been used widely to map land cover, relatively little work has been ... The Landscape Classification of Wales (Welsh. Office ... by the Welsh. Office study, going beyond the description of land- ...... agricultural land in England and Wales. As with.
Landscape Ecology vol. 7 no. 4 pp 253-274 (1992) SPB Academic Publishing bv, The Hague
The use of remotely-sensed satellite imagery for landscape classification in Wales (U.K.) Roy H. Haines-Young Department of Geography, University of Nottingham, Nottingham, NG7 2RD, U.K. Keywords: landscape classification, landscape survey, remote sensing
Abstract Remotely-sensed satellite data from Landsat TM and MSS were processed digitally to make landscape classifications of three study areas of south east Wales. The classifications were constructed by classifying major variations in land cover mosaics within the areas, and using these data to group the 1 km x 1 km cells of the National Grid into landscape classes according to the combination of cover types found within them. The TWINSPAN algorithm, which is a polythetic, divisive classification method, was used as the basis of the study. The results showed that while satellite imagery could only be used to extract information about land cover, the close association betwen landscape, land cover and terrain meant that the major physical divisions in the study area could also be detected in the landscape classification. The landscape types recognised in the study were found to be consistent with those indicated in other independent data which relate to the areas. These data included the ITE Land Classes for Great Britain, and the Agricultural (June) Census statistics for England and Wales. The approach to landscape classification described allows landscape classifications to be made rapidly. These classifications can provide a sampling frameworks for landscape survey in areas where basic map data are lacking or resources for field survey are limited. The landscape classifications can also assist in making landscape evaluations since they allow different landscape types to be compared in respect of such properties such as their typicalness, rarity, naturalness and position on a geographical or ecological gradient.
1. Introduction
The recognition of landscape units, in which there is a distinctive association of land cover and terrain, is a fundamental task in landscape ecology. In making such analyses a variety of data sources have been employed. These include map, air photo and field information. Increasingly, remotely-sensed satellite imagery have also been used, the particular advantages of these data being their repeated and large scale coverage and their digital format. They
are easy to integrate with other data sources for planning purposes and offer the prospect of monitoring landscape change. The use of remotely-sensed imagery in landscape analysis is not, however, without its problems. Although digital image processing techniques have been used widely to map land cover, relatively little work has been concerned with the recognition of ‘higher order’ patterns in the land cover mosaic which constitute different landscape types. Algorithms for the automatic regionalisation of imagery
254
Fig. 1. Study areas in south east Wales.
and the extraction of terrain information are more complex than those used for land cover classification and much less widely available. As a result, for the purposes of landscape analysis, imagery have mostly been interpreted by manual methods similar to those used for the analysis of air photography. The potential advantages arising out of the digital format of these data have often gone unrealized. The aim of the current study is, therefore, to examine how satellite imagery can be processed digitally in order to characterise landscapes, and to test how the results derived from this single data source compare with those obtained by other, more traditional methods. The relevance of this study for landscape ecology lies in the potential use of the methods examined for landscape survey and evaluation. The landscapes selected for study occur within the United Kingdom, in south east Wales (Fig. 1). They were chosen to include a range of the landscape types found within the general area and to provide an insight into mapping the landscape resources in this part of Wales.
2. Previous work
The classification of landscapes involves the grouping together of areas having the same or similar characteristics. Despite the simplicity of the idea, no single approach to the problem of landscape classification exists. The combination of different classification methods, choice of variables and different data sources has meant that a great diversity of landscape classifications exist. Part of this variety can be illustrated by reference to three studies which have attempted to develop regional or national classifications in the UK. The Landscape Classification of Wales (Welsh Office, 1980) was an attempt to describe the scenic resource in terms of a set of features describing the physical characteristics of the countryside and its land cover. These characteristics included relative elevation, slope break and field, tree and woodland density. The major data sources were maps and aerial photographs. Overlays showing each variable on an ordinal scale were combined manually in order to identify the various landscape units. The Welsh Office study was typical of many numerical attempts to classify landscapes which were
255
undertaken in the 1970’s (cf. Robinson et al. 1976; Penning-Rowsell 1973, 1980; Vink 1983). Its major limitation was that it was too general at the local scale for specific applications and difficult to test at the regional or national one, since the units identified were so broad and difficult to recognise in the field. These limitations largely arose from the difficulty of coping with the large volumes of data required in such classifications by the manual methods then available. The landscape assessment technique proposed by the UK Countryside Commission (Countryside Commission 1987) contrasts markedly with the ‘mechanistic’ approaches typified by the Welsh Office study, going beyond the description of landscape to an evaluation of its quality. The method combines the ‘objective’ analysis of landscape, in terms of its constituent elements, with the more ‘subjective’ response of the viewer. In addition to collecting data on the physical elements of landscape, assessors are encouraged to record their impressions on a whole range of criteria including ‘scale’, ‘variety’, ‘texture’, ‘stimulus’ and ‘pleasure’. An example of the application of these methods is provided by the Mid- Wales Uplands Landscape Assessment (Land Use Consultants 1986). The Countryside Commission methodology is rich in the range of criteria allowed in the landscape description, but much less specific about how the analysis is performed than other methods. Indeed, it is only intended as a general method of working which will vary with the purposes and scale of any particular project. As a result, it does not address the problem of integrating our understanding of landscape pattern and process at different scales, nor does it facilitate the easy integration of landscape information with other sources of environmental data. The Land Class System of Great Britain developed by the UK Institute of Terrestrial Ecology (ITE) (Bunce et al. 1981a, 1981b, 1983) represents the continuation of the ‘statistical approach’ to landscape classification. Although initially intended as a framework for ecological and land use survey, the land classes defined were found to capture much information about landscape character
