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The separation of land cover from land use using data primitives
A.J. Comber,
Department of Geography,
Leicester University,
Leicester,
UK.
E-mail: [email protected]
Abstract
The common confusion of land cover and land use in many datasets is problematic for
many data integration activities. This paper proposes an approach for the separation of
land cover and land use based on data primitives. Data primitives are those dimensions
that describe at the most fundamental level the processes under investigation. In this case
they provide building blocks that to allow land cover and land use to be separated. A
series of data primitives were identified from the literature and applied to US National
Land Cover Dataset (2001). Mapped outputs, separating the concepts of form (land
cover) and function (land use), show the degree of land use, the degree of land cover and
locations where the concepts of use and cover are confused. The separation of land cover
and land use facilities the integration of land data for environmental modelling and
planning activities. The work promotes the need for land use and land cover to be
maintained as distinct concepts in data collection activities.
Key words: semantics, expert, integration, land use, land cover, data primitive
1. Introduction
The concepts of ‘land cover’ and ‘land use are commonly confused in most land surveys
including those derived from satellite imagery. This confusion creates problems for
research and other activities that seek to integrate different land data as land cover and
land use are fundamentally distinct (GLP, 2005). Land cover is determined by direct
observation of the earth’s surface and land use is a socio-economic interpretation of the
activities that take place on that surface (Fisher et al, 2005). Mixing of the concepts of
land cover and land use has become so prevalent that classifications of ‘pure’ land use or
land cover are rare even when that is the stated objective (Di Gregorio and Jansen, 2000).
The historical reasons for the blurring of these concepts have been reviewed by Fisher et
al (2005) and Comber et al (submitted) which, in brief, relate to the different mapping
needs of the different agencies involved in the project and the ability to statistically
process digital remotely sensed data.
The recording of land cover is a relatively recent phenomenon. Historically the
overriding trend has been for land use to be recorded as documented by Fisher et al
(2005) who note that until the 1960s there was little evidence of land cover being
recorded. The availability of digital satellite data in the 1970s resulted a shift away from
demand driven classification (e.g. of aerial photography) to data driven statistical
classifications of remotely sensed imagery. The classification techniques employed by
those involved in recording land cover from remotely sensed imagery have become
dependent on and determined by the imagery available to them (Comber, 2002). For
these reasons, some workers have identified the need to step back from such data driven
approaches to the manipulation of remote sensing data. For example, Skelsey et al (2003)
propose and implement Task Oriented approaches to the classification of remote sensing
data that focus on the task in hand rather than on the specifications and characteristics of
the data.
Confusing the concepts of land cover and land hampers data integration and modelling
activities such as evaluating the impacts of climate change and the interaction between
terrestrial and atmospheric environments. Land cover is needed for the development of
physical environmental models and land use is required for policy and planning purposes.
The IGBP have called for the explicit separation of the concepts of land use and land
cover in order to facilitate such modelling activities (GLP, 2005). Comber et al
(submitted) noted that the concepts of land use and land cover need to be separated in
order to foster a culture of consistency in land data recording. Similarly, Brown et al
(2000) argue for land cover to be separated from land use in order to better link socioeconomic changes with observed changes in land cover. As a step in this direction, this
paper proposes a method to separate land use and land cover in existent data, based on
the concept of data primitives. Land cover and land use have very different
characteristics. For instance there may be many different simultaneous or alternate land
uses at any given place whereas the classification of land cover records the physical
material on the surface of the earth and is static. This means that the relationship between
land use and land cover cannot be directly inferred as they have a complex many-tomany relationship. As models require one or the other the separation of land cover and
land use will facilitate activities that require either as an input.
In order to integrate data into models, measures of semantic and conceptual overlap
between the data and the intended application are needed (see Wadsworth et al this
volume). As Wastfelt (2005) comments “if improved technologies such as remote sensing
and GIS are to become more useful to society, they need new strategies for interpreting
their source material that will bridge the wide epistemological gap between physical and
social-scientific understandings of space” (p398). The problem is that data are commonly
collected for a specific purpose. The classes and concepts embedded in the data reflect
the interests of those involved in the commissioning of the data (e.g. the steering group).
