson, et al., 1998). A forest map

covering most of the European Union was produced using

stratified unsupervised clustering of a calibrated WiFS

image mosaic. In the Finnish national forest inventory,

satellite imagery from Landsat TM and Systeme pour

l’Observation de la Terre (SPOT) are now used operation-

ally. The method is based on a combination of a systematic

ground sample grid and satellite images, using the k-nearest

neighbor approach (Kilkki & Paivinen, 1987; Tomppo,

1991). The procedure resembles the method of Poso,

Paananen, and Simila (1987).

The most serious drawbacks of the existing satellite

image-based forest maps over Europe are firstly, that the

information is often binary, i.e., forest/nonforest. Secondly,

due to the small and fragmented forest areas common

throughout temperate and Mediterranean Europe, simple

detection and discrimination of the forest cover with low

spatial resolution satellite data becomes problematic. The

general tendency is that forest area is underestimated if the

forest is fragmented and overestimated if the forest cover is

homogeneous and uniform over large areas (Kuusela &

Paivinen, 1995). Finally, the accuracy and reliability of

existing European forest data sets have not been estimated

using statistically sound procedures.

Studies investigating alternative techniques to the con-

ventional image classification have included regression

analysis and mixture modeling. Regression analysis has

been applied to forest area estimation using AVHRR data

(e.g., DeFries et al., 1997; Iverson, Cook, & Graham, 1994;

Zhu & Evans, 1994). It has also been proven effective for

biomass estimation of coniferous forests, but less successful

where the target area includes a mixture of conifers and

broad-leaved trees. This is because the reflectance of the

NIR of broad-leaved trees is 1.5 times to twice as high as

that of conifers (Hame, Salli, Andersson, & Lohi, 1997).

Classification techniques applied to coarse resolution

satellite data such as the AVHRR have the inherent problem

that pixels seldom belong exclusively to any distinct ground

class (Foody, Campbell, Trodd, & Wood, 1992). Spectral

mixture modeling that allows a pixel having components

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–91 77

from several classes has proven to be effective in vegetation

mapping. The benefit of the mixture modeling has been

particularly apparent with coarse resolution data, where

several different surface components may occur within an

image pixel (e.g., Cross, Settle, Drake, & Paivinen, 1991).

Most often, the mixing of the classes is assumed be linear,

but also nonlinear approaches have been tested (Foody,

Lucas, Curran, & Honzak, 1997). A major difficulty in

applying a mixture model is the accurate selection of

spectral end-members, which is critical for the model

performance. Global mapping of percent woody cover,

herbaceous vegetation, and bare soil has been performed

using linear mixture analysis to the 8-km AVHRR database.

The mapping resulted in fair agreement with other land

cover data sets (DeFries, Hansen, & Townshend., 2000).

The objective of this study was to develop a method for

producing a Pan-European raster database where forest

proportion (FP) is estimated for each pixel. The database

could also be referred to as a forest probability database,

because the value of the pixel gives the probability that a

randomly selected point within the pixel falls in forest.

In the presented method, the estimate of FP for a pixel is

obtained as a probability-weighted average of the propor-

tions of forest area within the different spectral classes.

Unlike the traditional classification approach, where pixels

would be assigned the value of the ‘most probable’ class,

pixels are not classified but represent different classes with

different probabilities. The method resembles spectral mix-

ture modeling in-so-far-as it takes into account that a pixel

may have components from several spectrally distinct

classes. However, it does not require pure ‘end members’

(e.g., forest/nonforest classes). Instead, the classes represent

different values (ranges) of the proportion of forest area. The

maximum number of variables to be estimated is not related

to the number of the spectral channels, either.

2. Materials

2.1. Satellite data

Imagery from theAVHRR instrument onboard theNOAA-

14 satellite was selected as the reference satellite data. Such

data remain the most economically viable for use over the

Pan-European area. NOAA-14 was chosen as its overpass

time is around noon at local time. NOAA-12 data were

rejected on the basis of the early morning or late afternoon

overpass, meaning that reliable atmospheric corrections can-

not been applied due to the low sun elevation angles.

