# Terraced landscapes characterization. Developing a methodology to map and analyze the agricultural management impacts (Monte Pisano, Italy)

Davide Rizzo, Mariassunta Galli, Tiziana Sabbatini, Enrico Bonari

► To cite this version:

Davide Rizzo, Mariassunta Galli, Tiziana Sabbatini, Enrico Bonari. Terraced landscapes characterization. Developing a methodology to map and analyze the agricultural management impacts (Monte Pisano, Italy). Revue Internationale de Géomatique, 2007, 17 (3-4), pp.431-447. 10.3166/geo.17.431-447. hal-03581721

HAL Id: hal-03581721

https://hal.science/hal-03581721v1

Submitted on 7 Aug 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Terraced landscapes characterization
Developing a methodology to map and analyze the agricultural management impacts (Monte Pisano, Italy)

Davide Rizzo*, Mariassunta Galli, Tiziana Sabbatini, Enrico Bonari

Land Lab, Scuola Superiore Sant'Anna, via Santa Cecilia, 3 - 56127 Pisa - Italia

KEYWORDS:

Landmarks
Landscape management
Geographical Information Systems

ABSTRACT

Mediterranean terraced landscapes are mainly shaped by agricultural drainage systems. They are increasingly exposed to degradation because their management has been neglected since the 1960s. The aim of this research is to characterize slope-lands drainage systems, at the landscape scale, in a hilly Mediterranean area, in order to define action priorities for their "active conservation". The selected study area covers 62 km² on the southwest side of Monte Pisano (near Pisa, Tuscany, Italy) mainly dedicated to olive growing. The methodology is GIS-oriented and uses remote-sensing techniques. Main results are new management guidelines for these rural landscapes to ensure their agro-environmental functionalities.

MOTS-CLÉS:

Landmarks,
Gestion du paysage
Système d'Information Géographique

RÉSUMÉ

Les paysages méditerranéens de terrasse sont souvent façonnés par des systèmes de drainage agricole. Ils sont aujourd'hui fortement exposés à des dégradations du fait de changements et abandon progressifs de leur gestion à partir de 1960. L'objectif de la recherche présentée est de caractériser les systèmes de drainage des terrains en pente à l'échelle d'un territoire dans des secteurs de collines méditerranéennes, pour aider à définir des priorités d'interventions agro-nomiques, environnementales et socio-économiques pour préserver ces systèmes de drainage. La zone d'étude comprend 62 km², principalement couverte d'oliveraies, et située sur le versant sud-ouest du Monte Pisano (près de Pise en Toscane, Italie). La méthodologie mobilise le SIG et les techniques de la télédétection. Ceci permet de dégager des orientations de gestion de ces territoires pour assurer le maintien de leurs fonctions agro-environnementales.

## 1. Introduction

The word landscape is defined as «an area, as perceived by people, whose character is the result of the action and interaction of natural and/or human factors» (Council of Europe, 2000). The use of the word interaction focuses our attention to the continuous evolution of the character of each landscape (Antrop, 2005; Bürgi et al., 2004).

The agronomical sciences are increasingly contributing to the characterization of rural landscapes, especially concerning its evolution, driven by changes both in farm techniques and management and in the socio-economic framework (Deffontaines et al., 1995; Girardin and Weinstoerfer, 2003). In this perspective, the agronomical sciences have to operate at the territorial scale, rather than at the farm scale which is too small for the complexity of current landscape changes. This challenge implies cooperating with other disciplines to solve the main issues of territorial planning (Cavazza, 1996; Bonari et al., 2006).

<footnote>* Corresponding author — E-mail address: ridavide@gmail.com</footnote>
The endless metamorphosis of farming systems is understandable if we apply the concept of landmark, meant as a landscape feature that makes cropping and farming systems detectable (Méjean et al., 1996; Bonari et al., 2006). Indeed, there is a matrix on the territory where different agricultural features coexist: the changing ones – transient landmarks– and the more lasting ones – permanent landmarks –. The Mediterranean farming systems are a clear example of this matrix, as in the case of terraced landscapes. Here transient landmarks are, for instance, land covers that change (from cultivated toward abandoned and vice versa) and permanent landmarks are the terraces and the other visible drainage structures (Ambroise et al., 1989; Blanchemanche, 1990; Pin-to-Correia and Vos, 2002; United Nation, 2003). These landmarks – especially the latter – may be interpreted, on the one hand, as management indicators of local farming practices (Girardin e Weinstoerfer, 2003) and, on the other, as a suitable frame for future perspectives of landscape preservation or modification (Antrop, 2005), offering a new approach to planning at a territorial level (Galli et al., 2006).

In this perspective, European terraced landscapes are an interesting case especially because there is a call for studies promoting their "active conservation" (Varotto, 2006). Although there are several studies targeted at the field scale those at the landscape scale are still weak. Furthermore, the characterization of these landmarks use data that are rather poor or unavailable for rural mar-ginal areas (Puissant, 2006) where terraced landscapes are frequently comprised.

