# A Landscape Approach for Detecting and Assessing Changes in an Area Prone to Desertification in Sardinia (Italy)

Authors: Francesca Giordano; Alberto Marini
Journal: International Journal of Navigation and Observation
 (2008)

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## Abstract

Land degradation and desertification processes represent a serious problem in many areas of Sardinia (Italy), as in the Nurra region where urbanization, overgrazing, and fires have induced environmental degradation and rapid land-use change. In this study, using satellite remote sensing and geographical information system, landcover and landscape change dynamics were investigated. Comparing two Landsat-5 Thematic Mapper, it was possible to assess landcover transformations, and with the FRAGSTATS software it was possible to quantify the changes of landscape characteristics in the Nurra region over a 10-years period. The images were classified into seven landcover types, and a stepwise indicator approach was adopted. The results show a decrease in cropland and an increase of forestland and urban areas. The overall change was estimated to be about 2.5% of the total study area, with two most frequent landcover conversion types: cropland to urban areas and cropland to forestland.


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## Body

## 1. Introduction

In the last two centuries, the impact of
human activities on the land has grown enormously, altering entire landscapes
with important ecological consequences like biodiversity loss, deforestation,
soil erosion, and desertification.According to article 1 of the United Nations
Convention to Combat Desertification (UNCCD) desertification is “land
degradation in arid, semi-arid and dry sub-humid areas resulting from various
factors, including climatic variations and human activities” [1]. Instead, land degradation is a reduction in the
biological and economic productivity of terrestrial ecosystems, including soil,
vegetation, and biota. Land degradation also disturbs the ecological,
biogeochemical, and hydrological processes.Before the presence of the man, natural processes shaped the landscape,
but after the man came to Earth, Earth’s face has changed. In the past two
centuries, the impact of human agricultural, industrial, and extractive
activities, combined with natural and human actions, induced climatic
variations which led to land degradation on an unprecedented scale.Due to its particular geographical
position and its extreme climatic variations such as droughts and floods, Sardinia can be considered a representative area of the typical environmental
problems of the Mediterranean basin. Desertification processes,
in particular, represent a serious threat to many areas on the island where the
impact of human activities on the environment is overlapped to the climatic
stress. In the last decades, the urbanization along the coastal areas has
strongly increased due to new tourist settlements and urban infrastructures.
Not only urbanization but also the loss of fertile soils, massive water
exploitation, overgrazing, and fires are the other important reasons for the
environmental problems in the Nurra region, the north-western part of the Island. Many scientific
projects such as DesertNet [2], RIADE [3], and DesertWatch [4] have previously
investigated this area with results demonstrating the high sensitivity to desertification of the area.The
objective of the present study was to detect, characterize, and quantify the
changes in terms of landcover and landscape in an area sensitive to land
degradation and desertification in the Nurra region. In the present study,
remote sensing techniques were applied in order to analyze landcover change
from 1990 to 2000 by Landsat Thematic Mapper TM images interpretation and to
assess landscape structure change by using FRAGSTATS, a spatial pattern
analysis program software package [5].


## 2. Study Area

The study area (ca40°43′ N, 8°34′ E) is located in the north-western part of Sardinia (Italy) (see Figure 1) and covers four
municipalities: Sassari,
Alghero, Stintino, and Porto Torres. This area of about 88400 ha is characterized by a
high geological and morphological complexity. The climate is typically Mediterranean dry-subhumid with abundant amount of rainfall during the autumn-winter period
and a scarce amount of rainfall with very high temperature during the summer
period. Mean annual precipitation values vary between 490 mm and 870 mm. The area belongs to the phytoclimatic area of Lauretum.Figure 1
Nurra and town of Sassari in Sardinia (Italy).As demonstrated from
recent studies [6], the area is strongly sensitive to land degradation and
desertification with more than half of the territory belonging to critical environmentally sensitive
areas (ESAs) or “areas already highly degraded through past misuse, presenting
a threat to the environment of the surrounding areas” [7].