The epistemology of data collection and measurement and the ontology of data
classification will be specific to those specific interests (Comber et al., 2003). Other uses
of the data therefore have to rework the data in some way to fit the objectives and
concepts of their analyses or their data. The work presented in this paper makes an
attempt to do this by separating the concepts of land use and land cover using scores in
different data primitive dimensions.
2. Background
Land use and land cover are fundamentally different concepts. Their conflation is
convenient but represents an illogical but commonplace paradigm for the following
reasons. First, the cover that exists at a given place is a single land class (not withstanding
work that seeks to record the heterogeneity of cover such as fuzzy set theory). This is
because land cover is defined by what is observed at any given point on the earth’s
surface. Whilst there may be variation in precisely what is observed due to different data,
sensors, classification algorithms and operators, the phenomenon of land cover is agreed
to be a single phenomenon at any given point in time. Second, the land use that exists at
any given place is likely to be multidimensional. This is because land uses are defined by
human activity on the land which may be single, simultaneous or alternate. For example,
Fisher et al (2005) note that a single patch of plantation forestry may also be used
simultaneously for several forms of recreation, (hunting, hiking) and for grazing as well
as timber production and that the land uses may alternate (grazing, hunting). Some land
uses also vary seasonally – a reservoir provides flood control in the spring, hydro-electric
power in the winter, fishing in season and boating all year round. Jansen and Di Gregorio
(2003) comment that land-use is influenced by cultural factors such as agricultural
practices with the result that different land-uses are practised on the same type of land in
different areas. Third, land cover and land use classes are not directly compatible. They
rarely have a one-to-one relationship and more commonly have a one-to-many or manyto-many relationship. For example, the cover type ‘Grass’ may occur in a number of uses
(sports grounds, urban parks, residential land, pasture, etc). Similarly the use type
‘Residential’ may be composed of many covers including trees, grass, buildings, and
asphalt. Jansen and Di Gregorio (2003) comment that relations between land cover and
land use vary in rural and urban contexts noting that in rural contexts (composed mainly
of agricultural and forest land uses) there is more likely to be more direct one-to-one
relations between cover and use, whereas in urban contexts (i.e. where there are more
people) there are fewer one-to-one relations. Fourth, relationships between land cover
and land use may change depending on the scale or level of analysis at which they are
observed due to the complexity of the links between socio-economic and environmental
systems (Veldkamp and Lambin, 2001; Monroe, 2007). Veldkamp et al (2001) noted that
land use to land cover interactions at different scales can create local, spatially dependent
process.
2.1 Classification
Classification is the process by which objects or individuals are allocated to classes or
categories. Many scientific endeavours are based on the need to simplify the real world
into some ordered aggregation and classification provides a method to do this. The
assumptions that underpin classification are that objects of the same class can be treated
as a single phenomenon and generalisations can be made about their behaviour,
characteristics or attributes. The grouping provided by classification (or stratification)
therefore allows scientists to analyse those variations in behaviour between and within
groups. In this section, the objectives and characteristics of classification are reviewed in
light of the classification of remotely sensed imagery.