In light of this preface, the aim of our research has been to characterize the main agricultural landmarks of a terraced landscape through remote sensing techniques. The work here is part of a larger project on the agronomic evaluation of the functional degradation risk.

## 2. The study area

The study area is on the southwest side of Monte Pisano (Tuscany, Italy). It extends for 62 km² (figure 1) in the province of Pisa and it includes three Municipalities – Calci, Buti and Vicopisano –. The reason for this choice has been the coexistence of a complex surface water drainage system – covering a big part of the hillside, even above 500 m – and the active interest of local governments in the management of the territory and in the prevention of erosion risk.

<figure><img src="image_2.png" /><figcaption>**Figure 1.** Location of the study area</figcaption></figure>

The altitude noticeably increases in the few kilometres between the Arno River and the highest peak of Monte Pisano (917 m). Consequently, the slope is quite steep, with an average value of 37%; more than four-fifths of the area is over 20%, which is considered the limit for the ordinary mechanisation of agricultural activities. The soils are prevalently the brown Mediterranean type,
lithosoils and leached soils and they are strongly affected in their pedogenetic processes by the steep slopes, especially in terms of erosion (Bonari and Silvestri, 1993). The average rainfall is about 1000÷1300 mm per year; its distribution follows a sub-Mediterranean pluviometric regime (Cavazza, 1975).

These pedo-climatic characteristics make surface water runoff regulation crucial to allow agri-cultural activities and to safeguard foothill settlements by ensuring land stability. Hence, similarly to many other hilly Mediterranean areas, slope structures have been re-shaped with the construction of intensive drainage systems (principally using dry-stone walls techniques) since the fourteenth century (Basso, 1975). However, since the 1960s, agricultural exploitation and territory management have been declining due to the dwindling number of farmers; this has determined a simplification of the farming systems and of the management practices, and the weakening of the interconnection among the different landscape portions (i.e. uphill woods, mid-hill olive groves and foothill crops; see Basso, 1975; Galli et al., 2003).

**3. Material and methods**

The characterization of agricultural terraced landscapes has been focused on the identification of geographical features coherently with the spatial scale of the whole terraced system and with territorial management aims. The methodology we have developed – and applied on the Monte Pisano – is represented in figure 2.

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Dataflow of the methodology for terraced landscape characterization</figcaption></figure>

All the steps have been performed in a Geographical Information System in order to organize, structure and elaborate heterogeneous information and data (i.e. qualitative and quantitative). Moreover, in the perspective of the landscape management, GIS has proven to provide an effective support to plan interventions, by localizing and prioritizing management needs (Peccol et al., 1996; Malczewski, 2000; Cohen and Mering, 2004).

**3.1. First step: to collect the existing knowledge**

The first step in our landscape characterization was gathering and storing the existing knowledge with respect to data types (i.e. topographic map, thematic layers, etc.) and attributes (i.e. referring scale, year of production, etc.). We then processed and introduced them in a common geographical reference system to obtain a comprehensive geodatabase, in order to cross-read different information. The most relevant kinds of data are described in the following list.

**Topographic maps:** the main component has been the vector edition elaborated by the Tuscan Region (CTR, scale 1:10,000, year 2001), which has provided contour lines and height points used to elaborate the Triangulated Irregular Model (TIN) with ArcGIS 9.0® (3D Analyst module). Roads, streamlines, ridges and the few dry-stone walls reported in this map have been inserted as "hard
breaklines". A digital elevation model (DEM) with a spatial resolution of 10m has been obtained
from the TIN and used to retrieve aspect and slope maps.

Thematic maps: the provincial master plan of Pisa (1:25,000 scale, year 2004) and the three municipal master plans (1:10,000 scale, years 2002-2004) have provided several helpful maps and documents to characterize geography, land uses and socio-economic aspects of the territory. The CORINE Land Cover database (CLC) has been consulted as the principal source of information about land cover; unfortunately, when the project has been developed there was just the 1:100,000 scale map. Finally, two land cover maps have been disposable, both of them at 1:25,000 scale representing the forests (Fornaciari and Marchetti, 1983) and the natural and semi-natural phytocoenosis (Bertacchi et al., 2004) but they did not reported data for the agricultural areas.

Aerial photographs: the most recent and detailed series (panchromatic, year 2002, nominal scale 1:7,500) were chosen to analyze the current situation. Other photos, such as the "Terraltaly" flight (real colours, year 1999, 1:10,000 nominal scale) and the G.A.I. flight (panchromatic, year 1954, 1:31,000 nominal scale) were used to retrieve complementary data. All the photo-prints were scanned at 1200dpi and orthorectified with the Photogrammetry suite Leica, using the DEM previously obtained and achieving a final resolution of 0.7m.