## 3. Methods

Two
Landsat satellite Thematic Mapper TM images were selected over
the study area. The first image was a Landsat-5 Thematic Mapper of May 12, 1990, with
seven spectral bands and a spatial resolution of 30 m. The second one was a Landsat-5
Thematic Mapper of June 27, 2000. For the analysis of Landsat satellite images,
ERDAS Imagine 8.5 software was used [8, 9]. Landsat 5 TM of June 27, 2000 was
used as a reference image to geo-reference the other image within acceptable limits
(rms = 0.71 pixels for the 1990 scene), and the nearest neighbour algorithm was
used as the resampling method [10]. Atmospheric correction is not strictly
necessary when postclassification change detection is the method selected for
the analysis of changes. In this case, each image is individually classified
and then the resulting maps are compared [11].In this approach,
supervised  classification was used in order to perform a landcover map for each image. In this type of classification, the user selects the spectral signatures
defined from recognized locations in the image or “training sample.” The computer system then identifies the pixels with
similar characteristics and assigns them to a class based on specific criteria.
About 50 sample data were defined with help of ortophotos (scale1:10.000) and available maps. These
tools helped classify a seven-class legend: urban areas, forestland, shrubland, cropland, barren
land, wetlands, and water bodies [12]. Supervised classification was then
performed using the maximum likelihood
method, in which a pixel with the maximum likelihood is classified to
its corresponding class. The accuracy assessment showed that the overall
accuracy of the landcover maps approached 88% for the 1990 image and 92% for
the 2000 image. The existence of mixed pixels (pixels
having more than one class in their footprint), in particular among vegetation
classes, would require an analysis at a higher geometric resolution or a
comparison with multitemporal data to exploit the phenological curves. This
analysis would be useful in order to find if the classification accuracy could
further improve. After performing the landcover maps, the next step was to
overlay them and create a landcover change map (1990-2000).In order to perform the landcover
and landscape dynamics analysis of the study area, a stepwise indicator
approach was used to measure the various aspects of landcover and landscape and their change [13–15].Landcover maps represent the source oflandcover
indicators (1990 and 2000) and landscape metrics (1990 and 2000).In particular, landcover indicators aimed
at the evaluation of the extent of each landcover class in 1990 and in 2000 and
measured the direction of change (increase/decrease) of each class during the
study period.Landscape metrics measure the spatial
structure of landcover metrics in terms of composition (number, proportional
frequency, and diversity of landscape elements within the landscape) and
configuration (spatial position and distribution of the elements within the
landscape). In order to calculate landscape metrics, landcover maps were
converted into Grid format using ERDAS Imagine [8, 9] and introduced into the FRAGSTATS software [5].In particular landscape metrics at class
level, measuring the aggregate properties of the patches belonging to a single
class or patch type, and landscape metrics at landscape level, measuring the
aggregate properties of the entire patch mosaic, were performed.Landcover change map
(1990-2000) represents
the source oflandcover change indicators, which aim at the location of
the areas of change and at the evaluation of the extent of changes for the
total study area and for each landcover class. These indicators measure also
the landcover transition direction and the gains and losses for each class
during the study period.The entire  set of landcover
and landscape indicators has been setup in order to answer the following
questions. Where are the landcover changes (location)? Which is the magnitude
of landcover change (extension)? Which is the direction of landcover change
(direction)? Which are the spatial characteristics of landscape change
(structure)?