(Benefield and Bunce, 1982). The methodology employed is interesting because, although it is more tightly specified than that of the Countryside Commission, it is flexible enough to allow subjective factors to be included. The ITE system uses the 1 km x 1 km cells of the UK National Grid as its basic spatial unit. When setting up the scheme, it was impractical to survey all of them nationally and so the classification was based on a stratified sample of the 1228 cells at each 15 km x 15 km grid intersection. The characteristics of each sample 1 km x 1 km square on 282 environmental variables were recorded from maps and Indicator Species Analysis was used to make a multivariate classification (Hill et al. 1975). Thirty-two land classes were recognised at the national scale, each with their own unique characteristics of land use, soils, climate and ecology. The detailed characteristics of these classes were established by visiting a subset of each type in the field. The ITE study identified a set of about 70 key ‘indicator attributes’ which both defined the classes and enabled unknown 1 k m x 1 km cells to be assigned to the appropriate national land class (Barr and Dias 1989). The key was used, for example, to extend the original sample t o 6028, and has recently been augmented with a method based on a ‘reduced attribute set’ of only seven variables, which can assign all of the 1 km x 1 km cells in the UK to a national land classes (Institute of Terrestrial Ecology 1991). The ITE system is both a methodology and an application. In seeking to generalise the approach for application elsewhere, its major practical limitation is that the basic data are obtained manually from maps and field investigation. Given problems of data capture by these traditional methods, the system can only usually be implemented on a sample basis at large scales. Moreover, the fixed nature of the basic data unit hinders changes in the scale and reduces choice of criteria. These latter characteristics might be important if a classification is to be flexible enough to meet a range of user needs. The major drawback of the ITE scheme in the UK has been that the national classes are too general to be of use at local scales. In the UK the ITE Land Class System has,
256 despite these practical limitations, proved extremely versatile, supporting a number of important applications (see section 7). Given the recent advances in remote sensing and computer-based processing of digital environmental data, it should be possible to overcome some of the problems of data capture and build an even more flexible system which could be applied elsewhere. The study described here is intended as a contribution to this end.
3. Study areas and methodology The areas selected for study (Fig. 1) were diverse in their landscape characteristics. The West Glamorgan area was a 40 km x 20 km block of countryside. In the east of the area the uplands reach an altitude of 481 m and are dominated by Sitka and Norway spruce plantations (Picea sitchensis and Picea albies). Moorlands dominated by Nardus stricta, Molinia caerulea or Vaccinium myrtillus are common in the north-west of the area and neighbouring the woodlands in the east. Salt marshes and sand dunes occur along much of the coast. A pastoral landscape with small fields, hedges, small blocks of broadleaf woodland and bracken (Pteridium aquilinium) is found in the low-lying lands to the west of the study area. Urban and industrial landscapes are associated with the towns of Swansea, Llanelli and industrialized valleys of the north-east. The Black Mountains study area, by contrast, was a 20 km x 20 km block on the borders of the counties of Powys, Gwent and Hereford and Worcestershire (Fig. 1). The uplands in this area reach 8 11 m and are mostly covered by open moorland communities in which Nardus stricta, Molinia caerulea, Eroiphorum vaginatum and Vaccinium myrtillus. Heather moor, dominated by Calluna vulgaris is common in the south-east of the study area and bracken (Pteridium aquilinum) is widespread on steep slopes. Coniferous woodland is largely restricted to a central valley running through the upland massif. Broadleaf and mixed woodland occur on the lower lying land and on the fringes of the moorland areas. In the west is an area of mixed agriculture around Llangorse Lake. Grass, wheat, barley, oats and maize are the main
crops of the area. Settlement is mainly in the form of villages and small market towns. Finally, the Wye Valley study area was a 25 km x 20 km block of countryside along the borders of Gwent and Gloucestershire (Fig. 1). It includes the section of the Wye Valley between Monmouth and the Severn Estuary and the lands to the west as far as a line joining Raglan in the north and Magor in the south. The landscapes consist of combinations of pastoral and arable farmland together with extensive tracts of broadleaf and coniferous woodland. The approach to landscape classification used for these study areas involved a two stage process. First, the classification of land cover was conducted using remotely-sensed imagery and the most efficient and accurate algorithm available. Second, the analysis of patterns within the mosaic was carried out in order to classify landscape types. The study used the 1 km x 1 km cells of the UK National Grid as the basis of the analysis, although unlike the ITE scheme, this decision did not preclude the investigation of patterns at other scales. In developing the use of remotely-sensed data for landscape classification, it was recognised that despite their promise for land cover mapping, the extent to which they have been used successfully has been limited. Allen (1986), for example, suggests that only land cover categories equivalent to Anderson’s level 1 can be reliably detected (see Anderson et al. 1976). Anderson’s scheme categorises land cover hierarchically. His level 1 is the most general and in the UK would correspond to broad cover types such as forest or urban land. Allen’s comments should not be taken to suggest that particular cover types in Anderson’s scheme cannot be recognised reliably at more detailed levels, but that in attempting to produce general land cover maps across a range of categories, errors of classification may vary unacceptably between classes so that interpretation may be difficult or misleading. The cover types considered appropriate for the classification of landscapes in this study are shown in Table 1. The minimum information considered necessary was an estimate of the relative abundance of such cover types in each grid cell. A four-point ordinal scale (Table 2) was used for this purpose.