**3.2. Second step: to complete the terraced landscape characterization**

In the second step an evaluation of the geodatabase (obtained in the first step) was performed
in order to evaluate what data were needed to complete the definition of agricultural landmarks.
The available maps had a rather coarse resolution (i.e. vegetation maps); furthermore part of the
data did not cover all the study area, or they would not be joinable because their classes were not
harmonized, especially for the land cover. Finally, data on the terraced system were quite poor.

Hence we have chosen to complete the geodatabase with information for the surface drain-
age works, the agricultural land cover and the state of cultivation. The main issue was identify the
proper geographical scale to characterize the terraced landscape as a system, while keeping
enough detail to face the study of its management. As referring scale we have chosen 1:10,000,
usually adopted for master plans for the territory management. A wider scale (e.g. 1:25,000) would
has been presented a minimal mappable unit too large to survey the landmarks associated with
the drainage system. Instead, a bigger scale (e.g. 1:5,000 or 1:2,000) would be preferable for a
more advanced analysis; it can be excessively time-consuming for a first framing of a terraced sys-
tem.

The data were gathered through remote sensing techniques. Many satellite images ordinarily
accessible have proved to have a too coarse resolution to survey the highly fragmented agricul-
tural land cover of Mediterranean marginal areas (Cohen and Shoshany, 2002; Allen et al. 2006).
Moreover, the surface drainage works have added specific troubles because they are modifica-
tions of the hilly-mountainous topographic profile, so requiring three-dimensional viewing. Therefore
we have chosen a mirror stereoscope for the acquisition of these data. Landscape features identi-
fied through the mirror stereoscope (Topcon MS-3) were edited in an Arc-Info station (ESRI software)
on the same orthorectified photos, in order to reduce the relief displacement errors.

In the photointerpretation process a first phase has been dedicated to the operator's training
through field surveys. At the end of the process we have checked the precision of the interpreta-
tion by surveying the ground truths with some transect in a different area from those used for the
training. We then calculated the accuracy (Congalton, 1991; Lillesand et al., 2004) using these da-
ta.

**3.2.1. Surface Drainage Works**

Data concerning the different agricultural surface drainage works (hereafter called SDW) are
preliminary to any decisions on the management of terraced areas; but a proper methodology for
their characterization is still poorly defined in the literature (Brancucci et al., 2000; Varotto, 2006).
Before the photointerpretation, some field surveys were performed to define the features to identify the SDW classes, each one determined case-by-case by soil texture, parcel slope and farmer choices (Landi, 1984; Baldeschi, 2000; Grove and Rackham, 2001; Bounatsou et al., 2005) and to collect ancillary data regarding the determination of each class. The selected features were level, bank and ditching (fig. 3).

<figure><img src="image_4.png" /><figcaption>**Figure 3.** Standard section of a surface drainage work (SDW) showing its basic features (redrawn and simplified from a sketch by Casella F.)</figcaption></figure>

The level (called in Italian *lenza*) is the cultivable space; its width and regularity in the study area may range from 1m to 10m or more, with a coarse average of about 2m as reported by local experts and verified during field surveys.

The bank (called in Italian *argine*) is the vertical part of the SDW; it can be held either by turf or by dry-stone walls. The structure and the shape of the bank may be linear or fragmented.

The ditching is a crucial component in the management and conservation of the SDW; nonetheless it is difficult to detect it through remote sensing. In fact, it comprises several forms of ditches with different level of intensity. Sometimes they are very reduced or subsurface, but ordinarily they may range from a simple ditch at the foot of the bank to a small channel (also called in Italian *acquidoccio*) which drains water for entire parcels, ordinarily orthogonal to the contour lines and built with stones (usually in those areas where the bank is made of stones as well).

These three features allow us to distinguish between intensive and extensive SDW, the former being more structured than the latter. The intensive SDW are divided in the literature in bench terraces, pocket terraces, *gradonamenti* and taluses. In terms of photogrammetric characteristics, these classes have been defined as described in table 1. In this study we have excluded a *priori* the forest SDW because they are too difficult to be recognized on the aerial photos due to the dense canopy of the trees.