## 4. Results

### 4.1. Landcover Indicators

Once the classification
of each image was performed, and the landcover maps for each period of study were obtained, the first step was to
quantify each landcover class extension over the total area (see Table1).Table 1
Landcover areas per
classes in the study area, 1990 and 2000.
ClassArea in
1990ClassArea in
2000Direction of changeha%ha%Cropland67.209,576,0Cropland65.956,974,6decreaseForestland8.246,49,3Forestland9.088,610,3increaseShrubland7.528,88,5Shrubland7.487,88,5—Urban
areas4.033,24,6Urban
areas4.455,75,0increaseBarren1.064,21,2Barren1.118,61,3slight
increaseWetlands230,30,3Wetlands251,90,3slight
increaseWater
bodies75,90,1Water
bodies28,80,0decreaseTotal88.388,2100,0Total88.388,2100,0Cropland is largely the
dominant landcover type in the study area in both cases, followed by
forestland, shrubland, and urban areas. In particular during the period from
1990 to 2000, cropland decreased from 76% to 74.6%, while forestland and urban
areas increased in area (from 9.3% to 10.3% and from 4.6% to 5%, resp.)
and shrubland maintained the same dimensions. Furthermore, a sharp decrease of
water bodies occurred.The increase of urban areas, due to the
continuous urbanization process in the Nurra region during the last years, is
not a direct cause of the decrease in the area of water bodies. The variation
in water bodies is probably due
to the increase of riparian vegetation, with a consequent silting up of the
shallow waters, in particular along the coast of the Baratz lake.


### 4.2. Landscape Indicators at Class Level

Landcover maps were the
source for the landscape metrics computation at class level and at landscape
level by means of FRAGSTATS software [5].Table2 shows the most relevant landscape
metrics performed at class level for the entire study area and for the most
changing classes (cropland, forestland, and urban areas) [16–18]. For the
analysis and the comprehension of landscape metrics at class level, it is
advisable not to analyze just a landscape metric but rather a set of metrics to better understand
and describe the dynamics of ecosystems and landscape structures.Table 2
Landscape metrics at
class level, 1990 and 2000.
ClassCAPLANDNPPDAREA_MNSHAPE_AMPROX_MNIJI(ha)(%)(#)(#/100 ha)(ha)(ha)(m)(%)1990Cropland6721029,218300,836,821,85671063,7Forestland82463,651402,21,65,871,254,9Urban
areas40331,817750,82,35,9112,233,42000Cropland6595728,716600,739,717,66655069,8Forestland90883,941901,82,27,3212,261,0Urban areas44551,96890,36,55,9301,348,0Cropland in the study
area represents the major class of the landscape. The study shows a decrease of
cropland in terms of the percentage of landscape (PLAND, proportional abundance
of each patch type in the landscape), the number of patches (NP, extent of
subdivision or fragmentation of the patch type), and the patch density (PD,
number of patches on a per unit area) during the period from 1990 to 2000.
Instead, the area weighted mean shape index (SHAPE_AM, mean patch shape complexity
weighted by patch area) shows the highest values both in 1990 and 2000 for
cropland in the study area and shows a decline during the study period from 21.8
to 17.6 indicating a reduction in the shape complexity. Shape complexity relates
to the geometry of patches whether they tend to be simple and compact or
irregular and convoluted.In regards to the
forestland during the period 1990–2000, the number of patches (NP) decreased
from 5140 to 4190, the mean patch area (AREA_MN, average size of patches)
increased from 1.6 to 2.2, and the mean proximity index (PROX_MN, average
proximity index for all patches in a class) increased from 71.2 to 212.2. This indicates
that patches of the same type increasingly occupy the neighborhood between
forested patches, which is defined by 300 m, and those patches have become
closer and more contiguous in distribution. In this sense, the
forested landscape has become less fragmented [19].The analysis of urban
areas indicated that the number of patches (NP) significantly decreased from
1775 to 689, while the mean patch area (AREA_MN) increased from 2.3 to 6.5 and
the class area (CA, total area of all patches per class) increased from 4033 to
4455. This combination of results shows that new urban buildings generally
occur in the voids of the core or adjacent to existent urban patches indicating
that urban areas grow in a concentrated way [20]. Finally, the increase of the
mean proximity index (from 112 to 301) and of the interspersion and
juxtaposition index (from 33,4 to 48,0) of urban class shows a more uniform
landscape configuration. The first index indicates an increase in the proximity
of urban patches, which has become closer and more contiguous in distribution.
As for the second index, it measures an increase of the interspersion (or
intermixing of patch types) of urban patches (more equally adjacent to each
other).


