Convergence or divergence in desertification risk? Scale-based assessment and policy implications in a Mediterranean country

Salvati, Luca; Zitti, Marco

doi:10.1080/09640560903181220

Cited by 26 publications

(14 citation statements)

References 58 publications

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“…This observation is in line with the results illustrated in previous studies which classify some areas of northern Italy as exposed to the desertification risk as many southern areas (e.g. Salvati and Zitti 2009c). This analysis was applied separately to the same indicators calculated for three geographical belts: northern, central and southern Italy.…”

Section: Resultssupporting

confidence: 67%

“…It was found that the economic determinants of LD act differently in northern and southern Italy according to the contrasting development paths that have characterised the two areas during the last 50 years at least (Salvati and Zitti 2008b). Together with the economic weaknesses of southern Italy due to the 'development deficit' which is shared with other Mediterranean areas (King et al 2001), the present study shows evidence of an 'environmental divide' between northern-central Italy and southerninsular Italy, as the latter region shows, on average, a persistent increase in land surface vulnerable to degradation which is not comparable to that observed in the former area (Salvati and Zitti 2009c). The reasons why southern Italy remains one of the most disadvantaged regions in the European Union are extremely complex as they depend on path-dependent economic factors that are difficult to reverse (e.g.…”

contrasting

confidence: 67%

“…These processes were explored by using key indicators that cover a relatively large area and are made available at a detailed enough spatial scale. The use of the local district spatial unit in the analysis of the environmenteconomy nexus is relatively novel in LD studies and, more generally, in ecological economics (Salvati and Zitti 2009c). Despite some criticisms concerning the relevance of the LLMA district as a homogeneous region, this spatial unit shows appreciable features that fill the need for data integration in the two research fields (Dumanski et al 1998).…”

mentioning

confidence: 98%

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Economic growth vs. land quality: a multidimensional approach in Italy

Salvati

Zitti

2011

Journal of Environmental Planning and Management

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This paper evaluates the recent economic dynamics in Italy and their implications for a specified environmental topic - land quality and its vulnerability to degradation. As opposed to the traditional economic literature, centred on GDP level and changes, a multidimensional concept of 'economic growth' was developed in the present study by considering several variables (share of agriculture and industry on the total production, labour productivity by sector, per capita value added) within a Principal Component Analysis (PCA). This analysis was carried out with the aim of assessing diachronically the spatial association between (and, possibly, to clarify the implications of) the economic growth and the distribution of the vulnerable lands to degradation across nearly 700 Italian districts in the mid-1990s and mid-2000s. The geographical gradients and the socio-economic factors supposed to have a prominent role in this relationship were analysed by dividing the investigated area into three geographical belts with different environmental characteristics. PCA results indicate that the traditional north-south economic gap has not been significantly reduced throughout the last 10 years. Furthermore, coastal-inland and urban-rural gradients were revealed as crucial in the environment-economic transition. Finally, the implications of this nexus on land quality were discussed.economic growth, local district, multidimensional analysis, land quality, geographical gradient, Italy,

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Section: Resultssupporting

confidence: 67%

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confidence: 67%

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confidence: 98%

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Economic growth vs. land quality: a multidimensional approach in Italy

Salvati

Zitti

2011

Journal of Environmental Planning and Management

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“…Candidate responses to LD can be classified as 'broad' or 'narrow' spectrum based on the observed correlation with the local background and the overall level of desertification risk (e.g. Salvati and Zitti, 2009). Fire prevention, sustainable farming and protected areas were identified as broad-spectrum actions (Kosmas et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

A multidimensional statistical framework to explore seasonal profile, severity and land-use preferences of wildfires in a Mediterranean country

Salvati¹,

Ferrara²,

Mancino

et al. 2015

Int. Forest. Rev.

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This study analyses spatio-temporal patterns of wildfires in Greece using a multidimensional statistical framework based on non-parametric correlations, principal component analysis, clustering and stepwise discriminant analysis. Specifically, we assess the frequency, seasonal profile, severity and land-use type of 135 178 wildfires which occurred between 2000–2012 in Greece, one of the countries most affected by fire in Europe. Our results show that both the number of fires and the average size of the area covered by fire show a specific seasonal pattern with a marked increase during the dry season. Principal component analysis identifies three dimensions linked with the main type of land-use affected by the fires: (i) medium and large fires primarily affected landscapes composed of forests, mixed woodlands/shrublands and croplands; (ii) small fires mainly affected fragmented landscapes, i.e. those with mosaics of different crops, market gardens and non-vegetated, abandoned or marginal areas; (iii) fires affecting wetlands and pastures occurred particularly in late summer and showing medium-low severity. Hierarchical clustering highlights similarities in spatio-temporal patterns between fire indicators (ignition date, burnt land cover classes, fire size, fire density). K-means clustering allows us to distinguish between low-severity fires occurring in the wet season from intense and frequent fires occurring in the dry season but with distinct land-use selectivity. The research reported here contributes insight into the complexity of wild fires in the Mediterranean region and supports the design of more effective fire prevention measures including sustainable forest management practices and careful regional planning to minimise risk factors

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“…A multidisciplinary approach based on biophysical and socioeconomic factors is required to identify areas vulnerable to desertification. Several mathematical models have been developed across the world to delineate areas vulnerable to desertification (Dasgupta, Sastry, Dhinwa, Rathore, & Nathawat, ; Salvati & Zitti, ; Symeonakis, Karathanasis, Koukoulas, & Panagopoulos, ). The Environmental Sensitivity Index is assessed by computing the Soil Quality Index, Climate Quality Index, Vegetation Quality Index, and Management Quality Index in the MEDALUS methodology (Kosmas, Ferrara, Briasouli, & Imeson, ).…”

Section: Introductionmentioning

confidence: 99%

Desertification vulnerability index—an effective approach to assess desertification processes: A case study in Anantapur District, Andhra Pradesh, India

et al. 2017

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There is a need for the up-to-date assessment of desertification/land degradation maps that are dynamic in nature at different scales for comprehensive planning and preparation of action plans. This paper aims to develop the desertification vulnerability index (DVI) and predict the different desertification processes operating in Anantapur District, India, based on machine language techniques. Climate, land use, soil, and socioeconomic parameters were used to prepare DVI by a multivariate index model. The computed DVI along with climate, terrain, and soil properties was used as explanatory variable to predict the desertification processes by using a random forest model. About 14.2% of the area was created as a training dataset in 9 places for modeling and remaining area was tested for prediction of desertification processes. We used desertification status map (DSM) of Anantapur District prepared under Desertification status mapping of India-2nd cycle as a reference dataset for calculation of accuracy indices. Kappa and classification accuracy index were calculated for training and validation datasets. We recorded overall accuracy rate and kappa index of 85.5% and 75.8% for training datasets and 71.0% and 51.8% for testing datasets. The results of variable importance analysis of random forest model showed that DVI was the most important predictor followed by potential evapotranspiration and Normalized Difference Vegetation Index for prediction of desertification processes. The results from this work given new insight into using the existing knowledge on prediction of desertification in unvisited areas and also quick update of DSM maps.

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Convergence or divergence in desertification risk? Scale-based assessment and policy implications in a Mediterranean country

Cited by 26 publications

References 58 publications

Economic growth vs. land quality: a multidimensional approach in Italy

Economic growth vs. land quality: a multidimensional approach in Italy

A multidimensional statistical framework to explore seasonal profile, severity and land-use preferences of wildfires in a Mediterranean country

Desertification vulnerability index—an effective approach to assess desertification processes: A case study in Anantapur District, Andhra Pradesh, India

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