In conclusion, the photointerpretation of SDW has provided two kinds of maps: (a) a polygonal one to localize classes of SDW; (b) a linear one to trace the dry-stone walls. The latter has been further processed to achieve an exploratory evaluation of the SDW density. For this computation, the linear meters of dry-stone walls have been referred to a square-mesh with a unit side of 10m, constructed using the "Repeating shapes" extension for ArcGIS 9.0 (Jenness, 2006). This measure has allowed us to obtain squares comparable with the resolution of the raster maps in the geodatabase.
<table><caption>**Table 1.** Classes of SDW and description of their photogrammetric characteristics as function of their basic features.</caption><thead><tr><th>Class of SDW</th><th>photogrammetric characteristics</th><th></th></tr></thead><tbody><tr><td>a. bench terraces</td><td>The level is narrow but with regular width, predominantly parallel to the contour lines and well recognizable because the bank is frequently a dry-stone wall. The ditching is so intensive and detailed that makes the shape of each single terrace sharper. If used for fruit crops, they ordinarily host just one or two rows of trees.</td><td></td></tr><tr><td>b. other intensive SDW</td><td>This class comprises SDW such as pocket terraces, taluses and other forms, locally defined by the micro-topographical conditions.<br>Pocket terraces. The level is small, not linear and fragmented; it is limited to a single tree. The bank, a dry-stone wall, creates a pot around the roots of each tree. No ditching is perceivable.</td><td></td></tr><tr><td></td><td>Gradonamenti. The level is large and has an irregular width; the bank is normally not tall, so perceivable as a small shadow. No ditching is detectable. When used for fruit crops, they can host several rows of trees.</td><td></td></tr><tr><td></td><td>Taluses. The levels are not linear and very fragmented, albeit hosting several trees; their width is very irregular. The banks are held-up by turf, so shadows are pretty blurred. The ditching is reduced or almost absent. They may host from one to several rows of plants.</td><td></td></tr><tr><td>c. other extensive SDW</td><td>None of the previous photogrammetric features; perceivable as sparse and narrow linear features (ditches), predominantly in the arable lands.</td><td></td></tr></tbody></table>

### 3.2.2. Land cover

The land cover has been mapped at the 1:10,000 scale by implementing and detailing all the existing data. The classes have been defined on the CLC standard nomenclature, following the legend key of the CLC fifth level proposed for its implementation in Italy (MATT, 2002). The different classes have been grouped into the following main typologies: artificial surfaces (urban fabric, industrial units, quarries, artificial non-agricultural vegetated areas); agricultural areas (arable land, permanent and mixed crops, pastures); natural and semi-natural areas (shrubs, Mediterranean maquis, woodlands and forest plantations, outcrops); water bodies.

### 3.2.3. State of cultivation

The survey of the state of cultivation has been meant to stress some agricultural management aspects; in particular, our working hypothesis has been that a correlation exists between this parameter and the condition of SDW structures (not directly detectable at the meso-scale). In other words, damaged SDW (e.g. falling dry-stone walls) would be more likely to be associated with abandoned cultures. Thus, by stressing the cultural condition it might be possible to infer if SDW are prone to be managed or not.
Operatively, the state of cultivation was reported during the photointerpretation, adding two specific classes to the land cover legend: fallow lands and abandoned olive groves. In this regard the CLC standard nomenclature associates all the abandoned crops to the semi-natural areas (Bossard et al., 2000). Though this is acceptable for fallow lands, a few words must be spent on fruit crops. In the agronomic perspective, adopted throughout this work, it has been necessary to divide the "abandoned olive groves" from the semi-natural areas, because olive groves can be recovered even after several decades of abandonment. In fact their long-lasting permanency makes them an important landmark; indeed, the other fruit crops (i.e. orchards and vineyards) seem to be less relevant because they have not been detectable after abandonment; therefore they were considered, when abandoned, part of the semi-natural areas.

The "abandoned olive groves" class has been defined with the expert knowledge of the area and field observation (cf. Wassenaar et al., 2002). It is characterized by the presence of olive trees (both as isolated specimens or heterogeneous rows) where the rest of the parcel is prevalently covered by shrubs (e.g. ferns, thorn bushes, etc.). Stereo pairs of aerial photos has been relevant, thanks to the stereovision, to recognize and locate abandoned olive groves by identifying the presence of olive trees rising-up from the under-shrubs vegetation which has grown after the abandonment. Field surveys have been required to improve the distinction between actual abandonment and the effects of more or less intensive farming practices (cf. Cohen and Mering, 2004). Finally, olive trees have allowed us to distinguish abandoned olive groves from other fallow lands with a different cultural precession.

### 3.3. Third step: data integration strategy

The third step of the research integrates the data gathered in the second step with the previous knowledge in order to achieve a single cognitive framework. On this basis, a cross-comparison of the different layers has been performed as function of their spatial attributes in order to proceed from landscape characterization toward the evaluation of the landscape condition.

## 4. Results and discussion

### 4.1. Surface Drainage Works

The results of the photointerpretation of the SDW are reported in table 2. The majority of the intensive SDW are bench terraces (66.2%), while the rest are taluses, pocket terraces and gradonamenti (10.4%). Other SDW cover 23.4% of the total agricultural surface.