### 4.3. Landscape Indicators at Landscape Level

Table3 shows the most
relevant landscape metrics performed at landscape level for the study area.Table 3
Landscape metrics at
landscape level, 1990 and 2000.
YearNPPDLPIAREA_MNSHAPE_AMPROX_MNIJIPRSHDISHEI(#)(#/100 ha)(%)(ha)(ha)(m)(%)———1990119605,228,47,417,9875055,970,850,44200098404,327,59,014,61139061,170,890,46As seen in Table3, during the decade 1990–2000
the patch number (NP) in the study area decreased (from 11960 to 9840), and the
mean patch area (AREA_MN) increased (from 7.4 to 9.0), showing a trend towards
an increasingly large-grained landscape.Shannon’s diversity
(SHDI) and evenness (SHEI) indices both became greater, showing that the
landscape heterogeneity and evenness slightly increased. Shannon’s
diversity index is, in fact, a popular measure of diversity in community
ecology, applied here to landscapes as a measure of the equitability of the number of patch
types and of the proportional distribution of area among patch types. Shannon evenness index is another popular diversity measure borrowed from community
ecology, indicating the evenness of the distribution
of area among the different patch types [5].The spatial context of landscape patches also had some
changes. For instance, the mean proximity index became greater (from 8750 to
11390) displaying that the spatial distribution of patches became more
continuous. In addition, the increase of
interspersion and juxtaposition index (from 55.9 to 61.1), together with the
increase of mean proximity index, indicated a more uniform landscape
configuration.


### 4.4. Landcover Change Indicators

From the overlaying of
the two landcover maps, a landcover change area (1990–2000) was estimated to be
about 2.5% of the total study area. Now, we do not have enough ground
information to be able to rule out errors in the classification. Considering that
there is 8%–12% error in classification accuracy, we expect to improve the
analysis through further ground points all over the changed areas in order to
increase the significance of the result.Two landcover conversions were the most
frequent in the study area: conversion from cropland to urban areas (45.7%) and
conversion from cropland to forestland (44.5%). In Sassari, Alghero, and Stintino
municipalities, the overlay of the landcover change map within the
administrative limits showed that the percentage of landcover was mainly
composed by cropland followed by forestland, shrubland, and urban areas. The
only difference found was in Porto Torres, where the landscape was dominated by
cropland followed by urban areas, which represented the second landscape class
in terms of spatial extension.The major landcover changes
occurred in the Sassari
municipality (3%), followed by the Alghero municipality (1.7%) and finally by
the Porto Torres and Stintino municipalities (1.2%). In particular, the
conversion from cropland to urban areas represented 52% of the changes in the Sassari
area, 26.3% of
changes in the Alghero area, and 61.7% of the changes in the Porto Torres area and, in conclusion,
45.6% of the changes in the Stintino area were related to the conversion from
cropland to forestland.


## 4.1. Landcover Indicators

Once the classification
of each image was performed, and the landcover maps for each period of study were obtained, the first step was to
quantify each landcover class extension over the total area (see Table1).Table 1
Landcover areas per
classes in the study area, 1990 and 2000.
ClassArea in
1990ClassArea in
2000Direction of changeha%ha%Cropland67.209,576,0Cropland65.956,974,6decreaseForestland8.246,49,3Forestland9.088,610,3increaseShrubland7.528,88,5Shrubland7.487,88,5—Urban
areas4.033,24,6Urban
areas4.455,75,0increaseBarren1.064,21,2Barren1.118,61,3slight
increaseWetlands230,30,3Wetlands251,90,3slight
increaseWater
bodies75,90,1Water
bodies28,80,0decreaseTotal88.388,2100,0Total88.388,2100,0Cropland is largely the
dominant landcover type in the study area in both cases, followed by
forestland, shrubland, and urban areas. In particular during the period from
1990 to 2000, cropland decreased from 76% to 74.6%, while forestland and urban
areas increased in area (from 9.3% to 10.3% and from 4.6% to 5%, resp.)
and shrubland maintained the same dimensions. Furthermore, a sharp decrease of
water bodies occurred.The increase of urban areas, due to the
continuous urbanization process in the Nurra region during the last years, is
not a direct cause of the decrease in the area of water bodies. The variation
in water bodies is probably due
to the increase of riparian vegetation, with a consequent silting up of the
shallow waters, in particular along the coast of the Baratz lake.