The classification of remote sensing data commonly employs one of two approaches:
identifying regions with similar characteristics or matching regions to pre-defined
prototypes. In the first approach only the number of classes are predefined. The
characteristics of the classes are not. An iterative statistical process considers the digital
numbers of the image objects (pixels or segments) in the N layers of the image data. It
identifies statistical clusters in this N-dimensional space and allocates each image object
to the class to which it is nearest under some criterion of distance in this N-dimensional
feature space. The clusters represent classes based on spectral similarity. In this approach
it is assumed that the number of classes specified matches the number of spectral classes,
that the selection of image bands captures variation between objects on the ground and
that the spectral clusters represent land classes of interest. This unsupervised
classification technique can be used in situations where little ground based data or
samples are available. In the second approach image objects such as segmented regions
or pixels are compared with prototypes (also known as exemplars, training or samples
data) of different classes. The prototypes, or training regions, represent categories of
information and serve as abstractions of the most typical or central members of that
category. The image object is allocated to the class to which it is nearest under some
criteria – distance in spectral space, fulfilment of some condition, probability calculated
from a set of beliefs. Brown (1998) note that this approach has traditionally been used in
vegetation mapping, where vegetation stands are represented as discrete spatial units and
allocated to one of a number of predefined categories. There is an obvious relationship
between this discrete model of classification (Brown, 1998) and the allocation of
homogenous areas of vegetation into specific categories based on their characteristics. In
this context a prototype for a class can be seen as a theoretical cognitive structure (Lloyd,
1994). A number of studies have considered the encoding of cognitive structures as
semantic and spatial reference points (Rosch 1975a; 1975b), family resemblance (Rosch
and Mervis 1975), and basic-level categories in a hierarchy (Rosch et al. 1976; Tversky
and Hemenway 1983). There are known issues relating to the use of prototypes in
classifying remote sensing data including the number of number samples used to define
the prototype, the selection of the image bands used to differentiate between the land
features of interest, and the number of classes, all of which produce variation in the
mapped outcome. This supervised classification technique is the most commonly used
generic method for classifying remote sensing data as it allows the operator to have a
degree of control over the classes that are created. Comber et al (2005a) provide a wider
discussion of classification theories in relation to assigning image objects to classes or
categories.
It is instructive to reflect on the implications of classifications for mapping land cover
and land use from remote sensing data. The process of classification allocates individuals,
in this case image objects, uniquely to one class based on their characteristics, predefined
(supervised) or not (unsupervised). In supervised classification, the class assigned to each
individual is that of the ‘closest’ prototype, where the closeness is usually defined by
some measure of distance in the N-dimensional image space. Since the nineteenth century
most land surveys have used such approaches, often with a taxonomic hierarchy, as the
basis mapping with the objective of defining relations between mapped objects in order
to understand them better. Thus conventional land mapping defines classes and identifies
areas of land to which those descriptions could be applied and produces a crisp
choropleth map of spatially discrete mapped areas without gaps or overlaps. Recent
developments acknowledge the shortcomings of crisp classifications such as fuzzy set
theory (Wang, 1990). In fuzzy classifications membership functions are generated for
class for each image object, usually based on the distance in N-dimensional space of that
object to the centre of the category cluster.
In summary, classification is the process of simplifying and ordering the real world. In
remote sensing unsupervised classification approaches are ‘bottom up’ and the closeness
of objects to each other is calculated through statistical clustering algorithms or matching
characteristics to classes. Supervised approaches to classifying remote sensing data are
‘top down’ and match image objects with predefined prototypes. In each case, objects are
allocated a class which itself has a position within a wider taxonomy of land.
2.2 Mapping land cover and land use
Bibby and Shepherd (1999) comment that land use objects only “are best regarded as
objects by convention, that is, they are objects by virtue of the fact that they are held to be
so” and that such objects are “grounded in discourse and projected onto the physical
world” (p584). Conversely, land cover is concerned with pre-existing physical matter.
The authors make two salient points: that land-use categories lack an intrinsic relation to
physical matter, and that they form various hierarchies whose structures reflect different
economic and social organizations. The implications for mapping use from remotely
sensed imagery are that the same land use can be described at many different levels and
membership of one land use to one category does not preclude membership of another.
Monroe (2007) also noted that the allocation of a particular land use class is not an
objective, observable process: use categories are allocated for other reasons such as
institutional objectives, maximising profit or production factors. Heoschele (2000)
observed subsistence farmers being disadvantaged in the recording of land use compared
to large land owners who are more interested in the forestry. Thus the choice of land-use
class is not transparent and the specific circumstances of this choice are not directly
measurable (Anselin, 2002).
