Woody vegetation increase in Alpine areas: a proposal for a classification and validation scheme

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Woody vegetation increase in Alpine areas: a proposalfor a classification and validation scheme

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Woody vegetation increase in Alpine areas: a proposal for aclassification and validation scheme

M. MAGGI*, C. ESTREGUIL and P. SOILLE

European Commission – Joint Research Centre, Institute for Environment and

Sustainability, Via Fermi 1, 21020 Ispra (VA), Italy

(Received 22 November 2004; in final form 24 June 2005 )

This paper presents a change detection analysis based on a region growing

segmentation approach which combines both spectral and spatial information.

The test site is a French Alpine protected area, which like many other mountain

areas is characterised by a general increase of forest and woody vegetation due to

the abandonment of traditional land use practices. Two Landsat images of the

years 1984 and 2000 were used and a classification scheme nomenclature based

on four vegetation change classes, implying a gradual modification of land cover,

was adopted. The accuracy of the change map was assessed both during two

visits on the field and using a bi-temporal aerial photographic coverage. A

sampling scheme specifically conceived for change detection products was

adopted. Error matrices and accuracy indices to assess commission and omission

errors of the change maps were generated.

The proposed change detection methodology circumvents limitations which

are intrinsic to traditional classification procedures based only on spectral

information. On the basis of the accuracy assessment, overall accuracy was 90.1%

and the increase of woody vegetation turned out to be the vegetation change class

better estimated, with user and producer accuracies of, respectively, 62.3% and

70%. However, confusion between the no change and the other vegetation change

classes was noticed, due to standard problems encountered in change studies.

Advantages and drawbacks of the use of multitemporal aerial photographs as the

validation data set are also discussed.

1. Introduction

The abandonment of traditional land use practices has become an important force

driving landscape changes in many mountainous areas since the middle of the 19th

century. Indeed, more and more steep slopes and unfavourably situated areas have

been abandoned. A reduction in rural population has lead to a decline of both

agricultural activities and grazing animals. When fields, meadows, or pastures are

abandoned, the natural vegetation takes over and woody vegetation begins to

appear, causing a progressive increase of forest cover. Such a change in land use

leads to significant alterations in diversity, appearance and functioning of the

landscape. This issue is of great concern to local planning agencies and protected

area managers for potential consequences on tourism (Hunziker and Kienast 1999,

Hochtl et al. 2005), loss of biodiversity (Burel and Baudry 1995, MacDonald et al.

*Corresponding author. Present address: Via Stradella 13, 20129 Milano, Italy.Email: [email protected]

International Journal of Remote Sensing

Vol. 28, No. 1, 10 January 2007, 143–166

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2007 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160600851785

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2000), cultural landscapes (Meyer and Turner 1994), and traditional land use

practices (Tappeiner et al. 1998). In addition, recent studies demonstrate the

responsibility of forest regrowth for the ‘missing sink’ in the global carbon budget

(Foody et al. 1996, Houghton et al. 1999, Caspersen et al. 2000).

In the context of nature protection, the estimation of land cover changes and

particularly the increase of woody vegetation occurring within a protected area

contribute to the creation of a simple but effective indicator of its conservation

status (Cernusca et al. 1999). Furthermore, the spatial estimate of land cover change

serves as a benchmark state of the environment against which benefits of local,

national, and international policies can be assessed.

This study aims to provide managers of protected areas with a methodology to

estimate land cover changes, and in particular woody vegetation increases, from

Landsat imagery. The test site which has been chosen for this study is the National

Park of Mercantour in the French Alps. This area, as many other Alpine areas, is

experiencing a general increase of the surface occupied by forests and woody

vegetation due to a decline of both agricultural activities and grazing animals.

Remote sensing is the only option to monitor such a large area in a timely and cost

efficient manner (Song et al. 2002), and to understand the general trends which are

active within the region.

To assess land cover changes affecting the National Park of Mercantour, two

Landsat images were used, acquired respectively on 23 July 1984 and 27 July 2000.

The change detection approach which was developed combines spectral and spatial

information. In a previous work (Maggi et al. 2004), this approach proved to work

better than a maximum likelihood pixel-based classifier that had a tendency to

overestimate changes. However, the results while promising were not quantitatively

validated; this study thus serves to fully test the new method and further proposes a

validation scheme. An initial spectral analysis allows us to define a set of pixels

(seeds) whose change class is known with a high confidence level. The procedure

then iteratively looks for neighbouring pixels in the spatial domain having similar

spectral signatures on the basis of a seeded region growing approach. Such a

combined methodology, originally conceived for one-band images (Adams and

Bischof 1994), is applied here for the first time to multispectral and multitemporal

data in the context of natural resources assessment.

To reinforce the link to protected areas and forest management on the ground,

this study paid attention to two other issues: the first related to the use of an

appropriate classification nomenclature for vegetation dynamics and management

purposes; the second dealing with the accuracy assessment of the map produced and

its reliability for the final users.

Although the focus of the study is on the increase of woody vegetation, the

nomenclature scheme we adopted is based on four vegetation change classes:

‘Increase of woody vegetation’, ‘Increase of herbaceous biomass’, ‘From vegetation

to mineral ground’, and ‘Decrease of woody vegetation’. The change classes

proposed are coherent with the short time period (1984–2000) of this study, which

does not allow the detection of abrupt changes in forest cover in a region where

afforestation programmes or large cuttings are not carried out. The types of

vegetation changes also justify our preference of a change detection combined

analysis approach (Singh 1989, Coppin et al. 2004, Lu et al. 2004) over a

conventional post-classification comparison method, since the latter only indicates

complete changes in class membership (Foody and Boyd 1999). Our change classes

144 M. Maggi et al.

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imply gradual modification of land cover (e.g. variations in the amount of the

woody vegetation), as opposed to post-classification change detection methods

which are insensitive to these more gradual or subtle variations in class properties.

To assess the accuracy of our change map, we adopted a sampling scheme

particularly aimed at increasing the sampling effort for change classes, which are

rare. As a test data set, a bi-temporal aerial photographic coverage was used.