<table><caption>**Table 2.** Distribution of the agricultural surface drainage works (SDW): extent and proportion on the total agricultural surface (TAS) of each class. Overall accuracy: 85.0%; kappa coefficient: 0.60.</caption><thead><tr><th rowspan="2">Classes of SDW</th><th rowspan="2">ha</th><th rowspan="2">% on TAS</th><th colspan="2">Accuracy report (%)</th></tr><tr><th>producer</th><th>user</th></tr></thead><tbody><tr><td>bench terraces</td><td>1,200.5</td><td>66.2</td><td>97.2</td><td>85.0</td></tr><tr><td>other intensive SDW</td><td>189.2</td><td>10.4</td><td>48.3</td><td>84.5</td></tr><tr><td>other extensive SDW</td><td>423.2</td><td>23.4</td><td>83.2</td><td>86.2</td></tr></tbody></table>

The performance of the photointerpretation process has shown to be rather constant and satisfactory for the user accuracy, while there are some differences in producer accuracy. In this regard bench terraces have resulted to be easily identifiable, while there is some uncertainty for the other intensive SDW. We suppose the low producer accuracy of this class (48.3%) has been due to their remarkable plasticity accentuated by the steepest morphology of the study area. Nevertheless, it could be possible to differentiate better among the intensive SDW (i.e. taluses, pocket terraces and gradonamenti) with a little more specific training. For instance, field surveys have shown
the relevance of the spatial distribution in the identification of each class of SDW. To give an example, the pocket terraces are not widespread: their presence is linked to specific areas, such as slope-lands with very shallow soils and steep slopes.

Finally, photointerpretation has been dedicated to digit linear features of SDW. It has been found that dry-stone walls extend for 1,933 kilometres; on the total agricultural area it results in an average density of 1048m/ha, locally ranging from 1.4 to 583.1 m/100m².

## 4.2. The land cover

The surveyed area of Monte Pisano has an agricultural land cover of 1,813 ha. Remaining 70% of land cover are woods and other semi-natural classes (3,977 ha), urban fabric (366 ha), rivers and outcrops (61 ha). The details of the agricultural area are in table 3. We can observe that most of the total agricultural surface was fruit crops, particularly olive groves (72.6%). Less important are arable lands (12.3%) and fallow lands (7.4%).

<table><caption>**Table 3.** Proportional distribution of the land cover on the total agricultural surface (TAS). Overall accuracy: 98.5%; kappa coefficient: 0.84.</caption><thead><tr><th colspan="2">Agricultural land cover classes</th><th>ha</th><th>% on TAS</th><th colspan="2">Accuracy report (%)</th></tr><tr><th></th><th></th><th></th><th></th><th>producer</th><th>user</th></tr></thead><tbody><tr><td rowspan="3">Arable lands</td><td>cereals</td><td>218.4</td><td>12.0</td><td rowspan="3">96.3%</td><td rowspan="3">100%</td></tr><tr><td>vegetables</td><td>4.7</td><td>0.3</td></tr><tr><td>olive groves</td><td>1,316.7</td><td>72.6</td></tr><tr><td rowspan="2">Fruit crops</td><td>orchards</td><td>5.1</td><td>0.3</td><td rowspan="2">99.0%</td><td rowspan="2">99.4%</td></tr><tr><td>vineyards</td><td>66.5</td><td>3.7</td></tr><tr><td rowspan="2">Other crops</td><td>poplar groves</td><td>5.9</td><td>0.3</td><td rowspan="2">90.2%</td><td rowspan="2">100%</td></tr><tr><td>mixed crops</td><td>47.8</td><td>2.6</td></tr><tr><td>Pastures</td><td></td><td>13.6</td><td>0.7</td><td>100%</td><td>63.5%</td></tr><tr><td>Fallow lands</td><td></td><td>134.3</td><td>7.4</td><td>74.9%</td><td>52.7%</td></tr></tbody></table>

Regarding the performance of the photointerpretation, the results have been quite satisfactory for both arable lands and fruit crops. Some misclassification of pastures, wrongly included among the fruit crops, has reduced the user accuracy of this class. This can be explained by the time lag between the date of the photos (year 2002) and those of the field surveys (2005). In fact, some fields have been identified as pastures because abandoned groves have been recuperated meanwhile and old trees have been cut down. Finally, some fallow lands have been misclassified as fruit crops.