Remote sensing captures the spectral reflectance from earth’s surface. The classification
of remote sensing data necessarily allocates image objects into land cover classes based
on reflectance values. A second stage of interpretation, often requiring ancillary data or
local knowledge, is needed to infer land use. In this context the classification of data on
the reflectance of the earth’s surface to land use categories is an unscientific slight of
hand as Monroe (2007) notes “Empirically, the discrete representation of land use is often
proxied by a discrete representation of land cover” (p522). A number of authors have
highlighted the problem of confusing land use and land cover in the classification of
remotely sensed imagery. Brown et al (2000) observe that the phenomena of land use and
land cover are linked to one another. However changes observed in remotely sensed
imagery may not relate to changes in socio-economic conditions and therefore need to be
mapping as separate processes for theoretical and practical reasons. Barnsley and Barr
(2001) comment that spectral radiance values recorded in remotely sensed data are only
indirectly related to the attributes and dimensions of land use.
A number of workers have sought to tackle the problems of identifying land use from
remotely sensed data using a secondary techniques based on the spatial configuration of
land cover elements. Barr and Barnsley (2004) proposed an approach based the
morphological properties of buildings to identify a range of land use categories. They
concluded that different types of urban land use may be identified through analysis of the
spatial disposition of their constituent land cover parcels and suggest that a quantifiable
mapping exists between urban form (land cover) and urban function (land use). Herold et
al (2002) used landscape metrics to describe urban land-use structures and land-cover
changes. Their results showed that different urban land-use types could be identified and
land use changes quantified. Jansen and Di Gregorio (2003) identified agricultural
production systems based on analysis of field patterns with the presence of and type of
built-up structures: the regular shape of land cover polygons indicate commercial
production systems and irregular forms may indicate protective and conservation uses.
They note that relations between land cover and land-use are more complicated in the
case of forestry as the presence of tree stands may not indicate their use(s) and that the
relation between land cover in built-up areas and land-use is extremely weak. In a more
local study Harrison (2006) has developed a classification system that explicitly separates
land cover and land use. The objectives of his work were to promote a coordinated and
consistent approach to data recording across government sectors in the UK. The clear
separation of land use and land cover allows the requirements of different user
communities to be explicitly supported without having to re-work land cover data to land
use for policy and planning purposes and land use data to land cover for environmental
objectives. This explicit distinction between use and cover facilitates analysis of
relationships between the drivers and patterns of land change.
2.3 Data primitives for land cover and land use
Data primitives are here defined as those dimensions or measurements that describe the
processes under investigation at the most fundamental level. They provide information
about the building blocks that underpin the concepts of the phenomenon – what they
mean and what they represent. The objective in describing data and land concepts using
primitive dimensions (variously referred to as “conceptual spaces”, “approximation
spaces”, “domains”) is not seek to generate a hierarchical taxonomy – another
classification – rather the approach seeks to generate descriptions of different data
features to allow the amount of overlap between them to be quantified. A number of
workers have used data primitives to facilitate the integration of data with different
ontologies and epistemologies.
Ahlqvist (2004) uses conceptual overlap in four dimensions (or primitives) to describe
classes simple land taxonomy with 2 agricultural and 2 forest land classes. Each class
was given a value in each dimension allowing the amount of overlap between different
classes to be quantified. Wadsworth et al (this volume) adapted and extended this
approach, defining 5 domains in order to quantify the overlap between 3 Siberian land
cover from 3 classifications (IGBP classification of 1km AVHRR, GLC based on 1km
AVHRR and SUC based on MODIS 500 meter). The domains were specific to the
objectives of the Siberia study: photosynthetic activity / biomass accumulation, wetness,
human disturbance, seasonality / phenology and vegetation height. The FAO Land Cover
Classification System (LCCS) developed by Di Gregorio and Jansen (2000) provides a
method for integrating land cover based on a 2-phase process. In the first phase, land
cover classes are allocated to one of eight major land cover categories. In the second
phase a further set of classifiers are used to refine the class description based on
environmental attributes (e.g. climate, land form, soils / lithology and erosion) and
specific technical attributes (e.g. floristic composition, crop type and soil type). One of
the criticisms of the LCCS is that the classifiers and categories are fixed and Boolean. For
example LCCS classifiers describe forest height to be 2-7m (B1), >3m (B2), >14m (B5),
7m-15m (B6) and 3m-7m (B7). The application of these sub-classes can result in
ambiguities and can create second order uncertainties when data are being described
using the LCCS. For these reasons Ahlqvist (2007) proposed modifications to LCCS and
suggested that LCCS-style reference systems should define the dimensions and unit of
measurement for quantitative data attributes in order to allow users freedom to define any
threshold values in that dimension.