Following a visual photo interpretation, error matrices were generated which

provided the starting point for a series of descriptive statistical techniques.

Advantages and drawbacks of the use of multitemporal aerial photographs as

validation data set were finally discussed.

2. Literature review

Applications of remotely sensed data for land cover change detection, and

particularly forest cover over time, have been reported by many investigators.

State-of-the-art of change detection techniques is discussed in depth by Singh (1989),

Mas (1999), Coppin et al. (2004), and Lu et al. (2004). Furthermore readers can refer

to Coppin and Bauer (1996) and Rogan et al. (2002) for reviews specifically

addressing forest change detection.

This section reviews literature on the three main issues which are addressed in this

paper: image analysis methods based on spectral and spatial information,

nomenclature schemes for forest change studies, and validation procedures for

change detection products.

Most digital change detection methods applied to forest ecosystems are based on

per-pixel classifiers and use only spectral information (Luque 2000, Sader et al.

2001, Woodcock et al. 2001, Rogan et al. 2002, Wilson and Sader 2002). The major

drawback of these approaches is the fact that a pixel is classified depending only on

its spectral response, without considering the signal of its neighbouring pixels. The

result is often noisy (Woodcock et al. 2001, Wilson and Sader 2002, Maggi et al.

2004) and lacks spatial consistency (Soille 1996). To circumvent these limitations

spatial information should also be considered. Methodologies which emphasise the

use of spatial information belong to the category of segmentation techniques

(Haralick and Shapiro 1985, Woodcock and Harvard 1992, Pal and Pal 1993, Soille

1996, Pekkarinen 2002). Segmentation can be either edge-based or region-based.

Edge-based image segmentation methods use gradient operators which enhance

boundaries between homogeneous groups of pixels. Region-based segmentation

methods proceed the opposite way: first they locate homogeneous regions on the

image; then they recursively grow these regions using some spatial and spectral

criteria until all pixels of the image are assigned to a region. In this case, boundaries

are created when regions meet. Region-based segmentation techniques include

region growing, region merging, and region splitting approaches. The use of

segmentation techniques for change detection, and in particular for forest change

detection, is still limited. Even more limited is the progress in such approaches for

analysing multispectral remote sensing data (Kartikeyan et al. 1998). This paper

aims at making progress in such a field of research.

Regarding nomenclature, studies focusing on the detection of forest changes

usually adopt coarse change classes such as forest change/forest no-change (Sader

et al. 2001, Woodcock et al. 2001) or cuttings/partial cuttings/no-change (Wilson

and Sader 2002). Rogan et al. (2002) examine five vegetation change classes (no-

change/vegetation increase/vegetation decrease/change in non-vegetated regions/

Woody vegetation increase 145

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recharge of reservoirs). Lowell (2001) detects decreases and increases of woody

vegetation. Coppin and Bauer (1994) propose a change detection methodology built

on four basic change categories (canopy depletion, recent storm damage, canopy

increment, and no-change) that meet predefined operational and scientific criteria.

Another perspective to study forest changes with remote sensing data is classifying

successional vegetation stages: this involves detecting more subtle vegetation

differences than does the mapping of abrupt forest changes. Either a multitemporal

(Hall et al. 1991a, Foody et al. 1996, Helmer et al. 2000, Song et al. 2002) or a

monotemporal (Fiorella and Ripple 1993) approach is adopted in this case.

Concerning the accuracy of change maps, a variety of factors influence the

accuracy of land cover change products, namely the classification scheme employed,

the classification and rectification errors, the remote sensor system characteristics,

the environmental conditions, and the change detection algorithm (Biging et al.

1999, Khorram et al. 1999). The error assessment process itself can be a source of

error and confusion (Congalton and Green 1993). It follows that the estimation of

accuracy of a change product is a difficult and challenging task (Congalton and

Green 1999, Foody 2002). Difficulties also arise from the impossibility of field

verification for past time periods or from the limited availability of reliable historical

data set.

Most of the change detection studies adopt the error matrix-based accuracy

assessment method developed for single-date remotely sensed data. These studies

use the error matrix to evaluate the accuracy of either the single classifications

(Luque 2000, Mertens and Lambin 2000) or the final land cover change map

(Coppin and Bauer 1994, Foody et al. 1996, Sader et al. 2001, Woodcock et al. 2001,

Rogan et al. 2002, Wilson and Sader 2002). Morisette and Khorram (2000) propose

using accuracy assessment curves to analyse the reliability of change detection

results. Lowell (2001) developed an area-based accuracy assessment method for

change map analysis. Liu and Zhou (2004) propose a rule-based method to separate

cases of real land use change and possible classification error, to be used when ideal

ground data are not available. Khorram et al. (1999), in a monograph specifically

devoted to accuracy assessment of change detection products, state that sampling

techniques designed for single coverages (Congalton 1991, Janssen and van der Wel

1994, Stehman and Czaplewski 1998, Stehman 1999) do not provide an adequate

framework for determining also the accuracy of a change detection product. The

main reason he advances for the differences in sampling strategy between a one-

point-in-time (OPIT) and a change thematic map is that, in the latter, change

categories usually represent a small portion of the original map and differently from

a OPIT map, a change map often has many classes with low or rare frequencies of

occurrence. The relative scarcity of change pixels in the change map and the

likelihood that they are concentrated in relatively few areas imply that these pixels

can be considered rare events that would only occasionally be detected using

traditional sampling techniques, unless the sampling intensity is high. To circumvent

this limitation, different sampling schemes for the change and no-change classes

should be adopted, in order to increase the sampling effort for rare classes. This was

also addressed in this paper.

3. Study area

The study area entirely corresponds to the French National Park of the Mercantour,

created in 1979 and mostly situated in the departement (district) of Alpes Maritimes

146 M. Maggi et al.

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at the border between Italy and France. Its climate, which is both Mediterranean

and Alpine, together with the geological composition of crystalline and sedimentary

rocks, is responsible for the high diversity of its flora including a high number of

endemic species. Altitudes range from 400 m to 3143 m above sea level (a.s.l.) and

landscapes are extremely contrasted.