The year 1996 was chosen as the target year. It was

deemed recent enough to reflect the current situation in

European forests considering the scale of the study. The

reason for the selection of 1996 was also practical, since a

great number of images was already available from an

earlier study. One scene from 1997 was selected to reduce

the cloud-covered area. Some mountainous areas, such as

the Carpathians remained partially cloud-covered. The red

and NIR channels of the AVHRR instrument of the NOAA-

14 satellite were chosen as the input spectral data. The

original channels were used instead of the commonly used

NDVI, because the NDVI has not been proven effective in

estimating biomass, nor discriminating forest from other

land cover types (Hame et al., 1997; Hame, Salli, & Lahti,

1992). The surface temperature was considered to vary too

much from day to day to make the thermal channel an

applicable spectral feature for this study. Thermal data were

utilized for cloud masking.

The images were selected from the most stable part of the

growing season when the leaves and new needles are fully

developed, but before the initiation of the discoloration and

loss of foliage in the autumn. Utilization of seasonal data

would also have been possible but unrealistic because the

applied image interpretation methodology required that

spectral data would have been available for every pheno-

logical stage across the whole Pan-European area. Seasonal

and cloud-free image data would have been very difficult to

collect (Kasischke & French, 1997). Furthermore, the pos-

sible seasonal range would have varied from 9 months in the

southernmost areas to some 3 to 4 months in the northern-

most borders (DeFries et al., 2000). This is because the

presence of snow complicates optical image interpretation

(Hame, 1991; Oke, 1987).

An approach was taken in which calibrated images are

compiled to a single image mosaic, covering the whole

study area. Making the forest area estimation on an image

mosaic (instead of using individual images) was consid-

ered a more practical procedure than the traditional image

by image approach. The estimation of forest area on an

image by image basis would have been very time con-

suming and problematic as large proportion of images

were cloud-covered and thus unusable. Independent image

estimates would further require that each image be inde-

pendently validated.

The image mosaic was compiled using the raw AVHRR

data. This was carried out in preference to using an existing

mosaic (e.g., the 1-km data set from the EROS Data Center),

in order to better follow and understand each step of the

processing chain.

2.2. Ancillary data

For geocoding, the images, aeronautical and tactical

pilotage charts, and tourist maps at a scale of 1:400,000 to

1:1,000,000 were purchased. The maps covered the Euro-

pean continent. In addition, topographic map sheets at a

scale of 1:50,000 were available for the Finnish territory.

Map sheets at a scale of 1:1,000,000 were used only in those

areas where appropriate maps of larger scales were not

available (i.e., Norway, Spain).

The CORINE Land Cover data represented the ground

data and thus the definition of forest in this study follows the

CORINE nomenclature. Unfortunately, the definition of

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–9178

forest in CORINE is rather vague (area dominated by trees)

(CORINE Land Cover, 1993). This, plus the fact that the

map has been generalized and its reliability is not com-

pletely validated decrease the value of the CORINE as a

reference data set. Despite its known weaknesses, the

CORINE was found the best available database covering a

large part of the target area. CORINE data were taken as the

ground reference data without any modification.

3. Methodology

3.1. Image mosaic compilation

The image mosaic was compiled using 49 images that

covered the entire target area. The images were acquired

from four different sources: (1) Dundee Satellite Receiving

Station, (2) Tromso Satellite Station, (3) Finnish Meteoro-

logical Institute, and (4) Satellite Active Archive of NOAA.

3.1.1. Preprocessing

Image preprocessing consisted of the following tasks:

� image extraction from the distribution media;� computing or extracting a tie point grid with sun and

satellite angles;� radiometric corrections;� cloud masking;� geometric corrections for individual images.