257 Table 3. Imagery used in the study
Table I . Target cover types used 1.
2. 3. 4.
5. 6. 7.
8. 9. 10. 11.
Woodland - broadleaves dominant Woodland - conifer dominant Bracken and scrub Rough grazing (Nardus stricta, Molinia caerulea, Eroiphorum vagenatum or Vaccinium myrtillus dominant) Heater moor (Calluna vulgaris dominant) Improved pasture Arable Open water Marshland and exposed peat Bare rock, quarries and mineral workings Settlement
Table 2. Ordinal scale used for land cover assessment 070
cover
0-5 6-15 16-35 > 35
Path
Row
Data
Type
Area
203 204 204 204 203
024 024 024 024 024
28/4/77 25/4/84 22/7/84 22/1/84 26/4/84
MSS MSS MSS TM TM
SE.Wales SW .Wales SW.Wales SW.Wales SE .Wales
10 km x 10 km squares in Britain. The algorithm is a development of Indicator Species Analysis (Hill et al. 1975) which itself has formed the basis a number of classifications of geographical data (Bunce and Smith 1978; Bunce et al. 1983; Ball and Williams 1977; Ball et al. 1981 and Smith 1982).
score 1 2 3
Such a scale is similar to those used widely in ecology (Kershaw 1985) where they are commonly employed to minimise the errors in the visual estimation of plant cover. The analogy of the method with those of plant ecology is useful because literature of plant ecology is rich in techniques for the analysis of ordinal scale data. In this study, once the classification of land cover had been made and the estimates of land cover in each cell had been obtained, landscape classifications were constructed using the TWINSPAN algorithm. TWINSPAN (Hill 1979; Gauch 1985; Kershaw 1985), or Two-way INdicator SPecies ANalysis, is a polythetic divisive method of classification developed for the analysis of plant cover data. It was designed to process ordinal scale data and produce a hierarchical classification. Although the TWINSPAN algorithm was designed for the analysis of vegetation, it is applicable to the study of a wide range of spatial and nonspatial phenomena. Moss (1985) for example has used TWINSPAN to analyze physiographic, climatic, geological and locational parameters of 282
3. Results and discussion 3.1. Land cover classifcation
Four Landsat TM and two Landsat MSS images were used for the study (Table 3). Field work was carried out in the summers of 1985 and 1986 to generate a set of training and test data for the purposes of land cover classification. The accuracy of field data was also assessed for the Black Mountains using high quality colour air photography flown in April 1984 at a scale of 1:25,000. Between 10 and 30 training sites were identified for each cover category in each study area. Sites covered an area on the ground equivalent to between 4 and 25 image pixels. On the basis of the results of the preliminary study, land cover classifications were made using the maximum likelihood algorithm with equal prior probabilities described by Mather (1985). Where available all seven bands of the spring (April) TM imagery were used. The results for each of these study areas are described below.
3.1.I . West Glamorgan Only July 1984 TM imagery were available for the entire study area in southeast Wales. When using these data, it became clear that the imagery did not allow the accurate separation of improved pasture
258 Table 4 . Classification accuracies obtained using the maximum likelihood method for the three study areas Study area Cover type
1
Broadleaved Coniferous Bracken Rough grazing Heather Improved pasture Arable Open water Marsh Bare ground Settlement
64 90 59 69 89 78 91 87 66 93
Mean
81
-
qo accuracy 2
71 97 75 92 79 99 86 86
3 71 61 88 -
97 77 99
-
-
-
88
70 92
88
86
Key: 1. West Glamorgan; 2. Black Mountains; 3. Wye Valley
and rough grazing because of their spectral similarity at this time of year. To overcome this problem the July TM imagery were combined with Landsat-4 MSS data for April 1984, by replacing band 6 of the TM scene with MSS band 4. The latter was geometrically corrected to the same scale (25 m) as the processed July TM image. An assessment of the accuracy of classification was made using the proportion of pixels from the test areas identified in the field which were assigned correctly to their known cover class. The results analyzed by means of a confusion matrix, the principal diagonal of which is presented in Table 4. All of the target cover types except arable occurred widely in the area. The overall classification accuracy achieved was 80.8%, although accuracies for individual cover types ranged from 59.8% for the bracken and scrub to 96.9% for water. These results are discussed in more detail below. 3.1.2. Black mountains April TM imagery was available for the whole of the study area so the classification of land cover was more straight-forward than that for West Glamorgan. Of the eleven target cover types all were present in the study area except coastal marsh and bare rock. The most common cover categories occurring in
the area were improved pasture and rough grazing, and these were found to be relatively easy to separate. There was, however, overlap of the broadleaf woodland, heather and urban cover types, which all have relatively low reflectance on all wave bands. Rough grazing on shaded northfacing slopes also has a low reflectance and, as a result, there was an overlap of the cluster for this category as well. The low sun angle and associated shadow effects are drawbacks of the April imagery and may have lead to misclassification of some land-cover types. In making the classification, a separate ‘shaded north-facing rough grazing’ category was used and combined with the main rough grazing class for the final land cover assessment. The leading diagonal of the confusion matrix obtained is shown in Table 4. An overall accuracy of 87.8% was obtained. 3.1.3. Wye Valley
The analysis of this study was straightforward since it occurred on the same TM image as the Black Mountains (Fig. 1). Eight of the eleven target cover types were found in the area. An overall classification accuracy of 86.3% was obtained (Table 4).