The Park is composed of a core area covering 68,500 ha and of a peripheral buffer

zone of 146,500 ha. The former mostly corresponds to a Natura2000 site proposed

in the framework of the European Habitat Directive (Council Directive 92/43/EEC),

while the latter constitutes an area where both conservation and local development

objectives are pursued.

The Park territory is mainly covered by pastures and meadows (44%), followed by

forests (40%), European larch (Larix decidua Mill.) being the most widespread

species (55% of Park forests). Broadleaves cover only a very small part of the

Mercantour’s territory (less than 10%) and dominate the altitudinal range between

600 and 1100 m a.s.l.. Conifers prevail between 1100 and 1700 m a.s.l.: Scots pines

(Pinus sylvestris L.) on southern slopes are the equivalent of fir (Abies alba Mill.)

and spruce (Picea excelsa Link) forests on northern slopes. The latter have often

been replaced by man with larch stands which can be devoted both to wood

production and pastoral activities. Larch is the dominant species at the subalpine

level, between 1700 and 2200 m a.s.l., where it constitutes transitional silvo-pastoral

formations (pre-bois) (Laurent 1988). The preservation of these traditional

landscapes which are mostly located in the central zone and which depend on

human intervention is one of the prime objectives of the Mercantour Park owing to

their high historical, cultural, and aesthetic values (Motta and Dotta 1995).

Forest gives place to grasslands, heaths, and rocks above 2200 m a.s.l. on north-

western sided slopes and above 2500 m a.s.l. on south-eastern exposed sites

(Biancheri and Claudin 2002). However, the position of the timberline does not

correspond anymore to a natural limit but is the result of historical human

interventions.

In the last decades, the surface occupied by forests and in particular larch forest

stands within the National Park of Mercantour and in the Maritime Alps has

increased owing to a decline of agricultural and pastoral activities (Sandoz et al.

1998). When human intervention ceases, larch stands are exposed to natural

dynamics: shrubs invade, new young larches settle, the herbaceous component

progressively diminishes, and other arboreal species such as pines, firs, and spruces

invade and become dominant, making larch formations disappear. A diachronic

analysis demonstrated that the time interval which is necessary in order for the

aforementioned process to be complete is between 50 and 75 years (Sandoz et al.

1998). Few decades, or even 10–15 years, are considered sufficient for the

development of dense woody pioneer formations 4–5 m high (Guidi and Piussi

1993). The process generates important modifications of landscape structure and

functioning (Didier 2001).

In such a context the monitoring of the increase of forest and woody vegetation is

mandatory for park managers to adopt appropriate conservation measures. At the

moment, the National Park of Mercantour is pursuing a policy to both encourage

pastoral activities in the most favourable areas and to develop ungulate populations

which have an important role in the maintenance of the larch silvo-pastoral

formations. Attention is also paid to vulnerable areas where intense pastoral

activities are carried out and erosion processes are active.


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4. Available data

Two Landsat images (scene 195/29), covering the entire study area, were used to

assess land cover changes. The earlier image was acquired by the Thematic Mapper

(TM) sensor on 23 July 1984 and the later image by the Enhanced Thematic Mapper

(ETM) sensor on 27 July 2000. This image was available at the European

Commission-Joint Research Centre within the IMAGE2000 database facility (http://

image2000.jrc.it/). It is an orthorectified product resampled to a Lambert Conic

Conformal projection. The mean square error of the rectification is 12 m and the

pixel size of the rectified image is 25 m.

A multitemporal aerial photography coverage was also available. The set

consisted of 50 colour infrared aerial photographs (APs) having a nominal scale

of 1 : 17,000 acquired in 1984 and located over those areas of the Park where major

changes were detected or more critical areas were observed and a check was

considered necessary. A full coverage of digital orthophotos acquired in 1999 and

2000 having a spatial resolution of 0.5 m was available.

Other geographic information, available for the study area, consisted of a 50 m

cell digital elevation model (DEM) and a forestry map produced by the Inventaire

Forestier National (IFN) in 1996 at a scale of 1 : 100,000. The latter contains

information on both forest categories and management schemes.

5. Methods

5.1 Pre-processing

In order to refer the multitemporal remote sensing data set to a common geometry,

the TM image acquired in 1984 was orthorectified to the same projection and spatial

resolution as the 2000 ETM image. Ground control points (GCPs) were selected on

the already orthorectified Landsat ETM image, which was assumed as the

geographic reference, and we applied a nearest-neighbour resampling algorithm.

The RMS error was lower than 1 pixel, the pixel size being 25 m625 m. However, a

residual mis-registration was noticed in the south-eastern part of the Park probably

due to the presence of cloud coverage on the TM image which prevented from

localising an appropriate number of GCPs in this area.

A second pre-processing step was represented by radiometric normalisation, a

process recommended for change detection studies especially when the objective is to

identify types of land cover changes (Hall et al. 1991b, Coppin and Bauer 1996). In

this context, a radiometric rectification technique was performed to compensate for

sensor calibration, atmospheric, and illumination differences between images (Hall

et al. 1991b). The radiometric rectification algorithm identifies radiometric control

sets, i.e. sets of scene landscape elements with a mean reflectance which is expected to

change little over time. The average digital count of these radiometric control sets or

pseudo-invariant features (PIFs) (Song et al. 2001) are used to calculate linear

transforms, relating digital number values between images. In this work, the 1984 TM

image was used as reference image and two types of PIFs were selected: high mountain

lakes and rocks, corresponding to dark and bright radiometric control sets. Then a

linear transformation was derived for each band, transforming the individual means

of the PIFs in the 2000 image to those of the 1984 image.

In rugged or mountainous areas, topographic normalisation is usually recom-

mended in order to remove all topographically induced illumination effects (Teillet

1986, Colby 1991, Meyer et al. 1993). The most common method to correct

148 M. Maggi et al.

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topography effects is the one based on the use of the Minnaert constant k, which

accounts for the vegetation as being non-Lambertian. Results of the methodology

are controversial. Some authors found the Minnaert methodology very efficient

(Colby 1991, Ekstrand 1996, Dorren et al. 2003), some others demonstrated the

methodology is of limited use (Tokola et al. 2001, Bishop et al. 2003), others

concluded that it fails to improve mapping accuracy (Carpenter et al. 1999). In the

framework of this study it was decided not to perform topographic correction and to

take into account effects of topography during the collection of training sets, i.e. for

the same land cover change class, training sets having different exposures were

visually selected. This was possible due to the selection of two images of the same

time, 23 and 27 of July, acquired in similar observation conditions. Indeed the forest

landscape pattern, especially in mountainous regions, is usually highly influenced by

sun angle.