3.1.2. Radiometric correction

Raw digital counts were first converted to TOA (top of

atmosphere) reflectance using time dependent calibration

coefficients that are published monthly by NOAA. These

reflectance values were then converted to atmospherically

corrected top-of-canopy reflectance by using the SMAC

(simplified method for atmospheric correction) algorithm

(Rahman & Dedieu, 1994). The aerosol optical depth of the

atmosphere was found to be the most significant variable

affecting this algorithm. Reflectance values were computed

for several images using different aerosol optical depth

values. The reflectance over mature coniferous forests was

evaluated. A value of 0.1 at 550 nm (unit-less) was found to

be the most appropriate. It produced reflectance values of

2% to 4% for mature forests in the red and 15 to 25% in the

NIR. Usually a large AVHRR pixel includes several vege-

tation types whereas one homogeneous forest stand has

many Landsat TM pixels. Thus, the reflectance in areas,

dominated by mature forests, can be one percentage unit

higher than the reflectance of individual mature forest stands

in a Landsat image. Finally, a BRDF (bidirectional reflec-

tion distribution function) correction was applied. The

BRDF method was based on the Roujean model with forest

surface parameters presented by Wu, Li, and Cihlar (1995).

The images were normalized to a nadir view with a solar

zenith of 45�. The atmospheric correction was applied to

every pixel.

A threshold using red and thermal channels was defined

interactively for each image to mask out the clouds. A pixel

was masked if the intensity in the red channel was higher

than the selected reflectance limit and the intensity in the

thermal channel 4 was lower than the temperature limit.

Cloud shadows were removed from the rectified images by

masking out approximately 3.5 km north from a detected

cloud. Threshold values were strict to remove also thinner

clouds. The maximum NDVI criterion was not used in cloud

screening since several overlapping images were used to

define the reflectance of the image mosaic.

3.1.3. Geocoding

A procedure developed by Andersson (1999) was applied

for image geocoding. First, an image mosaic covering the

research area was compiled to a rectangular latitude/longi-

tude coordinate system using eight manually selected

AVHRR scenes. This image mosaic served later as a

reference map for other images. Second, all the raw AVHRR

images were rectified to this coordinate system by using the

orbital parameters of the NOAA satellite. Using the refer-

ence mosaic, control points were selected automatically for

the images that had been rectified using the orbital param-

eters. In the final stage, all the images were rectified again

starting from the raw data. Thus, each image had only one

resampling phase. The pixel size was set to 0.006�N and

0.01�E. This was equivalent to about 700 m in the N–S

direction and 400–1000 m in the E–W direction, depending

on the latitude. An affine transformation with the nearest

neighbor resampling was applied in the computations. The

maximum satellite scan angle was limited to 45� by masking

300 pixels from the image edges. The reason for eliminating

the edges was that the atmospheric corrections do not

perform well at high scanning angles. Furthermore, the

pixel size on the ground is over 4 km at the image edge,

whereas it is around 2 km when the 300 pixels furthest from

nadir are excluded.

The final mosaic was compiled from radiometrically and

geometrically corrected images, by patching them together

using their geo-coordinates (Fig. 1). Clouds and cloud

shadows had been masked earlier from individual images

and, in cases of overlapping images, the mean reflectance

value was inserted. Computation of the mean reflectance of

several overlapping spectral measurements (pixels) was

considered to reduce the random noise in the images

compared to an alternative of using the reflectance of a

pixel from a single image. When the maximum NDVI

criterion was tested in image mosaic compilation, it was

noticed that two rather different combinations of red and

NIR could give the same NDVI. This resulted in large

radiometric variation spatially in the mosaic of original

spectral bands, since the neighboring pixels could be from

different images. Furthermore, the maximum NDVI crite-

rion would favor pixels from early summer images on

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–91 79

agricultural areas, whereas in the forested regions selection

of the images would likely vary more.

The maximum number of overlapping images was 21. In

most locations, there were three or four overlapping images

but in Scandinavia their average number was around 10. No

geometrical shifts or radiometric differences between

images could be seen in the mosaic. Despite the relatively

high number of images, some mountainous and arctic areas

remained cloud-covered.

3.2. Image interpretation

The image interpretation algorithm used the reflectance

mosaic as input. It produced a value of the target variable FP

for each pixel of the study area (Fig. 2).