## 4.3. The state of cultivation

The state of cultivation allows to analyze some management details of the land cover. As shown in table 4, about one-fifth of the agricultural area is completely abandoned. In particular, results have been divided between abandoned and cultivated crops; the latter are an aggregation of land cover classes (presented in the previous paragraph) given as term of comparison. It is interesting to notice that the 14.4% of the abandoned area consists of olive groves and 7.4% of fallow lands. That means olive groves represent two-thirds (66%) of the abandoned land. The good overall accuracy has been partially reduced by some confusion between fallow lands and abandoned olive groves, due to the presence of some trees in the former, misclassified as olive trees.
<table><caption>**Table 4.** Classes of state of cultivation and their proportion (%) on the total agricultural surface (TAS). Overall accuracy: 90.7%; kappa coefficient: 0.63. *Permanent crops are the sum of olive and poplar groves, vineyard and orchards, mixed crops and pastures.</caption><thead><tr><th rowspan="2">State of cultivation</th><th rowspan="2"></th><th rowspan="2">ha</th><th rowspan="2">on TAS</th><th colspan="2">Accuracy report</th></tr><tr><th>producer</th><th>user</th></tr></thead><tbody><tr><td rowspan="2">Abandoned</td><td>olive groves</td><td>260,3</td><td>14,4%</td><td rowspan="2">65.4</td><td rowspan="2">71.4</td></tr><tr><td>fallow lands</td><td>134,3</td><td>7,4%</td></tr><tr><td rowspan="2">Cultivated</td><td>permanent crops*</td><td>1,199.8</td><td>66.2%</td><td rowspan="2">95.3</td><td rowspan="2">93.8</td></tr><tr><td>arable lands</td><td>218.5</td><td>12.0%</td></tr></tbody></table>

### 4.4. Integrating the data

The distribution of the SDW classes per type of land cover, aggregated on the base of the state of cultivation, is shown in table 5. Noticeably, 96% of the bench terraces are used for permanent crops and more than one-fifth is abandoned (22.9%). The association of intensive SDW and arable lands is quite reduced. Finally, the different distribution of fallow lands, preferentially associated to intensive SDW different from bench terraces, may be underlined.

<table><thead><tr><th rowspan="2">SDW</th><th colspan="2">% Abandoned</th><th colspan="2">% Cultivated</th></tr><tr><th>olive groves</th><th>fallow lands</th><th>permanent crops*</th><th>arable lands</th></tr></thead><tbody><tr><td>Bench terraces</td><td>19.2</td><td>3.7</td><td>77.0</td><td>0.1</td></tr><tr><td>Other intensive</td><td>12.8</td><td>7.8</td><td>76.7</td><td>2.7</td></tr><tr><td>Other extensive</td><td>15.6</td><td>3.7</td><td>30.7</td><td>50.0</td></tr></tbody></table>

<caption>**Table 5.** Distribution of SDW on TAS presented as function of main aggregated land cover classes.</caption>

Results from this elaboration are intended as an exploratory evaluation of the SDW abandonment at the meso-scale. In fact, the re-setting of the knowledge framework through the integration between existing knowledge and newly acquired data highlights the need for more elaboration in order to evaluate the actual management level of SDW.

The field surveys have shown that the analysis of the state of cultivation is able to describe only part of the SDW state of management. Indeed, SDW have several small damages as well in the cultivated olive groves; these damages derive from a widespread and long-lasting inefficient either total lacking management that would require specific field measurements to be detected. Moreover, the consequences of the degradation of the permanent landmarks are difficult to be spatially inferred, even if they surely influence the state of the whole terraced landscape (Baldeschi, 2000; Brancucci et al., 2000; Schoorl and Veldkamp, 2001).

In this perspective our main focus in the ongoing data analysis is the assessment of the propensity of the terraced systems to degrade by using the obtained geographical information to characterize the transient and the permanent landmarks while focussing on the drivers of the abandonment. This assessment shall be addressed to highlight the critical issues of the landscape management, by identifying driving forces and defining suitable management practices for the future.

### 5. Conclusions

This research has studied the relationships between agricultural landscapes and territorial management, focusing on terraced landscape landmarks. In this perspective, we have developed a cognitive framework to characterize the landscape state through the quantitative analysis of landmarks using remote sensing techniques. In conclusion, four main topics are highlighted, both in terms of methodology and results, and we debate the perspective in the use of the results as a ba-

<footnote>*Permanent crops are the sum of olive and poplar groves, vineyard and orchards, mixed crops and pastures.</footnote>
sis for further qualitative and quantitative assessments of the terraced landscape state (ongoing fourth step: to manage).

First of all, the choice of the proper scale of analysis. We have chosen a meso-scale (i.e. 1:10,000) as the level for problem-solving and decision-making by the administrative bodies and the environmental bureaus. In agronomical sciences, as in other sciences concerned with the study of the territory, the limit and the advantages of the meso-scale are still poorly evaluated. The research presented in this paper is meant to arouse a scientific debate on approaches being used/developed in the agricultural landscape management, above all regarding the terraced ones (e.g. ALPTER and TERRISC projects).

Another topic is the database implementation; some constraints have been encountered while gathering and completing the cognitive framework, due to the heterogeneous nature of all the existing documents, both for qualitative (i.e. quality of attributes) and quantitative aspects (i.e. referring scales). This might be a real bottleneck in marginal areas, where the management interests and needs have to cope with a lack of coordination among the different research and administrative levels, resulting in gaps within the knowledge network. For this reason, it is important to start the research by structuring the available resources and further adapting and integrating them for the specific research purposes, by considering constraints of time and financial resources as well. In these terms, the three research steps – collecting, completing and integrating – and their interpretation through the landmark concept may represent an applied example in the agronomical operative approach to complex systems.