As a final pre-processing step, we applied a differencing algorithm to each of the six

Landsat band pairs. Image differencing is defined by some authors as a change

enhancement technique to be distinguished from change classification algorithms

(Rogan and Chen 2004). The former include, among others, vegetation indices

(Wilson and Sader 2002, Sader et al. 2001, Lyon et al. 1998), principal components

analysis (Lunetta and Elvidge 1998), multitemporal Kauth Thomas transformation

(Rogan et al. 2002, Collins and Woodcock 1996), and change vector analysis (Lambin

and Strahler 1994). We selected the normalised band difference as the basis for further

change detection analysis since it represents an output map of immediate and simple

interpretation. More precisely we applied a standardised version (equation (1))

(Coppin and Bauer 1994), in order to minimise confusion among change values that

are numerically equal, but that are describing different change events:

Normalised difference of band i~bandi{00{bandi{84ð Þbandi{00zbandi{84ð Þ ð1Þ

5.2 Classification scheme

On the basis of knowledge available for the study area, i.e. bibliographic material

(GEO Mediterranee 2001, Laurent 1988), and visual inspection of satellite images,

nine change classes from the year 1984 to the year 2000 were identified:

N growth of shrubs on pastures or meadows;

N settling of conifers (pines and larches) on shrubs;

N increase of larch (or other conifers) canopy cover;

N increase of herbaceous biomass;

N from vegetation to bare soil;

N enlargement of river beds;

N clearings for new roads;

N from spruce forest to meadows (cuttings);

N snow in 1984 and rock in 2000.

Training sets for each of these classes plus the no-change class were identified by

visual analysis of the satellite images. If possible, for each class, training sets on

different exposures were selected. In fact, since topographic correction was not

applied to the images, identical targets occurring in different terrain morphologies

may have different reflectance values.


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A separability analysis based on the Jeffries Matusita distance (Richards 1993)

was then performed on the normalised band difference and some change classes, if

ecologically coherent, were merged to obtain a greater separability.

The number of classes was then reduced by defining the following change classes:

N increase of woody vegetation (including the previous first three change classes);

N increase of herbaceous biomass;

N from vegetation to mineral ground (including the classes from vegetation to

bare soil, enlargement of river beds, clearings for new roads);

N decrease of woody vegetation;

N snow in 1984 and rock in 2000.

This last class has a limited meaning in the context of this study although it proves

useful for global warming studies when assessed systematically over many years.

5.3 Change detection

A hybrid change detection approach combining spectral and spatial information

was developed to identify changes on the normalised band difference.

Initially, training sets were identified to characterise the desired output classes.

Subsequently, pixels corresponding to changes with a high confidence level were

defined as pixels deviating in each band difference by a maximum of 1 standarddeviation from the mean of the training sets. This approach is sometimes referred to

as the box-classification (Richards 1993). The resulting pixels were considered as the

labelled seed pixels. However, since only one label can be considered for each seed

pixel, those coming from overlapping boxes were eliminated. For the no-change

class, seeds were obtained by considering all pixels of the multichannel difference

image which fall around the principal mode of the intensity difference distribution

function. The mode is naturally located near the zero value. By fitting a Gaussian

distribution around the tip of this mode, we found that a reasonable threshold value,for determining no change seed pixels with a high confidence, is about 0.125 (on the

normalised difference values ranging from 21 to 1).

Finally, a mask for the clouds and their shadows, which are only present in the

year 2000, was obtained by first detecting clouds shadows which appear in all

channels with a negative band difference. Clouds were then estimated by identifying

all those pixels having a positive difference and a shadow in their NE direction along

a line segment of 3 km, to avoid possible confusions with other classes. This was

achieved by performing a combination of directional dilations (Soille and Talbot

2001) and morphological reconstruction procedures (Soille 2003). Pixels labelled asclouds and shadows were masked out from further analysis.

To detect change classes, we applied a region growing technique based on theseeded region growing technique proposed by Adams and Bischof (1994). In this

approach, the seeds are progressively grown in the original image, i.e. in the

normalised difference image rather than its gradient, as it would be required by a

watershed-based segmentation (Vincent and Soille 1991). At each iteration, all those

pixels that border the growing regions are examined. Only those pixels having the

smallest Euclidean distance with respect to the seed core pixel of the region they

border are appended to that region. Therefore the proposed region growing process

is no longer controlled by a distance measurement between the pixels neighbouringthe grown region and the grown region itself, as in its original version; it is here

based on distance measurements between the pixels bordering the grown region and

150 M. Maggi et al.

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the corresponding seeds or core pixels. The process proceeds until all image pixels

have been allocated to a growing region. The speed of the growth is inversely

proportional to the Euclidean distance separating the neighbour pixels mapped into

the multispectral feature space (figure 1).

A filter was applied to the final result in order to retain only areas of minimum

262 pixels in size. Smaller areas were considered as errors, since they could be

generated by bad registration between images during pre-processing or by speckle,

which is common to many change detection results (Woodcock et al. 2001).

5.4 Accuracy assessment

In the present work, the evaluation of the map accuracy was first carried out

qualitatively during two visits on the field with the Park’s staff. They gave their

advice on the reliability of the change map according to their direct knowledge of

the present and past situations.

Nevertheless, a quantitative accuracy assessment was also necessary to assess the

robustness of our product. To this aim the available bi-temporal APs coverage was used.