3.2. Assessment of classification accuracy
In evaluating the results of the land cover classification, it is important to consider how the cover types are confused with each other. The major misclassification errors occurred between the improved and poor pasture categories. Because the two are often difficult to distinguish unambiguously in the field, the lower classification accuracy obtained may result from problems of definition in the training and test data. However, as classification accuracy varied among study areas according to time of data capture, some of the problem must lie with the nature of the image data themselves. For example, the summer TM imagery used for West Glamorgan did not always as good discrimination between pasture types as did spring data. Problems of classification also arose within the woodland classes. In the Wye Valley area, for example, the distinction between broadleaf and coni-
259 Table 5. Agreement of area estimates obtained by different methods for the three study areas
Cover type
la
Broadleaved Coniferous Bracken Rough grazing Heather Improved pasture Arable Open water Marsh Bare ground Settlement
65 79
Mean
55
46 93 52 -
94 80 89 12 73
Study areas ('70agreement) 2a 2b 2c 3a
64 90 68 62 89 88 82 100
95 61 59 I0 80 14 100
-
-
-
-
-
-
-
85
I1
61 93
72
15
92
85 85 82 91 100
-
81 63 91
98
98
86
16
82
80
86
80
Key: 1. West Glamorgan; 2. Black Mountains. 3. Wye Valley. Subscripts: a. remotely-sensed data vs field survey; b. remotelysensed data vs air photography; c. field survey vs air photography; - = cover type not present.
fer seems more difficult to make than for the other study areas. The difficulty arises because no mixed woodland class was used in the analysis. As desirable as such a class might be, in practice such a group proved difficult to introduce. It was impossible to find woodland blocks with a consistent mix of species so as to generate sufficient training and test data. Instead, the woodlands were assigned to broadleaf or conifer according to the dominant species and so the classes may merely identify opposite ends of a spectrum of types. Although confusion matrices are useful for making an initial assessment of the performance classification algorithms and the separability of cover classes, the conclusions about accuracy must generally be qualified. It is important to note, for example, that it does not follow that the levels of accuracy obtained for the test areas will carry over to the classified image as a whole. Accuracy will depend on the relative proportions of the cover types present in an area and the classification accuracies achieved for each of them. As the proportions of the different cover types in each of the study areas are unknown, a realistic assessment of cover classification is difficult to make. As an additional check on the accuracy of cover
classification made using remotely sensed imagery, the output was compared with data derived from field mapping and air photo analysis. Approximately 12% of the 1 km x 1 km grid squares occurring within each of the study areas were used in the analysis. The grid squares were selected at random and mapped in the field during the summer of 1985 and 1986. The field maps were used t o assess the area of each of the target cover types occurring within them and to assign each cover class an ordinal score. Table 5 shows for each cover type the proportions of the sample grid squares which have been allocated to the same ordinal class by the various techniques. When considering the results shown in Table 5 , it became clear that the field mapping was not itself free from error. The boundaries of some cover types were often difficult t o map accurately, and the distinction between some cover classes, particularly the pasture types, was often difficult to make. Thus the low agreement shown for such classes in Table 5 may not simply reflect poor performance of the image classification routine. As a further check of accuracy, estimates of cover in the sampled squares was also compared with estimates obtained using air photography (Table 5). The Black Mountains data indicate that, although there is a higher level of agreement between field survey and the analysis of the aerial photography than between any other pair of techniques, the superiority of field- or photo-mapping over mapping using remotely-sensed imagery is a small one.
4. Landscape classification
In order to make a classification of the landscapes within the study areas, the classified images were first geometrically corrected using a nearest neighbour algorithm so that they could be registered to the UK Ordnance Survey National Grid. The 1 km x 1 km cells of the National Grid were then superimposed onto the images and the area of each cover type in each cell was recorded. These data were used to make a classification of landscape with the TWINSPAN routine. The cells were assinned to
260
w Class 1
t Class 2
6-73
I
I
Class 4
Class 5
Class 9
Class 8
Class 16 Conifer dominated
Class 17 Conifer and pasture
Class 10
Class 18 Pasture and mixed woodland
Class 19 Pasture and conifer
Class 11 Urban coastal with woodland
Class 20 Pasture dominated
Class 12
Class 21 Pasture lbroadleaf woodland mix
Class 13 Coastal urban
Class 24 Urban fringe
Class 25 Coastal urban
Fig. 2. TWINSPAN classification of West Glamorgan study area.
% 100
5fi 0
II
Frequency of each
40 104 22
61 158 182 6 7 61 37 29 c landscape class
Settlement
nBare rock EEj
Open water Marsh Arable
0Pasture
n Heath
Rough pasture Scrub and bracken
El Conifer
--
Broadleaf
7 25 13 11 24 20 21 16 17 18 19 Class number Coastal Urban Upland Lowland
Landscape class
Fig. 3. Composition of landscape classes in West Glamorgan study area.