3. Method
The US National Land Cover Dataset 2001 for the state of Connecticut and surrounds
was downloaded from Multi-Resolution Land Characteristics Consortium hosted by the
USGS1. A description the NLCD project and methodology can be fund in presented in
Homer et al (2004). The dataset records 29 land types, with a mixture of cover and use
classes, classified from composites of Landsat satellite imagery and modified from the
Anderson et al (1976) classification.
A series of primitives were identified from other work that has sought to harmonise
classifications, including the FAO Land Cover Classification System (Di Gregorio and
Jansen, 2000), the conceptual overlaps of Wadsworth et al (this volume) and Ahlqvist
(2004) and Wyatt and Gerrard (2001). In setting the criteria the aim was to capture the
range of alternatives and information required by applications using land cover or land
use data. The objective was defines those parameters that characterise the important
features of land use and land cover. The selection of primitives will be returned to in the
discussion. Land use studies are concerned with the nature and degree of human activity
and there is much interest in land use classifications that capture the diversity of activity
in both urban and non-urban environments. In urban contexts the modelling and research
interest is in being able to identify the economic value or social value/appreciation of
activities as well as the nature of that activity itself. Often in agricultural contexts the aim
is to capture land use information that relates to food production. The elements that land
cover classifications seek to report on are the physical properties of the surface. These
may be related to the naturalness of the surface or human activity – impervious surfaces
and the like – or may be related to the nature of the vegetation present. Many land cover
classes relating to vegetation define it in relation to its structure. Often this is in terms of
percentage cover, vegetation height, seasonality, and the prevailing environmental
conditions, often wetness. As a result of this review the following dimensions or
1
http://gisdata.usgs.net/website/MRLC/viewer.php
primitives were identified for application to a land cover / land use dataset (NB units are
listed, if not then the primitive is an index):
1. Naturalness: the extent to which the class was a naturally occurring feature or was
directly the result of anthropogenic activity i.e. the cover primitive;
2. Vegetation height: the minimum height in metres of the vegetation;
3. Vegetation canopy coverage: the minimum percentage of vegetation coverage;
4. Homogeneity of appearance;
5. Seasonality: the extent to which the classes is seasonal or perennial;
6. Structure: the complexity of vegetation structure;
7. Wetness: the dependency on specific wetness conditions (e.g. soil, growing
medium, climate);
8. Biomass production: relating to the amount energy fixed through photosynthesis
by the class;
9. Human activity: the amount of human related activity in the class;
10. Human disturbance: the extent to which the existence and nature of this class
reflect anthropogenic activity;
11. Economic value: the importance economically of this class – how much money
can be earned or how much it is worth;
12. Production of crop related food;
13. Production of animal related food;
14. Artificiality: the extent to which the surface has been artificially created
Each class was given a score in each primitive of between 1 (least) and 9 (most) if the
class was thought to have some properties in that primitive. These were allocated based
on examination of the class definitions in Anderson et al (1976) and Homer et al (2004).
If the class was thought not to have any attributes in that primitive then no score was
allocated. The allocation of the primitive scores is subjective as it was done by a data user
with experience of other land cover classifications, with no direct experience of the
USGS NLCD classification. The scores for the different classes in these 14 dimensions
are shown in Table 1. The scores were applied to the data so that each NLUD class was
attributed with a score in each of the 14 primitives.
(Insert Table 1 about here)
4. Results
The objective was to apply the primitive scores to the NLCD classes and to test the
separability of land use and land cover elements in the NLUD data. The primitive were
allocated as into being primarily related to ‘Use’ or ‘Cover’ and overall scores for each
NLCD class were calculated by normalising use and cover average scores. The primitives
allocated to the Cover group were numbers 1) to 7) in the list above and numbers 8) to
14) were allocated to Use. The mean scores for the 2 groups were then normalised using
a cumulative distribution function:
(Equation 1)
where x is the original score, µ the group mean and σ the group standard deviation. The
Use and Cover scores for the different NLCD classes generated in this way are shown in
Table 2.