We set up a procedure finalised to select, from the 50 APs acquired in the year

1984, a subset not affected by subjective choice of photograph locations. This was

done also to reduce the effort of the orthorectification exercise. We overlaid a 500 m

cell grid on the satellite-derived map and for each cell we calculated the percentage

of change. Then, on the basis of a graph showing the number of cells against the

Figure 1. Example of how the proposed seeded region growing approach works. First seedsof change and no-change classes are identified (a). Then they are progressively grown in thenormalised difference image. At the first iteration, all the 8 pixels that border each seed areexamined (a). Only those pixels having the smallest Euclidean distance with respect to the seedpixels are appended to a growing region (b). At the following iterations the process isrepeated, and the Euclidean distance of those pixels that border the growing regions iscalculated with respect to the ‘core’ seed pixels. A region can stop growing at one iteration (c)and restart growing at the following iteration (d). The process finishes when all image pixelshave been allocated to a growing region.


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percentage of change, we visually decided four intervals or strata (0–10%, 10–30%,

30–50%, 50–100%) corresponding to changes in cell frequencies (figure 2).

We finally selected for orthorectification only those photographs containing

entire 500 m cells belonging to all the four strata of changes. The 1984 AP data set

was then reduced to 14 elements. The selected colour infrared APs were

orthorectified and resampled to 1 m resolution using a nearest neighbour algorithm.

The mean square error of the rectification was always less than 5 m which is

acceptable for the validation of a 25 m satellite-derived map.

The accuracy of the satellite-derived land cover change map was then tested. Two

different approaches were used to define the sample size for the classified change

and no-change classes.

The no-change class is by far the largest one, since it encompasses over 90% of the

thematic change map. The sample size for its accuracy assessment was decided based

on a binomial distribution. This method is suitable when we want to estimate two

categories, provided they are exhaustive. In our case we merged all the change classes

in one unique class and we obtained a map with two classes: change and no-change.

According to the binomial distribution, if we choose the half-width of the

confidence interval (which we will also refer to as error tolerance) to be 0.05, the

minimum required sample size is equal to 100, if the proportion of the category (p) is

equal to 0.9. We finally selected 105 samples for no change class. For the classified

change classes, Khorram et al. (1999) propose using a disproportionate sampling

design (Kalton and Anderson 1986) which increases the sampling fraction for those

classes in which the rare population is concentrated. In this case the sample size was

calculated based on a procedure originally presented by Tortora (1978) and based

on a multinomial x2 distribution (equation (2)):

n~x21,1{a=kð Þ �

p 1{pð Þa=2ð Þ2

" #ð2Þ

Figure 2. Histogram showing the number of 500 m cells, overlaid on the change map,containing different percentages of change. The two curves represent both the cells coveringthe entire change map and the cells included in the 1984 APs.

152 M. Maggi et al.

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where a denotes the desired confidence interval, p the proportion of the stratum and

k the number of change categories.

For k55, p50.5, a50.1, the total number of samples for the change stratum is

541, i.e. 108 for each change class. A confidence interval equal to 0.1 is large for

those classes having a probability of occurrence smaller than 1%, but the choice was

the result of a compromise between the error tolerance and the cost of the validation

exercise.

In order to distribute the sampling effort among the different change classes

according to the varying likelihoods of change, samples were re-allocated in such a

way that change classes with a higher likelihood of occurrence contain a higher

number of sampling pixels. Dimensions of testing samples are as follows: 154 for

‘Increase of woody vegetation’, 120 for ‘Increase of herbaceous biomass’, 95 for

‘Decrease of woody vegetation’, 120 for ‘From vegetation to mineral ground’ and 50

for ‘Snow–rocks’ class.

A stratified random sampling was then adopted in order to locate the required

number of samples within each change category. Strata corresponded to classified

change classes. Given the registration error in image processing, sample units

consisted of polygons of 262 pixels in size in order to ensure that the sampled plot

truly encompasses the designated sample selection point on the map (Biging et al.

1999).

Once the required samples were located on thematic satellite maps, each polygon

was checked on both the 1984 and 2000 aerial photographs. An error matrix was

finally formulated. However, for a stratified random sample based on the mapped

land cover classes as strata, accuracy parameters should be estimated considering

both the sample and population size of each stratum (Stehman and Czaplewsky

1998). Therefore each entry of the error matrix was weighted according both to the

total number of samples taken within the corresponding land cover class and the

proportion of each class within the map. Finally, the following accuracy statistics

were derived: overall accuracy (the proportion of correctly allocated pixels), kappa

(k) coefficient (the measure of correctly allocated pixels adjusted for chance

agreement) along with both the user’s (the percentage of a class on the thematic map

which really corresponds to that class) and producer’s (how often a class is

recognised as that class) accuracies for each class (Janssen and van der Wel 1994).

The user’s and producer’s accuracies are complementary of, respectively, the

commission and omission errors (Congalton 1991).

6. Results and discussion

According to the map derived from the satellite sensor data, 8.4% of the entire

Park’s territory has experienced land cover changes between 1984 and 2000. The

increase of woody vegetation class is the predominant change class corresponding to

50.5% of the changed areas. The remaining change areas are: ‘From vegetation to

mineral ground’ (17.6%), ‘Increase of herbaceous biomass’ (15.5%), ‘Decrease of

woody vegetation’ (8.4%), and ‘Snow–rock’ (8%) classes.

The estimated increase of woody vegetation (4.2%) over the study period 1984–

2000 is coherent with figures given by IFN for the two departements (districts)

(Alpes-Maritimes and Alpes-de-Haute-Provence) that encompass the Mercantour

National Park. Indeed, according to IFN (http://www.ifn.fr/), between 1984

and 1996, an increase of forest area between 4% and 8% occurred in this part of

France.


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.] At: 20:55 8 February 2007

Visits on the field gave the opportunity to qualitatively evaluate the correctness

of change classes in some sectors of the Park, both to document them from a

physiognomic point of view and to assess their relevance from a Park’s manage-

ment perspective. In particular, the ‘Increase of woody vegetation’ resulted to be the

most correctly classified change class. It corresponded to different situations, such

as:

N increase of shrubby vegetation on herbaceous ground where agricultural or

pastoral activities have ceased;

N increase of tree density, i.e. increase of canopy cover. Especially larches can

grow by 30–40 cm a year, i.e. up to about 6 m in 16 years (1984–2000);

N few cases where new trees have appeared, such as larches and Scots pines on

southern exposed slopes;

N changes in species composition due to natural succession or to a progressive

replacement of larch with fir and spruce. In fact while larch was favoured in the

past in the framework of reforestation programs, the ONF (Office National

des Forets) today tends to favour the replacement of larches with firs.