I
26 1 different landscape classes according to the varying combinations of land cover occurring within them. The TWINSPAN program which was used for the classification of landscapes was that provided as part of the VESPAN package (Mallock 1988). It formed the core of a suite of programs developed for this study which provided facilities for the preparation and input of the land cover data into TWINSPAN and the analysis and display of results. 4. I . West Glamorgan The TWINSPAN classification of the landscapes of the study area is shown in Fig. 2. The primary division at level 2 is between areas of rough grazing, scrub and broadleaf woodland on the one hand (class 2), and areas of settlement and/or marsh on the other (class 3). Class 2 sub-divides into those grid squares where coniferous woodland covers more than 15% of the grid square (class 4), and those dominated by pasture (class 5). The other primary class (class 3) splits into areas dominated by settlement or coastal marsh (class 6) and those dominated by open water (class 7). The landscape classification was terminated at level 4. With a minimum class size of 50 members, a total of eleven landscape types were identified. The composition of these landscape classes in terms of the proportions of the different land cover types within them are shown in Fig. 3. The spatial distribution of the landscape types is shown in Fig. 4. In the final classification the lowland rural landscapes are represented by classes 20, 21 and 24 in which pasture is the dominant cover type. Class 24 occurs along the north coast of the Grower and on the lands of the lower Afon Morlais between Llanelli and Pontardulais. Class 21 is more widespread, occurring in higher areas as two major blocks, one in the area north of Llanelli and west of the Afon Loughor, and the other as the core of the Gower Peninsula. Class 21 differs from 24 by its higher contribution of poor pasture and higher woodland cover. Both broadleaf and conifer have mean cover values of about 10%. The more elevated areas of the central and north eastern parts of the study area are represented by class 20 in which there
is a balance between poor and improved pasture and where woodland cover is greatly reduced. This class appears to be transitional between the landscapes of the lower rural areas and those of the uplands. As noted above, the uplands in the study area are largely dominated by conifer plantations. These areas are found mostly to the east of the study area, and most have mean elevations above 100 m. The core of the conifer-dominated landscapes is picked out by class 16, in which the mean conifer cover is about 76%. Classes 17, 18 and 19 represent landscape types associated with the margins of these areas. In each of these classes, conifers dominate, but other cover types exert a greater influence. Class 17, for example, is predominantly a conifer and poor grass mix and seems to represent the moorland fringe. Class 18, on the other hand, picks out those areas where woodlands occur along the slopes of the settled upland valleys such as the Vale of Neath. Finally, class 19 indicates those areas where conifers meet the landscape types of the lowlands. In this class there is an almost equal mix of conifer, poor grass and improved pasture. The more densely settled parts of the study area are represented by landscape classes 11, 13, 24 and 25. In classes 13 and 25, urban cover types predominate (Fig. 3). Class 11 picks out the broader, densely settled valleys, such as the lower Neath, where the woodland component is better represented (Fig. 4). The remaining classes at level 14 in the classification denote areas of open water (mainly Swansea Bay) or coastal marshes such as that along the estuary of the Loughor.
4.2. Black Mountains
The TWINSPAN classification of the study area was terminated at level 4 with a minimum group size of 50, giving three broad divisions corresponding to the lowland, upland fringe and upland landscapes. These units together with their sub-divisions gave a total of twelve classes in all (see Figs. 5 , 6 and 7) The lowland landscapes are represented by classes 8, 18 and 19. They form a continuous block to f
263
Class 1
I
J
J.
LEVEL 1
Lowland
Upland fringe
Open uplands
FlTI
LEVEL 4
Poor grass
poor grass
Poor grass
and poor
Fig. 5. TWINSPAN classification of Black Mountains study area.
% 100
-
15
19 9 5 3 4
14
4 5 2 0 14 2 0 2 5 3 2 4 2
11
Frequency of eech class
14 4-IandscaDe
Settlement Water Arable Improved pasture
50
Heath
-
Rough grassland
Scrub and bracken Conifer Broadleaf
0-
8
--‘
18
19 20
Lowland
21
22 23 24
14
25
26 27 3 0 I
Fringe
Upland
Landscape class Fig. 6. Composition of landscape classes in Black Mountains study area.
31 /
number
265
0 Class 1
Class 2
LEVEL 1
LEVEL 2
Coastall pasture
settlement
Fig. 8. TWINSPAN classification of Wye Valley study area
% 100
Freauencv of each
2 3 13
11
10 14 3 4 6 6 3 3 5 3 115 4 2 10 18
11
19 3 4 c landscape class settlement
W
0Bare rock
H Open water Marsh
a
50
Heath
Rough pasture Scrub and bracken
El Conifer Broadleaf
0
28 29 15
-'
6 23 40 41 42 43 44 45 16 17 18 38 39 Class number I
Coastal
Pasture dominated Landscape class
Fig. 9. Composition of landscape classes in Wye Valley study area.
'L----T---J Wooded
266 b
a
3
1
350 I
Tintern P k v a I
- 210
gi .200
Earlswood Common
- 190
C
Fig. 10. (a) General characteristics of the Wye Valley study area, with spatial distribution o f (b) pasture dominated landscapes; (c) wooded landscapes; and, (d) coastal pasture.
267
the west of the study area on the lands drained by the Usk and Wye. Class 19, characterised by a high proportion of pasture, can be regarded as the ‘norm’ for the lowlands with classes 8 and 18 as variants. Each is dominated by pasture but class 8 has more woodland and a more even distribution of cover types while class 18 is distinguished by higher arable cover. The upland fringe types are represented by classes 20, 21, 22 and 23. They occur along the scarp face on the upper Hardwick Terrace and Allt shelf, along the valleys of the Rhiangoll, Grwyne Fawr, Ewyas, Olchon and Monnow which dissect the dip slope to the east, and around the other upstanding areas within the lowland region. Among the fringe types, class 22 is the most extensive. It is distinguished by an improved pasture and poor grass mix and occurs in the northeast and along the Vale of Ewyas and lower Rhiangoll. This landscape type also occurs along the scarp face but these areas are distinctive in that they are better wooded. Classes 20 and 21 are characterised as a pasture and poor grass mixed with broadleaf and conifer woodlands respectively. These woodlands cover the lower scarp face. Class 23 is distinctive in its high bracken cover and smaller extent of pasture. Classes 12, 14, 15,26 and 27 represent the upland landscapes and occur as a block on the dip slope of the Black Mountains massif. Class 14 picks out those areas of blanket conifer, while 15 delimits those areas with a mix of heather moor and poor grass. Classes 12, 26 and 27 form a spectrum of types distinguished by the varying contribution of bracken. They tend to occur along the upper scarp face and higher parts of the dip slope. Class 27 is the most extensive, picking out those areas where poor grass is dominant and bracken subordinate. In class 26 the two are more evenly balanced, while in class 12 bracken is dominant.