3.2.1. Image clustering

Image clustering was carried out using an unsupervised

clustering method that was originally developed for change

detection purposes. In the method, an automatically selected

training sample consisting of the spectral means of 2� 2

pixels is clustered in a predetermined number (N) of

clusters, using the k-means algorithm. These 2� 2 pixel

groups are assumed to represent homogeneous ground

targets. The criterion for the homogeneity was the length

of the standard deviation vector of the reflectance values of

a 2� 2 pixel group compared to the standard deviation

vector of the whole image. The applied standard deviation

of a group varied from 2% to 6% of the deviation of the

whole image. This resulted in 2% to 10% samples from the

image pixels to be included in the clustering. The clusters

Fig. 1. Red band of the image mosaic in Lambert equal area projection. The size of the area is 4800� 4000 km2.

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–9180

were sorted using their red reflectance. The cluster with the

lowest red reflectance was assigned cluster code 1, whereas

the cluster with the highest red reflectance was assigned the

highest cluster code, i.e., the predefined total number of

clusters. The details of the clustering method are given in

Hame, Heiler, and San-Miguel Ayanz (1998). The spectral

mean vectors of the 2� 2 pixel groups, selected for the

clustering are from here on referred to as ‘observations’.

The clustering was performed on the image mosaic with

and without a geographic stratification. This meant that it

was performed on the whole Pan-European mosaic, and

separately for the Mediterranean and non-Mediterranean

regions (preclustering stratification). In all three cases, the

number of clusters was set to N = 50, which was found to

give a good coverage of the spectral space defined by the red

and NIR reflectance values over the study area. In the case

without a geographic preclustering stratification, postcluster-

ing stratification was implemented. The class-wise value of

FP was computed independently for the geographic strata.

The pixels representing water were masked out in the

clustering. If the NIR reflectance of a pixel was 12.0% or

lower, the pixel was assigned as water. This threshold was

defined interactively.

3.2.2. Class statistics and selection of observations for

ground sampling

The measured reflectance values in red and NIR defined

the spectral vector (x) of an observation. Using the spectral

vectors of the cluster members defining the spectral classes

(c), the mean (mc) and the covariance matrix (Sc) between

the two spectral channels were computed. A bivariate

normal density function ( fc) for each class (c) was estimated

based on these statistics:

fcðxÞ ¼1

2p j Sc j1=2

e�1=2ðx�mcÞTS�1c ðx�mcÞ: ð1Þ

The quadratic product appearing in the exponential of Eq.

(1) represents the squared distance from a spectral vector (x)

to the class mean (mc) as scaled and corrected for the

variance and covariance of the class (Strahler, 1980). Under

the normality assumption, it is c2-distributed with two (the

number of channels) degrees of freedom. The condition:

ðx� mcÞTS�1c ðx� mcÞ < c2

1�a ð2Þ

then defines the set of observations (x) for which the area

under the normal density curve is 1�a. It may be

interpreted that an observation not fulfilling Eq. (2) has a

probability smaller than a of belonging to the class (c).

A threshold value of 3.219 (a=.2) was used in selecting

observations for the CORINE sampling, i.e., observations

(x) for which the quadratic product (Eq. (2)) exceeded this

value for all classes (c) were not selected as representatives

for the sampling. The chosen threshold extracted approx-

imately 20% of the observations involved in the clustering

(the training data). The rationale behind this procedure was

to reduce the influence of members on the borders or

overlapping areas of the class contents. Namely, an inher-

ent problem of the probability method, where observations

are not assigned to a specific class but may have compo-

nents from several classes, is that, to determine the class

contents, the training data initially need to be classified.

By confining the ground sampling to the central part of a

class, only observations with high a posteriori probability

of belonging to a specific class were allowed to define the

content of this class.

The map coordinates of the accepted observations were

used to select ground-sampling units from the CORINE data.

Fig. 2. The main phases of the image interpretation process.

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–91 81

3.2.3. Computation of forest proportion

For each ground sample unit ( j) of a given class (c), the

forest proportion (probability) FPc( j) was estimated as the

area proportion of the unit classified as forest in the

CORINE data. The final forest probability for each class

(c), FPc, was computed as the mean of FPc( j) over the

sample units ( j). In the case where clustering had been

performed on the whole area (no preclustering stratifica-

tion), values of FPc were also determined for the Mediter-

ranean and non-Mediterranean regions separately. By this

‘postclustering geographical stratification’, the same spec-

tral class corresponded to a different ground content in the

two regions.