The ‘Increase of herbaceous biomass’ class encompasses such an increase on the

ground for three main reasons:

N abandonment of pastoral activities;

N different land use (in 2000 meadows were pastured or mowed later than in

1984);

N changes in phenology due to different precipitation regimes in 1984 and 2000.

The ‘From vegetation to mineral ground’ change class was explained on the ground

as resulting from:

N active erosion;

N enlargements of the river bed between 1984 and 2000 due to a flood event

occurred in 1994 and due to the presence of mining activities;

N new urban infrastructures in the valley bottoms or new ski resorts on slopes;

N areas where recent fire-events have occurred and regenerations are not yet

present;

N decreases of herbaceous biomass due to different land uses (in 2000 meadows

were pastured or mowed earlier than in 1984);

N changes in phenology due to different precipitation regimes in 1984 and 2000.

Both the ‘Increase of herbaceous biomass’ and the ‘From vegetation to mineral

ground’ classes were ranked by the Park’s staff as too heterogeneous for

management purposes, since they include both temporary and permanent changes.

Finally, ground observations allowed us to confirm that the ‘Decrease of woody

vegetation’ class corresponded to areas where cuttings or fires had occurred and

regeneration was already present in 2000. This class is the one presenting the highest

percentage of critical cases including a high percentage of overestimation errors

located above the timberline. For this reason, all pixels belonging to this class and

located above the timberline were masked out.

Availability of ancillary data, such as contours and dates of fires, could be of

some help to solve part of the aforementioned heterogeneity problem. Images

acquired at the end of August, when all meadows are pastured, could help to

separate permanent land cover changes from temporary land use changes. Finally,

154 M. Maggi et al.

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precipitation data and snow duration could be useful to identify phenological

changes. In fact, even if images have been acquired at the same time of the year,

there could be important variations in rainfall from one year to another and soil

moisture and vegetation can be different. These differences can be a significant cause

of error in change detection studies (Mas 1999, Gallego 2004).

Observations made on the field are coherent with results from the quantitative

validation carried out using APs. The map’s overall accuracy is 90.1% (table 1)

which is a very good result. However, the high overall accuracy’s value is influenced

by the high number of correctly classified pixels in the ‘No change’ class which

represents almost 92% of the entire map. This is reflected by the low k coefficient’s

value (0.46).

The highest user’s and producer’s accuracy pertain to the ‘Snow–rock’ class

(100%) because of its high spectral separability with respect to all the others change

classes. The ‘Increase of woody vegetation’ is the vegetation change class with the

highest user’s and producer’s accuracies of, respectively, 62.3% and 70% (figure 3)

This result was considered of importance since we were mostly interested in looking

at changes related to the increase of forest.

The ‘Increase of woody vegetation’ is also the class with the second highest kcoefficient’s value (0.61), after the snow–rock class (1.0).

The lowest user’s accuracy values are those of ‘From vegetation to mineral

ground’ (50.8%), ‘Increase of herbaceous biomass’ (25.8%), and ‘Decrease of woody

vegetation’ (22.9%) classes, which have the highest commission errors. These are due

to pixels assigned by the classifier to the above-mentioned classes and actually

corresponding to the ‘No change’ class (figure 4).

Table 1. Error matrix derived using the APs as reference data.

CLASSIF.

REFERENCE

Tot.

Useracc.(%)

Comm.error(%)

Nochange

Incr.woody

veg.

Veg. tomin.

ground

Decr.woody

veg.

Incr.herb.

biomassSnow–rock

No change 85.54 0.87 2.62 0.87 1.75 0.00 91.64 93.3 6.7

Incr. woodyveg.

1.45 2.63 0.03 0.03 0.08 0.00 4.22 62.3 37.7

Veg. to min.ground

0.59 0.01 0.75 0.07 0.05 0.00 1.47 50.8 49.2

Decr. woodyveg.

0.52 0.01 0.01 0.16 0.00 0.00 0.70 22.9 77.1

Incr. herb.biomass

0.72 0.23 0.01 0.00 0.33 0.00 1.29 25.8 74.2

Snow–rock 0.00 0.00 0.0 0.00 0.0 0.67 0.67 100 0

Totals88.82 3.76 3.41 1.14 2.21 0.67

Prod. acc. (%)96.3 70.0 21.9 14.1 15.1 100

Omission error (%)3.7 30.0 78.1 85.9 84.9 0

k 1 k 2 k 3 k 4 k 5 k 6 Overall acc. 90.1%0.4 0.61 0.49 0.22 0.24 1.00 k 0.46


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The ‘Increase of herbaceous biomass’ was also partly mislabelled as increase of woody

vegetation in the reference data, due to the spectral similarity of these two classes.

Producer’s accuracy shows the same behaviour as the user’s accuracy. The worse

results are those of the ‘From vegetation to mineral ground’ (21.9%), ‘Increase ofherbaceous biomass’ (15.1%) and ‘Decrease of woody vegetation’ (14.1%).

Therefore these classes have high omission errors.

(a) (b)

(c) (d)

(e) ( f )

Figure 3. Example of correct estimation of the class ‘Increase of woody vegetation’.Changed pixels estimated using satellite data have grey tones in (a). The same area has beenlocalised also on the normalised difference image (b), the 1984 AP (c), the 1984 TM Landsatimage (d), the orthophoto of year 2000 (e), and the 2000 ETM Landsat image (f) (scale1 : 3000).

156 M. Maggi et al.

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In order to verify the contribution to the observed errors of thresholds applied

within the classification process, we decided to run the region growing algorithm

selecting as seeds all those pixels deviating in each band difference by a maximum of

0.5, 0.75 and 1.5 times the standard deviation from the mean of the training sets.