(Insert Table 2 about here)
The normalised scores were used to identify the classes with a high degree of use and / or
cover.
The classes with high Cover scores above the 50th percentile are: Open Water, Perennial
Ice / Snow, Developed High Intensity, Barren Land, Deciduous Forest, Evergreen
Forest, Mixed Forest, Shrub / Scrub, Orchards / Vineyards / Other, Lichens, Moss,
Woody Wetlands, Palustrine Forested Wetland and Palustrine Scrub / Shrub
Wetland.
The classes with high Use scores above the 50th percentile are: Developed Open Space,
Developed, Low Intensity, Developed, Medium Intensity, Developed High
Intensity, Dwarf Scrub, Shrub / Scrub Orchards / Vineyards / Other, Pasture / Hay,
Cultivated Crops, Urban / Recreational Grasses, Woody Wetlands, Palustrine
Forested Wetland and Palustrine Scrub / Shrub Wetland.
Many of the classes identified as being strongly Cover and Use are perhaps unsurprising
and may have been identifiable in advance as belonging to a general use or cover
category without calculating a score from the 14 data primitives. Others are unexpected
especially the strong use scores for the wooded wetland and the scrub. The inclusion of
Biomass production as the sole use primitive given a score for these classes resulted in
these high use scores.
The classes Developed High Intensity, Unconsolidated Shore, Transitional, Dwarf Scrub,
Shrub / Scrub, Orchards / Vineyards / Other, Grassland / Herbaceous, Sedge /
Herbaceous, Woody Wetlands, Palustrine Forested Wetland and Palustrine Scrub / Shrub
Wetland all had similar Use and Cover scores. This provides an indication of the extent to
which they may be ambiguously defined. If the classes with high use scores based on
Biomass production are excluded, a set of highly mixed classes are identified:
Developed High Intensity has a strong cover attributes as it is highly homogenous
(spectrally and in terms of the land cover) as well having a degree of use:
Unconsolidated Shore is defined as being composed of “unconsolidated material such as
silt, sand, or gravel that is subject to inundation and redistribution due to the action
of water” and is “characterized by substrates lacking vegetation except for
pioneering plants that become established”. It has both weak and cover scores, with
little anthropogenic use and little homogeneity or structure of cover.
The Transitional class has a high degree of both use and cover: “Areas of sparse
vegetative cover (less than 25 percent of cover) that are dynamically changing from
one land cover to another, often because of land use activities. Examples include
forest clearcuts, a transition phase between forest and agricultural land, the
temporary clearing of vegetation, and changes due to natural causes (e.g. fire, flood,
etc.).”
Orchards / Vineyards / Other has a short NLCD description grounded in use (“Orchards,
vineyards, and other areas planted or maintained for the production of fruits, nuts,
berries, or ornamentals”), but also has a distinct cover dimension: orchards are not
forest (>5m) but could be included under Shrub / Scrub (“Areas dominated by
shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total
vegetation. This class includes true shrubs, young trees in an early successional
stage or trees stunted from environmental conditions.”).
Grassland / Herbaceous has a mix of cover and use, reflected in the definition: “Areas
dominated by grammanoid or herbaceous vegetation, generally greater than 80% of
total vegetation. These areas are not subject to intensive management such as
tilling, but can be utilized for grazing”.
The scores can be applied as weights to the NLCD to visualise the distribution of
confusions between use and cover. NLCD Data for Connecticut was downloaded from
the USGS website. The scores from the above tables were joined to the NLCD classes.
The weights as applied to the different classes were interpreted to as beliefs, then areas of
differential belief in land use, land cover and land use with land cover were identified.