The sampling unit applied to the CORINE was 1�1

km2. It was assumed that this unit size unit fits within the

2� 2 pixel observation of the clustering even if some

rectification errors were involved. The 1-km2 squares were

located in the CORINE database using their map coordi-

nates. The areas of the different CORINE Land Cover

classes falling within each square were calculated. Then,

the areas were summed up into two classes: forest and

nonforest. These were then converted into the percentage

forest coverage. The CORINE classes, summed up to the

forest area were: broad-leaved forest (CORINE code 311),

coniferous forest (312), and mixed forest (313).

The output of the sampling phase was the mean propor-

tion of forest area, and its standard deviation for each

AVHRR class (Table 1). Five geographic stratification

approaches were tested. Thus, the CORINE sampling was

also performed five times.

The phases of the estimation procedure thus far involved

only those observations that were selected for the clustering.

In the final stage, a value of FP was assigned to each pixel

on the study area. It was computed as the sum of the product

of class membership probabilities for the pixel and the class

content. The probability P(cjx) of a pixel with spectral

vector (x) to belong to a specific class (c) was set propor-

tional to the value of the density function of this class at x,

fc(x) (Eq. (1)); i.e., equal a priori class probabilities were

assumed (Gorte & Stein, 1998). The class membership

probabilities for a pixel consequently were defined as:

Pðc j xÞ ¼ fcðxÞPNc¼1

fcðxÞð3Þ

fulfilling the condition that the sum over all the classes

should be equal to 1.

The forest proportion FP(x) assigned to the pixel (x) was

obtained by multiplying the class membership probabilities

P(cjx) by the class values (FPc), and summing over all the

classes (Eq. (4)):

FPðxÞ ¼XNc¼1

Pðc j xÞFPc: ð4Þ

FP represents a weighted average of the proportions of

forest area estimated (by ground sampling) for the different

spectral classes. The weights, here termed class membership

probabilities, are measures of the likelihood by which a

pixel ‘belongs to’ a specific class. In a traditional

classification approach, the pixel would be assigned to the

‘most probable’ class and the target variable be given the

value (often binary) estimated for that class. For pixels with

a spectral signal close to a class mean, the estimate of FP by

Table 1

An example of the result of the CORINE sampling from the Mediterra-

nean area

Class Sample size

Forest

percentage

Standard deviation of

forest percentage (%)

14 2570 31.667 30.89

15 3271 31.193 34.692

16 1808 9.558 22.944

17 3183 25.388 31.831

18 1276 5.055 17.139

19 2549 26.271 33.887

20 3014 22.056 29.16

21 1756 10.504 21.183

22 3164 18.069 29.731

23 2220 11.969 21.634

24 3475 16.141 27.677

25 3146 15.904 27.694

26 1305 3.203 10.445

27 1705 9.644 22.584

28 626 3.406 14.454

29 2874 9.794 21.938

The total number of classes was 50.

Table 2

Statistics of the three clustering classifications

Clustering characteristics

Clustering

Number

of classes

Standard

deviation

limit (%)

Number of

observations

in clustering

Proportion

of whole

image (%)

Number of

observations

in largest class

Number of

observations in

smallest class

Size of CORINE

sample

Whole 50 2.0 608,048 2.2 53,944 77 39,637 (28,865TrB)

(10,744M)

Mediterranean 50 6.0 137,968 9.9 4725 572 99,455

Temperate and

boreal

50 4.0 366,926 2.9 22,458 36 48,968

Numbers in the parenthesis show the sample size in the postclustering stratification.

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–9182

the present method will be very similar to the class mean

because the value of P(cjx) (Eq. (3)) for this specific class ishigh (close to one). In the case of ‘uncertain’ pixels, whose

spectral signal is not close to any class mean, the fact that

they are not ‘forced’ to a specific class eliminates the bias

from misclassification.