## 4.2. Landscape Indicators at Class Level

Landcover maps were the
source for the landscape metrics computation at class level and at landscape
level by means of FRAGSTATS software [5].Table2 shows the most relevant landscape
metrics performed at class level for the entire study area and for the most
changing classes (cropland, forestland, and urban areas) [16–18]. For the
analysis and the comprehension of landscape metrics at class level, it is
advisable not to analyze just a landscape metric but rather a set of metrics to better understand
and describe the dynamics of ecosystems and landscape structures.Table 2
Landscape metrics at
class level, 1990 and 2000.
ClassCAPLANDNPPDAREA_MNSHAPE_AMPROX_MNIJI(ha)(%)(#)(#/100 ha)(ha)(ha)(m)(%)1990Cropland6721029,218300,836,821,85671063,7Forestland82463,651402,21,65,871,254,9Urban
areas40331,817750,82,35,9112,233,42000Cropland6595728,716600,739,717,66655069,8Forestland90883,941901,82,27,3212,261,0Urban areas44551,96890,36,55,9301,348,0Cropland in the study
area represents the major class of the landscape. The study shows a decrease of
cropland in terms of the percentage of landscape (PLAND, proportional abundance
of each patch type in the landscape), the number of patches (NP, extent of
subdivision or fragmentation of the patch type), and the patch density (PD,
number of patches on a per unit area) during the period from 1990 to 2000.
Instead, the area weighted mean shape index (SHAPE_AM, mean patch shape complexity
weighted by patch area) shows the highest values both in 1990 and 2000 for
cropland in the study area and shows a decline during the study period from 21.8
to 17.6 indicating a reduction in the shape complexity. Shape complexity relates
to the geometry of patches whether they tend to be simple and compact or
irregular and convoluted.In regards to the
forestland during the period 1990–2000, the number of patches (NP) decreased
from 5140 to 4190, the mean patch area (AREA_MN, average size of patches)
increased from 1.6 to 2.2, and the mean proximity index (PROX_MN, average
proximity index for all patches in a class) increased from 71.2 to 212.2. This indicates
that patches of the same type increasingly occupy the neighborhood between
forested patches, which is defined by 300 m, and those patches have become
closer and more contiguous in distribution. In this sense, the
forested landscape has become less fragmented [19].The analysis of urban
areas indicated that the number of patches (NP) significantly decreased from
1775 to 689, while the mean patch area (AREA_MN) increased from 2.3 to 6.5 and
the class area (CA, total area of all patches per class) increased from 4033 to
4455. This combination of results shows that new urban buildings generally
occur in the voids of the core or adjacent to existent urban patches indicating
that urban areas grow in a concentrated way [20]. Finally, the increase of the
mean proximity index (from 112 to 301) and of the interspersion and
juxtaposition index (from 33,4 to 48,0) of urban class shows a more uniform
landscape configuration. The first index indicates an increase in the proximity
of urban patches, which has become closer and more contiguous in distribution.
As for the second index, it measures an increase of the interspersion (or
intermixing of patch types) of urban patches (more equally adjacent to each
other).