Figure 1 a-e shows the NLCD data, weighted land cover and land use, areas where land
both use and land cover weights were high (>50th percentile of weights) and areas where
they were both low (<50th percentile)
(Insert Figure 1 about here)
5. Discussion and Conclusions
This paper has presented a method for separating the concepts of land use and land cover
that are embedded in most land datasets. There is a need for their separation to support
modelling activities (GLP, 2005), to better link observed changes in the earth’s surface
with socio-economic process (Brown et al, 2000) and to foster a culture of consistency in
land survey reporting (Comber et al. submitted). The method presented in this paper has
applied a set of data primitives to an existent land dataset and sought to capture the
essence of both land cover and land use. The aim was to develop and illustrate a generic
process by which the concepts of land cover and land use could be separated relative to
the task in hand.
The approach applied data primitive scores to land classes in a dataset where the concepts
of land cover and land use were confused, the USGS National Land Cover Dataset, in
order to generate measures of the degree of land use and the degree of land cover for each
class. The results of this analysis are necessarily subjective for a number of reasons. First,
fourteen primitives were identified by an expert user as being representative of the
essential elements of land cover and land use. Other primitives may be more important
for specific applications. For instance many of the LCCS classifiers have classifiers that
relate to spatial context. Possible spatial primitives include patch size, landscape ecology
indices, relationships with adjacent classes and bio-geographic context. Second, some of
the primitives are not orthogonal to each other and may essentially record the same thing.
For example, Naturalness and Artificiality may be the inverse of each other and Human
activity and Economic value may be describing the same processes. However the
objective of this work was to explore an approach for separating use and cover as
demanded by scientist and modellers. Third, the expert allocated scores based on their
limited experience of NLCD and the Anderson et al (1976) classification. Other work has
shown that different experts have varying opinions of how landscape features relate to
data (Comber et al., 2005b). Fourth, each of the primitives was related to either Use or
Cover. For some applications, the specific combination of primitives may vary. For
example, Biomass production may be directly related to vegetation cover or the land use
of the area. However, the analysis also shows that it is possible to identify classes which
are closely related to the surface cover and those which are related to the activity on that
surface. It is also possible to identify those classes which have similar degrees of use and
cover from the perspective of the interpreted primitives. More importantly, using the
primitives it is possible to generate weights that relate to the degree of land cover or land
use and the intended application (e.g. data integration). For example, data users would be
able to reclassify the classes of an existing data base based on their understanding of the
essential data primitives related to their application, identifying relevant land use
categories. Equally, other applications of primitives could be used to generate maps that
highlight uncertain areas in terms of their use or cover classification. An investigation of
spatial patterns would reveal the extent of any spatial autocorrelation aside from the
possible autocorrelation in the selected data primitives, as evident the clustering in
Figures 1d and 1e.
In this work the weights were generated from the perspective of an expert and for other
applications alternative weights may be generated from different expert perspectives
depending on the task in hand. The use of experts is problematic: opinion between
experts varies, they change their mind and may not give consistent opinions, their
reasoning is not always transparent and their time is scarce. For these reasons a number
of workers have explored the use of text mining approaches to determine the semantic
relations between spatial data concepts. Comber et al (submitted) and Wadsworth et al (in
prep) have used text mining with measures to weight the importance and overlap of each
term.
In conclusion, this paper has argued that the concepts of land cover and land use should
be separated and has presented a method based on the application of data primitives to do
this. It is acknowledged that some of the dimensions used in this work may be dependant
and not be orthogonal, they may overlap, they may be redundant for some workers.
However the aim of this work was to illustrate how such an approach could be applied
and used rather than proposing a definitive separation of use and cover. The separation of
land cover and land use facilities data integration modelling activities, etc and allows
existing data resources to be better utilised. It also allows the separation of the concepts
of form (land cover) and function (land use) which underpins much research in for
example urban planning, monitoring of resources and climate change modelling. The
fourteen data primitives suggested in this work are not intended to be definitive but to act
as an illustration of how capturing the essence of use and cover offers the potential to
separate these two concepts in existent data. The advantage (and possibly disadvantage)
of the proposed methodology is that it can be applied to any existing data set by any
worker. It is hoped that this will act as a starting point and promote discussion within the
land use and land cover communities about the nature of the primitives that are relevant
to different application areas and the need to maintain a land use and land cover as
distinct concepts in data collection activities.