4. Results

4.1. Characteristics of spectral classes

A user-given clustering parameter gave the highest

allowed standard deviation (length of the deviation vector)

within a candidate observation (2� 2 pixel group) com-

pared to the standard deviation of the whole image (Hame,

Heiler, et al., 1998). This parameter determines the number

of the sampled observations and thus regulates the compu-

tations in the clustering procedure, ensuring that they do not

to become too heavy. A lower value had to be given in the

clustering of the whole mosaic because the area was large

and thus the number of candidate observations higher (Table

2). The CORINE data were not available in the vast

majority of the target area. This is illustrated by the

relatively low CORINE sample size in the classification

of the whole image.

Those 2� 2 pixel groups that passed the deviation test

and were thus included in the clustering process were not

evenly distributed between the land cover classes but con-

centrated in the forestry classes. This is because the forests,

with their low reflectance values, showed a higher spatial

homogeneity. It was not assumed to harm the result, because

the number of observations within a cluster was in any case

rather high. However, there is a low risk that ground cover

Fig. 3. Spectral mean values of the original classes in the clustering of the whole mosaic. The drawn lines divide the spectral space to classes whose forest

percentage is above and below 10% according to the CORINE sample. The classes have been sorted by their increasing red reflectance. Solid line: All the

CORINE data had been used in the sampling. Dash and dot line: Only Mediterranean CORINE data had been used in the sampling (poststratification). Dash

line: Only the temperate and boreal CORINE data had been used in the sampling (poststratification). Note that the 10% limit is not referring to within-forest

canopy closure. Forest density was not considered within the forest class in the CORINE data.

Fig. 4. Spectral mean values of the original classes in the clustering to the Mediterranean mosaic. The line divides the spectral space to classes whose forest

percentage is above and below 10% according to the CORINE sample from the Mediterranean zone.

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–91 83

types whose inherent characteristics is one of heterogeneity,

were not represented in the clustering process at all. Such

cover types could be cities, for instance.

Figs. 3–5 show that the 10% forest border (practically

nonforested areas) in the spectral space runs consistently

between red reflectance 5% to 10%. When the red reflec-

tance is close to 10%, the NIR reflectance of the forested

areas is low. The border is at somewhat higher reflectance

levels in the Mediterranean area than elsewhere, but the

differences are small. Fig. 5 shows also that the highest

reflectance values (above 20% red reflectance) are practi-

cally missing in the temperate and boreal regions.

In the Mediterranean zone (preclustering stratification),

the area proportions of the original classes match very well

Fig. 5. Spectral mean values of the original classes in the clustering to the temperate and boreal mosaic. The line divides the spectral space to classes whose

forest percentage is above and below 10% according to the CORINE sample from the temperate and boreal areas.

Fig. 6. Area proportion of the original classes in the image mosaic (including all image pixels) and distribution of the CORINE sample into the classes.

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–9184

Fig. 7. Forest percentage of the original classes (solid line) ± standard deviation of the percentage (dashed line) computed from the CORINE sample.

Fig. 8. The final FP map (combined from the Mediterranean and the temperate and boreal strata). Prob_final means an estimate of FP within a 1�1-km2 pixel.

White is water and gray is cloud or snow. The maximum estimated FP was 93%. Original scale: 1:6,000,000.

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–91 85

with the class proportions in the CORINE sample. This

indicates that the CORINE data were representative for the

Mediterranean zone. For the temperate and boreal zones, the

matching is much poorer (Fig. 6). In the temperate and

boreal areas, the CORINE sample was concentrated in the

higher numbered classes, i.e., classes representing a lower

forest percentage. This reflects the fact that the boreal region

was included in the clustering but excluded in the CORINE

data. However, the sample size was in all geographic strata

several hundreds of 1-km2 units for clusters that represented

any significant FP.