## 4.3. Landscape Indicators at Landscape Level

Table3 shows the most
relevant landscape metrics performed at landscape level for the study area.Table 3
Landscape metrics at
landscape level, 1990 and 2000.
YearNPPDLPIAREA_MNSHAPE_AMPROX_MNIJIPRSHDISHEI(#)(#/100 ha)(%)(ha)(ha)(m)(%)———1990119605,228,47,417,9875055,970,850,44200098404,327,59,014,61139061,170,890,46As seen in Table3, during the decade 1990–2000
the patch number (NP) in the study area decreased (from 11960 to 9840), and the
mean patch area (AREA_MN) increased (from 7.4 to 9.0), showing a trend towards
an increasingly large-grained landscape.Shannon’s diversity
(SHDI) and evenness (SHEI) indices both became greater, showing that the
landscape heterogeneity and evenness slightly increased. Shannon’s
diversity index is, in fact, a popular measure of diversity in community
ecology, applied here to landscapes as a measure of the equitability of the number of patch
types and of the proportional distribution of area among patch types. Shannon evenness index is another popular diversity measure borrowed from community
ecology, indicating the evenness of the distribution
of area among the different patch types [5].The spatial context of landscape patches also had some
changes. For instance, the mean proximity index became greater (from 8750 to
11390) displaying that the spatial distribution of patches became more
continuous. In addition, the increase of
interspersion and juxtaposition index (from 55.9 to 61.1), together with the
increase of mean proximity index, indicated a more uniform landscape
configuration.


## 4.4. Landcover Change Indicators

From the overlaying of
the two landcover maps, a landcover change area (1990–2000) was estimated to be
about 2.5% of the total study area. Now, we do not have enough ground
information to be able to rule out errors in the classification. Considering that
there is 8%–12% error in classification accuracy, we expect to improve the
analysis through further ground points all over the changed areas in order to
increase the significance of the result.Two landcover conversions were the most
frequent in the study area: conversion from cropland to urban areas (45.7%) and
conversion from cropland to forestland (44.5%). In Sassari, Alghero, and Stintino
municipalities, the overlay of the landcover change map within the
administrative limits showed that the percentage of landcover was mainly
composed by cropland followed by forestland, shrubland, and urban areas. The
only difference found was in Porto Torres, where the landscape was dominated by
cropland followed by urban areas, which represented the second landscape class
in terms of spatial extension.The major landcover changes
occurred in the Sassari
municipality (3%), followed by the Alghero municipality (1.7%) and finally by
the Porto Torres and Stintino municipalities (1.2%). In particular, the
conversion from cropland to urban areas represented 52% of the changes in the Sassari
area, 26.3% of
changes in the Alghero area, and 61.7% of the changes in the Porto Torres area and, in conclusion,
45.6% of the changes in the Stintino area were related to the conversion from
cropland to forestland.


## 5. Conclusions

The analysis carried
out showed that the landscape of the study area is dominated by cropland, followed
by forestland and shrubland, and urban areas. During the study period
(1990–2000) cropland decreased, while forestland and urban areas increased. The
dynamics of change revealed that forested landscape has become less fragmented
during the decade thus avoiding the breakup of natural areas into smaller and
more isolated units. As for the urban areas, they showed growth in a
concentrated way, as new urban buildings generally occurred in the voids of the
core or adjacent to existent
urban patches.The overall landcover change
in the area was estimated to be about 2.5% of the total study area, with two
most frequent landcover conversion types:(i)
conversion from cropland to urban areas (45.7%),(ii)
conversion from cropland to forestland (44.5%).
The major landcover changes
occurred in the Sassari
municipality (3%), followed by the Alghero municipality (1.7%).The present work
describes the analysis performed in terms of landcover and landscape change by
means of remote sensing and GIS techniques in an area prone to land degradation
and desertification in Sardinia (Italy) during the period 1990–2000. For this purpose, a set of landcover and
landscape indicators was setup,
and a quantitative characterization of changes was performed. The combination of Landsat data, GIS, and FRAGSTATS
software was generally helpful
in providing techniques to monitor landcover and landscape evolution during the
study period. The procedure adopted answered
some important question about the changes that occurred in the area: Where are
the changes? Which are the magnitudes of changes? Which are the directions of
changes? Which are the spatial characteristics? Further research is needed in
order to better understand the evolution of landcover and landscape in areas in
which land degradation and desertification processes are occurring.



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*Source: 549630-2008-04-15.xml*