Our research has focussed on the identification of synthetic management indexes to arrange the results, making them readable in terms of incidence of the functional degradation risk. In other words, further efforts are required to integrate the spatial and descriptive attributes of the SDW, and then to evaluate the performances of the systems (i.e. the role of the decreasing/changed management practices). A research hypothesis could be to develop an index founded on structural and farming management parameters. The former have meant both as the geo-physical parameters describing the natural characteristics of a territory which are not directly changeable by human activities (primary structural parameters, e.g. aspect, geological matrix, etc) and as the permanent effects produced by human-nature interactions (secondary structural parameters, e.g. terraces and other permanent landmarks). The latter takes into account the effects of the agricultural practices (e.g. land cover and state of cultivation). The resulting index could assess the SDW propensity to functional degradation, helping to map potential risks at the administrative scale and to define intervention priorities.

Lastly, the identification of effective tools for terraced landscapes management. Even if the GIS approach goes in this direction, because it could be implemented in the routine work of local territorial programming, field surveys and meetings with local stakeholders have raised the requirement for more communication of results and for easily implementing research outputs. These demands could be answered delivering the results as a handbook for the management of terraced landscapes; it could be produced, for instance, by exploiting the field surveys performed either in the setting-up and the accuracy evaluation of the photointerpretation process.

## Acknowledgements

This work is part of a project funded by the Fondazione Cassa di Risparmio of Pisa. The authors would also like to thank the technicians of the three Municipalities for their precious transmission of local knowledge and particularly Fabio Casella for his schemes of terraces. Finally, our gratitude to Elisa Marraccini for the text revision and for her kind and useful advices.
## References

Allen H. D., Randall R. E., Amable G. S., Devereux B. J., "The impact of changing olive cultivation practices on the ground flora of olive groves in the Messara and Psiloritis regions, Crete, Greece", Land Degradation & Development, vol. 17 no. 3, 2006, p. 249-273. https://doi.org/10.1002/ldr.716

Ambroise R., Frapa P., Giorgis S., Paysages de terrasses, Aix-en-Provence, Edisud, 1989.

Antrop, M., "Why landscapes of the past are important for the future?", Landscape and Urban Planning, vol. 70 no. 1-2, 2005, p. 21-34.
https://doi.org/10.1016/j.landurbplan.2003.10.002

Baldeschi P., Il Chianti fiorentino, un progetto di tutela del paesaggio, Bari, Laterza, 2000.

Basso M., "L'agricoltura dei Monti Pisani", in Scaramuzzi G. (Ed) Per una valorizzazione dei Monti Pisani, Pisa, Pacini Editore, 1975.

Bertacchi A., Sani A., Tomei P.E., La vegetazione del Monte Pisano, Felici Editore, Pisa, 2004.

Blanchemanche Ph., Bâtisseurs de paysages: terrassement, épierrement et petite hydraulique agricole, XVIIe-XIXe siècles, Paris, Maison des sciences de l'homme, 1990.

Bonari E., Galli M., Rizzo D., "Gestione del territorio rurale e paesaggio", in Lorenzi R., Semprini M.P. (Eds) La tutela del paesaggio tra economia e storia, Firenze, Alinea Editrice, 2006, p. 47-56.

Bonari E., Silvestri N., "La stima dei rischi di erosione in provincia di Pisa", in Bonari E. (Ed) Coltivazioni erbacee e rischi ambientali in provincia di Pisa, Pisa, Centro Studi Economico-finanziari, 1993, p. 115-168.

Bossard M., Feranec J., Otahel J., CORINE land cover technical guide - Addendum 2000. European Environmental Agency - technical report no. 40, available at
http://www.eea.eu.int, 2000.

Bounatsou B., Vaitis M., Dalaka A., Petanidou T., "The contribution of geo-informatics to the study of the abandonment of terraces on Lesvos (Greece)", Proceeding of the Bioscene international conference, Ioannina (Greece), 20-25 September 2005.

Brancucci G., Ghersi A., Ruggiero M.E., Paesaggi liguri a terrazze: riflessioni per una metodologia di studio, Firenze, Alinea Editrice, 2000.

Bürgi M., Hersperger A.M., Schneeberger N., "Driving forces of landscape change - current and new directions", Landscape ecology, vol. 19 no. 8, 2004, p. 857-868.

Cavazza L., "Agronomia aziendale e agronomia del territorio", Rivista di Agronomia, vol. 30, 1996, p. 310-319.

Cavazza S., "Idrogeologia dei Monti Pisani", in Scaramuzzi G. (Ed) Per una valorizzazione dei Monti Pisani, Pisa, Pacini Editore, 1975, p. 43-97.