The standard deviations of forest percentages within each

class were high (Fig. 7). In the forested classes, they were

typically over 30%. The stratification did not clearly

decrease the deviation — with one exception. When the

clustering and CORINE sampling were made to the temper-

ate and boreal areas only, the standard deviations of the

classes, representing highest forest cover percentage (low

Fig. 9. Original clustering result from central Spain and Portugal (above) and final FP map (below). For the upper figure, the classes have been sorted by their

decreasing red reflectance (class with lowest red reflectance has the lightest gray tone value). The FP estimates below have been scaled with a linear scaling.

The minimum estimated forest percentage is 1% and the maximum 66%. The area size is approximately 620� 500 km2.

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–9186

class numbers), were less than 30%. This was lower than in

the other sampling/clustering combinations. In this prestra-

tification approach, only nine classes (from 42 to 50) had

forest percentage less than 10%.

It was concluded that the best alternative for image

interpretation was the stratified approach in which the image

interpretation is done separately for each stratum, i.e., the

preclustering approach. The justification to this conclusion

was the good matching of the CORINE and ground data in

the Mediterranean area and the decreased standard devia-

tions of the classes representing the highest FP in the

temperate and boreal areas. Furthermore, a visual evaluation

showed that the tundra areas were better discriminated from

the forested areas in the stratified approach.

4.2. Evaluation

All the interpretation phases were done to the image

mosaics in the latitude–longitude coordinate system. The

final map was transformed to one specific version of the

Lambert azimuthal equal area projection (Fig. 8). This

projection is the one used in the CORINE Land Cover

database. The spatial pattern of the classes in the original

unsupervised classification and in the final forest proportion

maps was very similar (Fig. 9). The forest area at high

latitudes may be overestimated. There are some obvious

errors, such as the estimated 47% forest cover for Iceland.

Iceland may be a special case in which the reflectance of the

dark volcanic rocks resembles that of forests.

The AVHRR forest area estimates show rather good

agreement with the CORINE data and the official forest

statistics. In the 12 countries shown in Fig. 10, the forest

area in the FP map was 4.2% lower than that in the official

EUROSTAT statistics. The underestimation compared to the

UN-ECE/FAO statistics was 6.0%. The underestimation was

1.8% compared to the CORINE data, which represented the

ground data in this study. It was considered appropriate to

evaluate the performance using CORINE data because the

sampling rate from CORINE was low, particularly in the

temperate and boreal region (Table 2). In fact, the compar-

ison with the official forest statistics is somewhat irrelevant

since the CORINE Land Cover Map and the official

statistics have completely different origins. However, the

comparison also indicates that the CORINE map, with few

exceptions, harmonizes well with the official statistics

although the forest area is smaller.

The most serious underestimation of forest area may

have occurred in France (Fig. 11). The reason for this is

still somewhat unclear. The possible reasons include the

mosaicking procedure, a seasonal effect, atmospheric cor-

rection, and the impact of the geographical stratification

on the image interpretation. The forest statistics show that

Fig. 10. Forest area in 12 countries of the European Union. Clouds have been taken into consideration in the AVHRR-based forest area by assuming the cloudy

areas to have the same forest cover proportion as the country in general.

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–91 87

Fig. 12. Forest area in Germany by regions.

Fig. 11. Forest area in France by regions.

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–9188

the coniferous percentage in France is only 38% (Kuusela,

1994). The percentage is approximately 70% in Germany

where the forest area estimation was more successful (Fig.

12). France and Germany belonged to the same geo-

graphic stratum.

The area-weighted root mean square errors (RMSE)

for country-wise forest percentage and regional forest

percentage for France and Germany were computed using

Eq. (5).

RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXi

ai

Aðpi � piÞ2

sð5Þ

where ai are the areas of the different countries or regions

considered, A is their total area (sum of ai:s), and pi and pidenote the estimated and reference (CORINE) forest

percentages, respectively, for the different countries/regions.

The country-wise RMSE for 12 European Union coun-

tries was 4.6%, for regions in France 8.8%, and in Germa-

ny 3.9%.