Error matrices were generated for each classification process. However, obtained

overall accuracies and overall k coefficients were lower, proving that a threshold of 1

standard deviation gives the best results and should not be responsible for the

observed errors (table 2).

Errors can be explained as a function of the moderate spectral separability

between the ‘No change’ and the poorly accurate change classes. Indeed the Jeffrey–

(a) (b)

(c) (d)

(e) ( f )

Figure 4. The region growing algorithm in some cases tends to overestimate changed areas.The example refers to the ‘From vegetation to mineral ground’ class. Changed pixelsestimated using satellite data have grey tones in (a). Plates from (b) to (f) represent the samearea as it appears, respectively, on the normalised difference image (b), the 1984 AP (c), the1984 TM Landsat image (d), the orthophoto acquired in 2000 (e), and the 2000 ETM Landsatimage (f) (scale 1 : 5000).


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Matusita distance varies from 1.61 for the ‘No change’–‘From vegetation to mineral

ground’ pair, to 1.88 for the ‘No change’–‘Increase of herbaceous biomass’ pair.

An additional cause of errors in the change map is represented by the temporal

shift between satellite images and aerial photographs of the same year. Therefore

some areas which have been classified on the map as an increase of herbaceous

biomass are assigned to the ‘No change’ class in the error matrix since aerial

photographs were probably acquired when hay was already mowed, i.e. later in the

season. The reverse situation contributes to omission errors, as shown in figure 5.

Examining the spatial pattern of errors we also discovered that a cause of errors

may be due to a residual mis-registration at the local level resulting in distinctive

areas of changes with an elongated shape. These areas are mostly localised in the

south-eastern part of the Park probably due to the acknowledged poorer

registration of images in this area. This is a problem inherent to any change

detection methodology which somewhat degrades area assessment of change events

(Coppin and Bauer 1994, Verbyla and Boles 2000).

As a consequence of the above discussed accuracy assessment, we merged the

‘From vegetation to mineral ground’, ‘Increase of herbaceous biomass’, and

‘Decrease of woody vegetation’ classes into one unique class named ‘Other

vegetation changes’. In this case we notice that the change map’s accuracy and the kcoefficient are almost constant (table 3). Moreover the high commission (62.5%) and

omission (80.5%) errors of the new change class still confirm the confusion between

the merged classes and all other classes.

We also assessed the accuracy of the ‘Increase of woody vegetation’ class against

all the other classes that were merged in one unique class (table 4). The latter had

low commission and omission errors due to the influence of the ‘No change’ and

‘Snow–rock’ classes. The overall accuracy slightly increases (93.5%) and the kcoefficient is almost constant (0.42); however, the omission error of the ‘Increase of

woody vegetation’ class increases up to 35%.

7. Conclusions

Alpine sites are affected by a general increase of woody vegetation due to a decline

of both agricultural and pastoral activities. This issue is of great concern to the

Park’s managers for its consequences on biodiversity, cultural landscapes, and

tourism. Moreover, mapping of forest regrowth is critical to understand the roles of

terrestrial ecosystems in global carbon budgets and global climate change (Sader

et al. 1989, Foody et al. 1996, Song et al. 2002). Therefore monitoring of the forest

cover increase, especially in marginal areas such as mountain regions, is deemed

necessary. The aim of this study was to provide managers of protected areas with a

methodology to estimate woody vegetation increase from Landsat imagery. As a

pilot area we selected the National Park of Mercantour, located in the French Alps.

Table 2. Overall accuracies and k coefficients relative to different thresholds applied withinthe classification process.

Applied threshold Overall accuracy (%) Overall k coefficient

0.5 60.25 0.10.75 68.58 0.171 90.1 0.461.5 62.41 0.24

158 M. Maggi et al.

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Identification of forest regrowth or secondary succession using remote sensing is a

challenging task since it involves detecting more subtle changes in signal than does

mapping abrupt changes. Our work provided a contribution on mapping woody

vegetation increase and to do so, proposed a new classification and validation

scheme. Two Landsat images acquired end of July 1984 and 2000 were used for

detecting changes. The change detection classification algorithm was based on a

supervised hybrid approach applied on a normalised band difference; hybrid in the

sense it combines both spectral and spatial information. This algorithm was applied

for the first time on natural resources and used on multitemporal and multispectral

(a) (b)

(c) (d)

(e) ( f )

Figure 5. Example of underestimation of the ‘From vegetation to mineral ground’ class (a)due to a temporal shift between satellite data and APs of the same year (scale 1 : 3000). Whitepixels in (a) correspond to estimated ‘No change’ pixels on the satellite-derived map. Thesame area is also represented as it appears on the normalized difference image (b), the 1984AP (c), the 1984 TM Landsat image (d), the orthophoto of year 2000 (e), and the 2000 ETMLandsat image (f).


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images. First, seeds were identified by a box classification and subsequently a region

growing algorithm was applied. The region growing process as defined in its original

version (Adams and Bischof 1994) was modified for the current study: it is no longer

controlled by a distance measurement between the pixels neighbouring the grown

region and the grown region itself, it is here based on distance measurements

between the pixels bordering the grown region and the corresponding seeds or core

pixels. The methodology is strictly dependent on training sets so it requires a good

knowledge of processes going on in the study area and the location of some of them.

It also circumvents limitations which are intrinsic to classification procedures based

only on spectral information: it produces objects, more easily interpreted by people

which are not familiar with remote sensing derived products. Furthermore, since the

Table 3. Error matrix resulting from the merging of the ‘From vegetation to mineral ground’,‘Increase of herbaceous biomass’, and ‘Decrease of woody vegetation’ classes in one unique

class named ‘Other vegetation change classes’.

CLASSIFICATION

REFERENCE

TotalsUser acc.

(%)

Comm.error(%)

Nochange

Incr.woody

veg.Other veg.

changesSnow–rock

No change 85.54 0.87 5.24 0.00 91.64 93.33 6.67Incr. woody veg. 1.45 2.63 0.14 0.00 4.22 62.34 37.66Other veg. changes 1.92 0.25 1.30 0.00 3.47 37.5 62.5Snow–rock 0.00 0.00 0.00 0.67 0.67 100 0

Totals88.91 3.75 6.68 0.67

Prod. acc. (%)96.21 70.13 19.49 100.00

Omission error (%)3.79 29.87 80.51 0.00

k 1 k 2 k 3 k 4 Overall acc. 90.13%0.40 0.61 0.33 1.00 k 0.46

Table 4. Accuracy of the ‘Increase of woody vegetation’ class against ‘All other classes’.