4.3. Wye Valley Although the landscape classification of this study was made using the same criteria as those for the other areas, the Wye Valley was found to be more uniform in its landscape characteristics. The
majority of cells were assigned to just two classes. In order to pick out the more subtle variations, it was necessary to reduce the minimum class size to 25 and to terminate the classification process at level 5. The results are summarised in Fig. 8 , 9 and 10. The main division was between land and open water. Subsequently, the land cells were subdivided into areas dominated by woodland (class 4) and agricultural land (class 5). Open water is subdivided into coastal (class 6) and non-coastal (class 7). Landscapes with a relatively high proportion of settlement or of arable land are not identified as separate classes until level 4 of the classification. In the final classification there is a four-fold division between the landscapes in the study area. These broad classes are those in which pasture, woodland, settlement and open water dominates. The pasture-dominated landscapes are the most widespread in the study area. At level 4 in the classification nearly 70% of the cells making up the study area were assigned to just three classes. The first was dominated by improved pasture. In the second improved pasture was associated with arable and woodland. The third was characterised by a pasturelarable mix. In fact, so large were the number of cells assigned to these classes that quite extensive tracts of land are represented as a single landscape class, and it was considered necessary to subdivide them by extending the classification to level 5. When the subdivision of the large pasture dominated group it split to form a class in which improved pasture covered over 90% of the area (class 44), and a class in which these pasture types were associated with settlements (class 45). The large class in which pasture was associated with arable and woodland split to form classes in which both broadleaf and conifer woodland were present together with arable (class 40), or where the woodlands were mainly broadleaf in character (class 41). Finally, the large class characterised by a pasture arable mix divided according to whether it was associated with broadleaf woodland or not (classes 42 and 43). Classes 42 and 43, differed from 40 and 41 in woodland was subordinate to arable in these classes (Fig. 9). Those landscape types in which woodland is the
269 Table 6 . Comparison between ITE classes and those of the Welsh Landscape Study ITE Classes
WLS classes'
Total
9
10
17
18
Lowland Fringe Upland
61(23) 8(20) 2(28)
40(14) 4(12) O(17)
28(80) 98(70) 123(98)
O(11) 3(10) 33(14)
129 113 158
total
71
44
249
36
400
'WLS = Welsh landscape study classes defined using remote sensing data ( ) = expected frequency if there were no association xz = 257.8 with 6 d.f.
the upland zone by the analysis of remotely-sensed imagery fall into classes 17 and 18 on the ITE classification (Table 6). Similarly, the majority of the cells designated as lowland fall within ITE classes 9 and 10. The bulk of the fringe types coincide with class 17. The null hypothesis of no association between the two classifications can be rejected (x' > 22.5 with p < 0.001). When assessing the significance of the relationship between the landscape classes defined here and the ITE Land Classes it is important to note that although the classification techniques used in the two studies are similar, the data they employed are completely independent. The current study has only used data for 11 land cover types obtained from the classification of satellite imagery. The ITE classification used data from paper maps of the study area (i.e., 1:50,000Ordnance Survey, 1:250,000 Geological Survey (solid and drift), climate maps at 1 :1,000,000). Moreover, the key used t o assign the 1 km x 1 km cells to an ITE land class was derived from a national survey rather than from one generated for the Black Mountains study area in particular. The similarity between the two classifications implies that the landscape divisions which both pick out are real ones, rather than artifacts of some particular classification approach. Despite the similarities with the ITE classification there appear to be several advantages in using satellite imagery for the classification of landscapes rather than maps. The correspondence between the classifications occurs onlv at a fairlv general level of
resolution. Because the ITE system is directed at the national level, the classification is rather coarse at the local one. Although Fig. 11 shows the major landscape contrasts within the study area, it is clear that in comparison to the map produced by the current project (Fig. 7), many of the subtleties in the landscapes have been lost. The landscape classes generated by the analysis of remotely-sensed imagery are nested within these broad national classes defined by the ITE. They can be used, therefore, to extend the national classification to a deeper, more detailed local level. The method has the particular advantage that availability of remotely-sensed data allows a complete survey rather than a samplebased one to be developed. Finally, it is interesting to note that, even through terrain data were not included in the analysis, the close relationship between land cover and terrain allowed the major physical units to be delimited.