Cohen M., Merig C., "Geographic Information System and spatial Effect Applied to the study of the relationship between dynamics of ligneous cover and sheep breeders' practices", Cybergéo, no. 272, 2004, p. 1-17.
https://doi.org/10.4000/cybergeo.3616

Cohen Y., Shoshany M., "A national knowledge-based crop recognition in Mediterranean environment", International Journal of Applied Earth Observation and Geoinformation, vol. 4 no. 1, 2002, p. 75-87. https://doi.org/10.1016/S0303-2434(02)00003-X

Congalton R.G., "A review of assessing the accuracy of classification of remotely sensed data", Remote sensing of environment, vol. 37, 1991, p. 35-46.
https://doi.org/10.1016/0034-4257(91)90048-B

Council of Europe, "European Landscape Convention", European Treaty Series, no. 176, available at
http://conventions.coe.int, 2000

Deffontaines, J.P., Thenail, C., Baudry, J., "Agricultural system and landscape patterns: how can we build a relationship?", Landscape and Urban Planning, vol. 31 no. 1-3, 1995, p. 3-10.
https://doi.org/10.1016/0169-2046(94)01031-3

Fornaciari M., Marchetti, M., "Carta Forestale Sperimentale dei Monti Pisani", Inventario Forestale della Toscana, Dipartimento Agricoltura e Foreste, Regione Toscana, 1983.

Galli M., Pieroni P., Brunori G., "Un'esperienza di ricerca partecipativa per la valorizzazione degli spazi rurali nel comprensorio del Monte Pisano", Estimo e territorio - Rivista di scienze ambientali, vol. LXVI no. 3, 2003, p. 54-63.

Galli M.; Rizzo D., Bonari E., "Le funzionalità paesaggistiche dell'attività agricola", Agribusiness Paesaggio & Ambiente, vol. IX no. 1, 2006, p. 67-74.
https://doi.org/10.5281/zenodo.15252643

Girardin, Ph. & Weinstoerfer, J., "Assessment of the Contribution of Land Use Pattern and Management of Farming Systems to Landscape Quality: a Landscape Indicator", NIJOS rap-port, vol. 7(03), 2003, p. 67-81.

Grove, A.T. & Rackham, O., The Nature of Mediterranean Europe: an ecological history, Oxford, Yale University Press, 2001.

Jenness J., Repeating shapes for ArcGIS, Jenness Enterprises, available at:
http://www.jennessent.com/arcgis/repeatShapes.htm, 2006

Landi R., "Regimazione idraulico-agrarie e conservazione del suolo", Rivista di Agronomia, no. 18, 1984, p. 147-174.

Lillesand M.T., Kiefer R.W., Chipman J.W., Remote sensing and image interpretation, New York, John Wiley & Sons, 2004.

Malczewski J., GIS and multicriteria decision analysis, New York, John Wiley & Sons, 2000.

MATT - Ministero dell'Ambiente e della Tutela del Territorio, Manuale delle linee guida per la redazione dei piani di gestione dei siti Natura 2000, available at
http://www2.minambiente.it/scn/records/?section=downlo-ad, 2002.

Méjean P., Vignon B., Benoît M., "Étude des critères d'appréciation des acteurs du paysage dans trois espaces agricoles lorrains", Espace Géographique, vol. 25 no. 3, 1996, p. 245-256.

United Nation, Promotion of Traditional Knowledge A Compilation of UNCCD Documents and Reports from 1997 to 2003, available at
www.unccd.int/publicinfo/pubblications/docs/traditional_knowledge.pdf

Peccol E., Bird C.A., Brewer T.R., "GIS as a tool for assessing the influence of countryside designations and planning policies on landscape change", Journal of Environmental Management, vol. 47 no. 4, 1996, p. 355-367.
https://doi.org/10.1006/jema.1996.0059

Pinto-Correia T., Vos W., "Multifunctionality in Mediterranean landscapes - past and future", in Jongman R. (Ed.) The new dimensions of European landscapes. Wageningen UR Frontis Series, vol. 12, 2002, p. 135-164.

Puissant A., Malet J.-P., Maquaire O., "Mapping landslide consequences in mountain areas", Actes du Colloque Sagéo, Strasbourg, 11-13 September 2006.

Schoorl J.M., Veldkamp A., "Linking land use and landscape process modelling: a case study for the Álora region (south Spain)", Agriculture, Ecosystem and Environment, vol. 85 no. 1-3, 2001, p. 281-292. https://doi.org/10.1016/S0167-8809(01)00194-3

Varotto M., "Progetto AlpTer: i paesaggi terrazzati da problema a risorsa", Atti del convegno I terrazzamenti risorse del territorio, available at
http://www.alpter.net/documents.html, 2006.

Wassenaar T., Robbez-Masson J.-M., Andrieux P., Baret F., "Vineyard identification and description of spatial crop structure by per-field frequency analysis", International Journal of Remote Sensing, vol. 23 no. 17, 2002, p.3311-3325.
https://doi.org/10.1080/01431160110076144