An exception to the general underestimation was Ireland,

where forest area was seriously overestimated (Fig. 10). The

reason for this is thought to be the moorlands, which possess

a signature similar to that of coniferous forests, even though

the main vegetation consists of dwarf shrubs. A similar

overestimation could be observed in Scotland (Fig. 8). The

area of moorlands was too small at the European scale to be

adequately represented in the CORINE sample. For Finland,

the forest area was overestimated by 3.2% and in Sweden by

5.6% compared to the official statistics from the national

forest inventories.

5. Discussion

The estimated forest areas were close to the forest areas

of the forest statistics although in general the forest area

was somewhat underestimated. The underestimation was

most severe in France and in the Mediterranean region. In

the Mediterranean region, the underestimation was some-

what surprising since the area selected in the CORINE

sampling was almost 10% of the total area. In the evalua-

tion, it was assumed that the cloudy areas had the same FP

as the country or region in general. This may be a

conservative estimate to the forest distribution because

both forests and clouds occur more frequently in mountain-

ous areas. However, in Austria, the correction may have

been too strong, because the snow-covered Alps was also

considered as ‘clouds’ in the interpretation. The proportion

of clouds varied from less than 1% of Spain and Portugal

to 12% and 10% in Austria and Germany, respectively.

When the cloudy areas were considered to have the same

FP as that of the forest statistics, the forest area increased

in the comparison procedure by 5% in Austria and 3% in

Germany. Elsewhere, the effect was smaller. The role of

the missing CORINE data in the overestimation of the

forest area in Finland and Sweden and in the apparent

overestimation in the tree line zone in Northern Russia

remains unclear.

If the forest area in the CORINE is systematically over-

estimated or underestimated, it would cause a corresponding

overestimation and underestimation of the forested area in

the maps that are derived from the AVHRR images. How-

ever, this relationship is not straightforward for the proce-

dure applied in this study because the approach is not based

on a multiphase or multistage sampling strategy. The forest

percentage for the spectral classes was derived using only

such observations whose reflectance values were rather

close to the mean reflectance of a class. Moreover, the

requirement of spectral homogeneity in selecting the train-

ing data also implied that the ground sampling was

restricted to homogeneous areas. Due to the area general-

ization procedure of the CORINE, it can be assumed that in

areas where the true forest percentage is low, the forest area

in CORINE is underestimated and vice versa.

The possible geographic mismatch between the CORINE

data and the AVHRR mosaic and the effect of overlapping

AVHRR pixels should lead to the averaging of forest cover

percentages for the classes. The forest cover percentages of

classes with low FP should increase and percentages of

classes with high FP should decrease. This should introduce

no bias to the general procedure. Possible inconsistency in

forest definition between countries in CORINE would have

a similar effect as the possible geographic mismatch

between the data sets. The inconsistency averages the forest

percentages of the classes within a stratum. In general, the

forest area in CORINE was lower than the forest area in the

official statistics, but possible intercountry inconsistency in

forest definition could not be confirmed.

Despite checking almost all relevant AVHRR scenes

over one summer, significant parts of mountainous and

northernmost areas were still cloud-covered. A partial

solution to the cloud problem is to combine images from

different summers. In an earlier study, images from three

years were successfully combined (Hame et al., 1997).

Another possibility is to use any possible image and

automatic selection of likely cloud-free pixels (Roy et al.,

1997; Running et al., 1994). However, this approach may be

difficult to combine with the idea of using averages of

several pixels in the mosaic.

A conclusion of the study was that the number of

geographic strata should be increased. In addition to select-

ing the boreal zone to a specific stratum (if ground data are

available), it seems to be important to separate a specific

Atlantic stratum from more continental temperate forests. A

successful estimation of Irish and Scottish moorlands would

require a very detailed stratification.

In the future research, a more physical-based approach in

the class labeling will be tested to reduce the problems with

the reference data. Furthermore, a more detailed geographic

stratification will be applied and variables other than forest

area will be considered.

ˆ

T. Hame et al. / Remote Sensing of Environment 77 (2001) 76–91 89

Acknowledgments

We would like to acknowledge the support of the

European Environment Agency’s Topic Centre for Land

Cover, for making the CORINE Land Cover database

available for this work. We also wish to express our

gratitude to the anonymous reviewers for their good and

constructive comments and suggestions.

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