5.2. Comparison with agricultural census
The Agricultural, or June, Census of MAFF collects information on the size and range of enterprises involving both crops and livestock on agricultural land in England and Wales. As with most census materials, the release of data relating to individuals or organizations is not permitted for reasons of confidentiality. Traditionally, data from the Agricultural Census has been aggregated on a parish or community basis. Although the Agricultural Census has been taken for over 100 years, the machine readable data held by MAFF only covers the period since 1975. In additional to these parish data, MAFF has also made available census data projected on a 1 k m x 1 km basis for certain years. The most recent available at the time of this study was that for the 1981 returns. The data set was obtained through the Economic and Social Research Council (ESRC) Rural Areas Database. These data could be compared with the land cover estimates and landscape classes produced in the current study. The difference in date between the census and satellite imagery is not considered important since, given the character of the agriculture in each of the studv areas. it is unlikelv
270 that significant land use change had occurred in the period between June 1981 when the census was taken and April 1984 when the satellite data were recorded. The 1 km x 1 km agricultural census data were generated by apportioning the parish totals for each census variable over the cells which fell within the parish rather than by regrouping the original returns by grid cells. Grid squares were assigned to parishes according to which parish covered the largest area within it. Cells which contained significant areas of non-agricultural land within them were assigned zero values (N. Walford, pers. comm.). The problems for local scale studies arising out of aggregation of the Agricultural Census data to the parish level have been discused by Clark (1982). Apart from inaccuracies in the returns themselves, problems of interpreting the census data arise from the fact that a farm unit may extend over more than one parish. Usually a single return is made and the data all assigned to one parish. Similar problems occur at county boundaries. Additional problems arise from incomplete coverage of the census and errors of amalgamating the statistics. Because the 1 kmx 1 km data were constructed from the parish data, many of these problems are carried over. However, despite the fact that estimates for individual grid squares may depart significantly from reality, these data, like the parish returns themselves, can be used to build up a general picture of the spatial variations in land use and land cover within a region. The census statistics were extracted for each 1 km x 1 km of the three study areas and partitioned by landscape class. For each landscape class, the mean value for the subset of variables which were available were calculated. The total number of cells extracted from the agricultural census may be less than the total number in the study area since cells not in Wales (i.e., east side of Black Mountains study area) and those with a large proportion of open water were not included in census data. The major contrast between the study areas in terms of the census data is in the much smaller proportion of land in West Glamorgan is covered by the returns compared to the other two study
areas. These data show only the mean values recorded in each landscape class and should not necessarily sum to 100 ha. However, there appeared to be a marked shortfall in the total amount of land recorded in each grid square compared to that which is theoretically possible throughout West Glamorgan. None of the landscape classes had more than about one-third of the total area covered by the census statistics whereas some landscape classes in the Black Mountains and Wye Valley areas showed agricultural areas approaching 75 070 of each cell. The area of West Glamorgan is 82,000 ha (Welsh Office 1985). The total agricultural area for the county is 43,445 ha (Welsh Office 1983), or about 53% of the total. The corresponding figure for 1980 is 45% which, as Rudeforth et al. (1984) notes, is the lowest of all the Welsh counties. The reasons for this low figure is difficult to explain, but the implication is that there are other major types of nonagricultural land occurring throughout the area. Alternatively, the difference between study areas may be explained by the differences in amount of common land between them. The rough grazing variable includes mountain, heath, moor and down over which the farmer has sole grazing rights. Common land is not included in the return. Despite such problems of interpretation, however, there is close agreement between the data obtained from the census returns for the study areas and the estimates of land cover for the most extensive cover types which can be obtained from the landscape classifications made in this study. The correspondence between the June Census and the remotely-sensed data was tested using three of the most important land cover variables recorded in the MAFF statistics and their counterparts derived from the classification of the satellite data. Pasture is the major agricultural land use in each of the three study areas. In the Census, these are broken by into the following categories: grassland put down in 1977 or later (census variable 5 ) , all other grassland excluding rough grassland (6), and rough grassland (7). The ‘other’ grassland type (census variable 6 ) corresponds most closely with the ‘improved pasture’ category used in the analysis of remotely-sensed satellite data. In each of the
27 1 Table 7. Association between selected agricultural census variables and remotely sensed land cover categories Study area
Census variable
Remotely-sensed cover class
West Glamorgan
other (6) rough grazing (7) other (6) rough grazing ( 7 ) total crops and fallow (4) other ( 6 ) total crops and fallow (4)
pasture (6) moor (4) pasture ( 6 ) moor (4) arable (7) pasture ( 6 ) arable (7)
Correlation coefficient' ~
Black Mountains
Wye Valley
~~~
+0.73** + 0.22 ns +0.84*** +0.71**
+0.93*** + 0.97*** +0.93***
'Significance levels for Spearman's rank correlation coefficient are as follows: ns=not significant, *, ** and *** significant at 95%, 99% and 99.9% levels respectively. In each case a one tailed test was used. ( )=variable code number in each data set.
study areas the agricultural census data showed that the largest areas of this grassland type occur in the lowland pasture landscape classes. This is especially evident for the Black Mountains and Wye Valley areas. Using Spearman's rank correlation technique to test for an association between the area estimates contained in the two data sets, in each case the null hypothesis of no positive association could be rejected with a high level of confidence. The results of the analyses are shown in Table 7. A significant association between area estimates derived from the agricultural census and the analysis of remotely-sensed data was also obtained for rough grazing vs poor grass in the Black Mountains. No rough grazing was recorded for the Wye Valley area from the analysis of remotely-sensed data and the agricultural census confirms that its area is indeed negligible here. Only in the case of rough grazing in West Glamorgan is no association found between the estimates obtained from the two survey methods. The small areas of agricultural land recorded in the census for this study area has been noted above. Since the analysis of the remotely-sensed imagery suggests that much of the rough grassland is associated with the forest types which dominate much of the area, and since these are not included in the census returns, this may also partly explain the difference between the two estimates. In addition to the comparisons made above, further statistical analyses were undertaken using the estimates for total crops and fallow (variable 4) obtained from the agricultural census. No analysis
could be made for the West Glamorgan area since the contribution of arable is limited. In the case of the other two study areas a significant positive association was obtained using Spearmand's rank correlation technique. It was suggested above that in the classification of remotely-sensed imagery, the agricultural cover types (improved and unimproved pasture, arable land) were amongst the most difficult to discriminate. The analysis presented in this section shows, however, that despite these difficulties, there is good agreement between the estimates of area types obtained from the classification of remotely-sensed imagery and the agricultural census returns. The landscape units appear to highlight many of the contrasts in agricultural land use and so it is likely that they also capture the major contrasts in landscapes within the study areas.