CLASSIFICATION

REFERENCE

TotalsUser acc.

(%)Comm. error

(%)All other

classesIncr woody

veg.

All other classes 90.91 4.88 95.78 94.91 5.09Incr. woody veg. 1.59 2.63 4.22 62.34 37.66

Totals92.50 7.51

Prod. acc. (%)98.28 35.04

Omission error (%)1.72 64.96

k 1 k 2 Overall acc. 93.530.32 0.59 k 0.42

160 M. Maggi et al.

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methodology is based on a combined spectral–temporal analysis, it does not require

deriving the land cover maps for two dates, reducing in this way the classification

efforts.

Although the study focus was on increase of woody vegetation, the classification

nomenclature scheme was based on four vegetation change classes plus the ‘No

change’ and ‘Snow–rock’ classes. We adopted coarse change classes in terms of the

level of detail extracted from the imagery. Both the visits on the field and the APs

demonstrated the heterogeneous nature of change classes. This characteristic can be

explained if we consider that all change classes mostly correspond to modifications

of land cover rather than a conversion of land cover type (Riebsame et al. 1994,

Skole 1994, Foody and Boyd 1999). However, the ‘Increase of herbaceous biomass’

and the ‘From vegetation to mineral ground’ classes were considered by the Park’s

staff as too heterogeneous for management purposes, since they include both

temporary and permanent changes. To distinguish the two types of changes, satellite

sensor data should be integrated with ancillary information.

To assess the accuracy of the change map we used a bi-temporal aerial

photographic coverage and we adopted a sampling scheme specifically conceived for

change detection products which increases the sampling fraction for rare classes.

Error matrices and accuracy indices were generated which allowed us to assess

commission and omission errors of our change maps. Since we adopted a combined

spectral–temporal change detection approach, dimensions of our error matrices

were smaller than those of error matrices generated for post-classification change

detection products. Indeed the latter have a dimension of n26n2, where n is the

number of classes of single date classifications at time 1 and time 2. The smaller

number of change classes in our approach greatly reduces the efforts of the

validation exercise. However, there are some drawbacks which are implicit in using

aerial photographs for the accuracy assessment of our satellite derived map. First,

their finer resolution with respect to the satellite sensor data implies a subjective

generalisation process of the same target area during visual interpretation. Second,

visual analysis of areas selected for the accuracy assessment which fall at the

boundary of two classes or which experienced slight changes, imply a subjective

interpretation. Finally, as we verified during our accuracy assessment, the temporal

shift between satellite images and aerial photographs of the same year can generate

omission or commission errors within agricultural land use classes.

The accuracy assessment shows that the ‘Increase of woody vegetation’ is the

vegetation change class which has been better estimated and since we were mostly

interested in looking to changes related to the increase of forests we consider this

result of importance. However, confusion between the ‘No change’ and the other

vegetation change classes was reported. Errors are due to different reasons, namely:

non-sufficient spectral separability, temporal shift between satellite images and

validation data set, and residual mis-registration. Since the change detection

methodology is supervised, we expect that these problems can be greatly reduced in

future work by improving the training extraction. To render the latter more

objective and robust, an automatic identification of training sets on APs and their

classification according to ranges of woody vegetation increase could be investigated

in the future. It should also be interesting to assess the influence of a topographic

correction of images on the results.

To conclude, we believe that the change map derived by applying the hybrid

approach to Landsat images can be used to stratify the protected area, thus


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facilitating the identification of critical sites to be accurately visited on the ground.

The proposed methodology is suitable for the analysis of medium resolution

imagery where the vegetation structure is not visible. For higher spatial resolutions,

such as those of SPOT5 multispectral, the method could be adapted by generating

texture channels so that difference in texture will aid the change detection. For very

high-resolution imagery such as IKONOS, the method is not applicable since the

shape of trees, the effects of shadow and view angle disturb texture measurements

too much.

The methodological approach, including the classifier and the validation scheme,

was tested for the issue of increase of woody vegetation in mountain regions; it is

currently applied to other protected areas, located in the Italian Alps, characterised

by landscape processes similar to the ones observed in the National Park of

Mercantour. The method is therefore directly relevant to the European Alpine

biogeographic region in which altitude-related conditions dominate the development

of vegetation and where land abandonment is a general trend. As future

development, the application of the method in flat areas will be straightforward

since technical problems related to topography effects will not be encountered.

However, in flat areas, anthropogenic forces (infrastructure building, agricultural

and silvicultural practices) influence in a different way woody vegetation dynamics.

Overall, we believe that such an approach is worth testing for monitoring vegetation

dynamics in European protected areas and outside Europe in general for different

types of vegetation dynamics.

Finally, this work is of interest for the biodiversity community, since it addresses

vegetation dynamics and focuses at the ecosystem level. It does not address directly

the habitat and species levels. The definition of habitat relies more on phytosocio-

logical/floristic criteria and their identification cannot be directly achieved using Earth

Observation or aerial photographs (Estreguil et al. 2003). Vegetation is, however,

considered the best indicator of environmental conditions of a habitat.

Acknowledgments

This study was supported by a grant for training through research of the European

Commission.

The Landsat TM image of 23/07/1984 was kindly provided by Jean Bernard-

Brunet of CEMAGREF-Grenoble; the digital orthophotos and the DEM by the

National Park of Mercantour. The Landsat ETM + image is an IMAGE2000 (EC)

product (http://image2000.jrc.it/). All the staff of the National Park of Mercantour,

who contributed to the field validation, are gratefully acknowledged. The authors

also wish to thank Frederic Achard, Jacek Kozak, Michel Deshayes, and Samuel

Djavidnia for their comments and suggestions.

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Woody vegetation increase in Alpine areas: a proposal for a classification and validation scheme

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