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List of Tables and Figures
Table 1. The primitive scores for the NLUD classes
Table 2. The normalised Use and Cover scores for the NLCD classes, values greater than
the 50th percentile are highlighted
Figure 1. a) NLCD data for Connecticut b) weighted land cover c) weighted land use d)
land use and land cover with weights >50th percentile e) land use and land cover with
weights <50th percentile
NLCD
Code
11
12
21
22
23
24
31
32
33
41
42
43
51
52
61
71
72
73
74
81
82
85
90
91
92
Class Name
1
Open Water
Perennial Ice/Snow
Developed, Open Space
Developed, Low Intensity
Developed, Medium Intensity
Developed, High Intensity
Barren Land
Unconsolidated Shore
Transitional
Deciduous Forest
Evergreen Forest
Mixed Forest
Dwarf Scrub
Shrub/Scrub
Orchards/Vineyards/Other
Grassland/Herbaceous
Sedge/Herbaceous
Lichens
Moss
Pasture/Hay
Cultivated Crops
Urban/Recreational Grasses
Woody Wetlands
Palustrine Forested Wetland
Palustrine Scrub / Shrub Wetland
9
9
3
2
1
7
9
4
9
9
9
9
9
3
9
9
9
2
9
9
9
2
3
2
2
4
3
1
2
9
9
9
9
2
9
5
2
2
1
3
9
9
9
3
3
6
1
1
1
1
1
1
1
5
5
4
2
5
2
9
9
7
Table 1. The primitive scores for the NLUD classes
4
5
9
9
6
5
7
9
7
6
3
7
7
5
4
4
6
7
4
5
5
7
7
9
3
3
3
2
6
3
2
1
3
2
1
2
5
7
2
5
4
5
6
2
2
5
7
7
7
2
2
7
2
3
5
5
1
8
7
6
2
2
1
3
3
3
7
8
9
10
11
9
1
1
1
6
7
8
9
2
1
2
9
9
9
9
2
7
7
8
9
6
6
2
2
2
6
1
1
1
4
6
6
6
5
3
3
7
3
7
3
8
4
7
6
3
7
7
4
7
9
9
7
9
9
8
9
7
9
9
1
9
2
1
4
7
7
3
2
1
7
3
3
3
3
2
2
2
2
4
4
4
2
2
2
7
9
9
1
5
9
9
9
6
6
8
7
4
1
1
7
8
3
7
6
6
12
13
14
1
6
7
8
9
2
NLCD Code
11
12
21
22
23
24
31
32
33
41
42
43
51
52
61
71
72
73
74
81
82
85
90
91
92
Class Name
Open Water
Perennial Ice/Snow
Developed, Open Space
Developed, Low Intensity
Developed, Medium Intensity
Developed, High Intensity
Barren Land
Unconsolidated Shore
Transitional
Deciduous Forest
Evergreen Forest
Mixed Forest
Dwarf Scrub
Shrub/Scrub
Orchards/Vineyards/Other
Grassland/Herbaceous
Sedge/Herbaceous
Lichens
Moss
Pasture/Hay
Cultivated Crops
Urban/Recreational Grasses
Woody Wetlands
Palustrine Forested Wetland
Palustrine Scrub / Shrub Wetland
Cover
0.862
0.976
0.204
0.105
0.068
0.976
0.832
0.376
0.39
0.866
0.771
0.793
0.237
0.447
0.544
0.11
0.215
0.426
0.426
0.16
0.229
0.097
0.725
0.749
0.647
Use
0.058
0.048
0.719
0.747
0.799
0.963
0.212
0.048
0.451
0.439
0.439
0.439
0.689
0.689
0.788
0.5
0.356
0.048
0.048
0.852
0.879
0.574
0.822
0.689
0.689
Table 2. The normalised Use and Cover scores for the NLCD classes, values greater than
the 50th percentile are highlighted.
Figure 1a.
Figure 1b.
Figure 1c.
Figure 1d.
Figure 1e.
Figure 1. a) NLCD data for Connecticut b) weighted land cover c) weighted land use d)
land use and land cover with weights >50th percentile e) land use and land cover with
weights <50th percentile