7. Discussion
The methodology described and tested in this study can be used as a basis for landscape characterization. Beyond the basis it offers for this type of application, it also has several implications for those concerned with the general assessment of landscape and other ecological resources. These implications concern the design sampling frameworks for environmental survey, and the development of more reproducible methods for landscape evaluation.
7.1. The problem of survey The need for adequate sampling frameworks based on landscape character is fundamental to much work in environmental science. Unfortunately, there is often little agreement as to what kinds of sampling design are most appropriate and how sampling schemes at different scales relate to each other. In looking for a flexible approach to the problem of designing a sampling framework the ITE methodology is a good example of what might be achieved at the national scale. The system has been used to estimate total areas of the major land cover types in Great Britain (Bunce et al. 1981a) and the extent of land cover change over a six year period (Barr et al. 1986). Elsewhere, the land classes have been used to estimate badger densities (Cresswell et al. 1989), fox densities (MacDonald et al. 1981), the availability of wood energy in the UK (Bunce et al. 1981c), assessing site suitability for wind turbines (Bell et al. 1989) and planning (Bunce et al. 1986a, 1986b). The development of such a national sampling framework has, however, required considerable time and resource inputs. In areas where basic map data are poor, national land classifications are lacking and survey resources limited, it is clear that landscape classifications can be constructed rapidly from the analysis of remotely-sensed satellite imagery. Such classification could perform a similar role as the ITE has in the UK. The steps involved in such a process are: 0) the collection of test and training data for the purposes of image classification at a general level, (ii) the generation of landcape classes based on the combinations of cover types occurring within defined spatial units such as grid cells, (iii) the detailed survey of the ecological characteristics of a sample of each stratum of the landscape classification to provide information for ecological attributes which cannot be obtained directly from the analysis of remotely-sensed imagery. The important point to note about the procedure is that landscape attributes not directly observable by remote-sensing systems can be included in such
a framework. Moreover, the survey is not tied to a single spatial scale because land cover pixels can be aggregated into cells of any size for the purposes of the landscape classification. The hierarchical nature of the TWINSPAN algorithm enables the resolution of the classification to be changed in the context of the particular problem under investigation. For extensive surveys more general classes might be used. At the local scale more detailed classes would be more appropriate. The fact that the classes are nested one with the other, however, ensures that the potential gap between the national and local scales could be bridged. The availability of modern GIS technology will allow such integration to be achieved (cf. Johnson 1990).
7.2. The problem of landscape evaluation
Approaches to landscape evaluation are often highly contentious because they depend on a whole range of factors many of which are difficult to specify objectively. In looking at the factors which have hindered progress in this area it is valuable to compare recent experience gained in the area of the ecological evaluation of sites for conservation purposes. The framework for the ecological evaluation of sites in the UK was provided over a decade ago by Ratcliffe (1977). Although arguments still occur about what criteria should be applied and whether they should be weighted in some way (Goldsmith 1983), it is clear that their formulation represented a considerable advance. The widely-used concepts of rarity, fragility, diversity and naturalness, etc. have provided a language in which a debate about the value attached to a particular site can be conducted. Progress in the area of site evaluation contrasts quite starkly with that concerned with landscape evaluation. Here the process seems to have been frustrated by the lack of any suitable framework for discussion. If we consider the landscape classifications produced by using remotely-sensed data, however, it is striking how easily the kinds of criteria previously applied to habitat can now be applied to landscape.
273 Rarity can easily be assessed in terms of the proportion of cells of a given type occurring within a region. Extent of a given block is also easily assessed. The geographical relationships between classes, in terms of the environmental, economic and social gradients present can also be explored using the landscape classifications and the vulnerability to changing land management practices determined. Finally, the ‘subjective’ criteria suggested by the Countryside Commission (see Section 2) could be applied to the landscape classes as part of the detail collected during a site visit. The fact that criteria used for site evaluation may not have been used for landscape evaluation may reflect no more than the difficulty of acquiring basic data. With the availability of satellite imagery, however, such barriers may be removed and a consistent framework brought to the problem of landscape evaluation.
8. Conclusion The present study shows that remotely-sensed satellite data can be processed digitally for purposes of landscape classification. Analysis has shown that despite the fact that imagery has been used only to extract information about land cover, the close association between cover and terrain has me the major physical divisions in the areas are reflected in the landscape classific The patterns are consistent with those indicated in other independent data which relate to the areas. The approach to landscape classification described here not only allows landscape descriptions to be made rapidly, but also allows framework for landscape survey and landscape evaluation to be constructed in areas where basic ma resources for field survey a
Acknowledgements
I would like to acknowledge the receipt of a grant from the ESRC (ref D00232115) for this work together with the support of the Welsh Office. I would also like to thank Charles Smart, Peter
Bradbury and Paul Mather for their valuable input of effort and ideas.
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