Monitoring Sensitive Areas To Desertification In Sardinia: The Contribute Of The Regional Agrometeorological Service

Motroni, Andrea; Canu, Simona; Bianco, Giuseppe; Loj, Giosu È

doi:10.1007/978-1-4020-8937-4_10

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…The findings of the RAP of Basilicata proved to be aligned with other ESA-based studies [105,106]. Lastly, the RAP of Sardinia (see [107]) presented results partially overlapping with our map for the time frame 1990-2010 and with the map derived from an independent work, based on the integration of models and remote sensing data [67]. For Sicily, our 1990-2010 intercept-to-slope ratio map captured a large part of areas, identified as vulnerable by Giordano et al [108] and by the last Regional Action Programme to fight desertification (see [109]).…”

Section: Discussionsupporting

confidence: 85%

Modeling Spatio-Temporal Divergence in Land Vulnerability to Desertification with Local Regressions

et al. 2022

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Taken as a classical issue in applied economics, the notion of ‘convergence’ is based on the concept of path dependence, i.e., from the previous trajectory undertaken by the system during its recent history. Going beyond social science, a ‘convergence’ perspective has been more recently adopted in environmental studies. Spatial convergence in non-linear processes, such as desertification risk, is a meaningful notion since desertification represents a (possibly unsustainable) development trajectory of socio-ecological systems towards land degradation on a regional or local scale. In this study, we test—in line with the classical convergence approach—long-term equilibrium conditions in the evolution of desertification processes in Italy, a European country with significant socioeconomic and environmental disparities. Assuming a path-dependent development of desertification risk in Italy, we provided a diachronic analysis of the Environmental Sensitive Area Index (ESAI), estimated at a disaggregated spatial resolution at three times (1960s, 1990s, and 2010s) in the recent history of Italy, using a spatially explicit approach based on geographically weighted regressions (GWRs). The results of local regressions show a significant path dependence in the first time interval (1960–1990). A less significant evidence for path-dependence was observed for the second period (1990–2010); in both cases, the models’ goodness-of-fit (global adjusted R2) was satisfactory. A strong polarization along the latitudinal gradient characterized the first observation period: Southern Italian land experienced worse conditions (e.g., climate aridity, urbanization) and the level of land vulnerability in Northern Italy remained quite stable, alimenting the traditional divergence in desertification risk characteristic of the country. The empirical analysis delineated a more complex picture for the second period. Convergence (leading to stability, or even improvement, of desertification risk) in some areas of Southern Italy, and a more evident divergence (leading to worse environmental conditions because of urban sprawl and crop intensification) in some of the land of Northern Italy, were observed, leading to an undesired spatial homogenization toward higher vulnerability levels. Finally, this work suggests the importance of spatially explicit approaches providing relevant information to design more effective policy strategies. In the case of land vulnerability to degradation in Italy, local regression models oriented toward a ‘convergence’ perspective, may be adopted to uncover the genesis of desertification hotspots at both the regional and local scale.

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Section: Discussionsupporting

confidence: 85%

Modeling Spatio-Temporal Divergence in Land Vulnerability to Desertification with Local Regressions

et al. 2022

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“…The intense wildfire activity (Bajocco and Ricotta, 2008), together with grazing pressure (D'Angelo Enne et al, 2002), local droughts (Fiori et al, 2004), mismanagement and salinization of groundwater resources (Santini et al, 2010), deforestation, land overexploitation and abandonment (Giordano and Marini, 2008) represent the key factors causing LD in Sardinia. For a more detailed description of the LD processes in the study area, see Motroni et al (2009).…”

Section: Materials and Methods 1 Study Areamentioning

confidence: 99%

Land degradation versus fire: A spiral process?

Bajocco¹,

Salvati

Ricotta

2010

Progress in Physical Geography: Earth and Environment

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Originally associated to arid regions, vulnerability to land degradation (LD) has rapidly spread in temperate areas, such as the Mediterranean basin. In this region LD increased in the last years due to worsening climate conditions, land-cover changes, soil erosion and anthropogenic pressures. Increasing land vulnerability is mutually linked with an increasing risk of disturbance propagation: the spatio-temporal distribution of areas with different degrees of LD determines different dynamic patterns of the disturbance; in turn, LD is strongly affected by the disturbance occurrence regime, which alters the status quality of a given territory. These considerations invite a comparison between vulnerability to LD and fire occurrence, since historically, in the Mediterranean areas, fire represents one of the main disturbance sources, which is mostly human-induced and with a strong seasonality pattern. Under certain conditions, LD may create conducive conditions for fire to thrive that in turn if repeated may alter the quality status of a landscape, setting the interested area into a LD-fire feedback dynamic. The aim of this paper is to analyse the relationship between fire incidence and LD and their potential feedbacks in Sardinia during two reference periods, 1990 and 2000. Results indicated that in areas already affected by high LD vulnerability there is a sort of LD-fire spiralling connection that can be seen as a 'mutual early-warning system' with strong implications on fire prevention strategies and landscape quality status monitoring

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“…On the other hand, we found that the northwestern slope had a negative association; the occurrence of desertification was about 15% lower compared to other aspects. Previous works [77,121,122] mentioned the fact that slope aspects influence the local environment, as the duration of the solar radiation affects the temperature and evaporation. The latter leads to the creation of a microclimate.…”

Section: Key Factors and Desertification Occurrencementioning

confidence: 99%

Desertification Susceptibility Mapping Using Logistic Regression Analysis in the Djelfa Area, Algeria

2017

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Abstract:The main goal of this work was to identify the areas that are most susceptible to desertification in a part of the Algerian steppe, and to quantitatively assess the key factors that contribute to this desertification. In total, 139 desertified zones were mapped using field surveys and photo-interpretation. We selected 16 spectral and geomorphic predictive factors, which a priori play a significant role in desertification. They were mainly derived from Landsat 8 imagery and Shuttle Radar Topographic Mission digital elevation model (SRTM DEM). Some factors, such as the topographic position index (TPI) and curvature, were used for the first time in this kind of study. For this purpose, we adapted the logistic regression algorithm for desertification susceptibility mapping, which has been widely used for landslide susceptibility mapping. The logistic model was evaluated using the area under the receiver operating characteristic (ROC) curve. The model accuracy was 87.8%. We estimated the model uncertainties using a bootstrap method. Our analysis suggests that the predictive model is robust and stable. Our results indicate that land cover factors, including normalized difference vegetation index (NDVI) and rangeland classes, play a major role in determining desertification occurrence, while geomorphological factors have a limited impact. The predictive map shows that 44.57% of the area is classified as highly to very highly susceptible to desertification. The developed approach can be used to assess desertification in areas with similar characteristics and to guide possible actions to combat desertification.

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Monitoring Sensitive Areas To Desertification In Sardinia: The Contribute Of The Regional Agrometeorological Service

Cited by 6 publications

References 1 publication

Modeling Spatio-Temporal Divergence in Land Vulnerability to Desertification with Local Regressions

Modeling Spatio-Temporal Divergence in Land Vulnerability to Desertification with Local Regressions

Land degradation versus fire: A spiral process?

Desertification Susceptibility Mapping Using Logistic Regression Analysis in the Djelfa Area, Algeria

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