Assessing the impact of multiple drivers of land sensitivity to desertification in a Mediterranean country

Egidi, Gianluca; Cividino, Sirio; Paris, Enrico; Palma, Adriano; Salvati, Luca; Cudlín, Pavel

doi:10.1016/j.eiar.2021.106594

Cited by 27 publications

(14 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the Land Use (MEDALUS) model quantifies the magnitude of the land desertification probability in terms of soil, climate, erosion, vegetation, and human activities and management [22,23]. The MEDALUS model is widely used in Italy [24][25][26], India [27], Central Asia [28], Greece [29], Iran [30], Turkey [31], Northern China [32], and even worldwide [33]. This model scientifically and flexibly integrates many factors driving desertification processes to provide data support for active and effective desertification monitoring.…”

Section: Instructionmentioning

confidence: 99%

“…Clarifying the driving role of factors on land sensitivity to desertification and grasping the main contradictions are important prerequisites for developing effective control plans. Studies have been conducted using Geographically Weighted Regression [26], Random Forest [27], Cluster Analysis [32], and Geodetector [34] on the driving role and driving mechanisms of indicators. Land sensitivity to desertification in Italy is mainly driven by vegetation quality and is more important than land management and climate quality [26], which is consistent with Rajbanshi's study indicating the importance, up to 46%, of vegetation drought resistance [27].…”

Section: Instructionmentioning

confidence: 99%

“…Studies have been conducted using Geographically Weighted Regression [26], Random Forest [27], Cluster Analysis [32], and Geodetector [34] on the driving role and driving mechanisms of indicators. Land sensitivity to desertification in Italy is mainly driven by vegetation quality and is more important than land management and climate quality [26], which is consistent with Rajbanshi's study indicating the importance, up to 46%, of vegetation drought resistance [27]. Regions located at different latitudes have different soil and water conditions, resulting in different contributions of vegetation, climate, and soil to the desertification process.…”

Section: Instructionmentioning

confidence: 99%

See 2 more Smart Citations

Spatiotemporal Changes and Driving Force Analysis of Land Sensitivity to Desertification in Xinjiang Based on GEE

Zhao

Yang

et al. 2023

Land

View full text Add to dashboard Cite

Land desertification profoundly affects economic and social development, thus necessitating a collective response. Regional land control planning needs to assess the land sensitivity to desertification across different regions. In this study, we selected 12 factors from soil, vegetation, climate, and terrain aspects to calculate and evaluate Xinjiang’s land sensitivity to desertification, from 2001 to 2020, and analyzed its trends and drivers. The results indicated that the region is highly (22.93%) to extremely sensitive (34.63%) to desertification. Of these, deserts, Gobi lands, oasis–desert transitional zones, and the downstream of rivers are highly and extremely sensitive areas. Mountainous areas, oases, and along rivers are non- and mildly sensitive areas. Over the past two decades, most areas have experienced stability (45.07%) and a slight improvement of desertification (26.18%), while the Junggar Basin and Central Taklamakan Desert have seen slight and severe intensification trends, respectively. Climate-related indicators, such as surface temperature and potential evapotranspiration (PET), were identified as the most important drivers of changes in land sensitivity to desertification. Having an integrated water resource allocation and establishing the long-term monitoring of land sensitivity to desertification would have positive implications for desertification control.

show abstract

Section: Instructionmentioning

confidence: 99%

Section: Instructionmentioning

confidence: 99%

Section: Instructionmentioning

confidence: 99%

See 1 more Smart Citation

Spatiotemporal Changes and Driving Force Analysis of Land Sensitivity to Desertification in Xinjiang Based on GEE

Zhao

Yang

et al. 2023

Land

View full text Add to dashboard Cite

show abstract

“…Based on multisource data such as climate, vegetation, and soil, the establishment of comprehensive assessment models using methods such as AHP is also commonly used in desertification monitoring work [13][14][15]. However, this method has defects such as cumbersome data collation, strong subjectivity, and data coupling correlation [16][17][18]. Mixed pixel decomposition is also used in desertification monitoring [19].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

Chen

Shen

et al. 2022

Sustainability

View full text Add to dashboard Cite

The ecological stability of mining areas in Northwest China has been threatened by desertification for a long time. Remote sensing information combined with machine learning algorithms can effectively monitor and evaluate desertification. However, due to the fact that the geological environment of a mining area is easily affected by factors such as resource exploitation, it is challenging to accurately grasp the development process of desertification in a mining area. In order to better play the role of remote sensing technology and machine learning algorithms in the monitoring of desertification in mining areas, based on Landsat images, we used a variety of machine learning algorithms and feature combinations to monitor desertification in Ningdong coal base. The performance of each monitoring model was evaluated by various performance indexes. Then, the optimal monitoring model was selected to extract the long-time desertification information of the base, and the spatial-temporal characteristics of desertification were discussed in many aspects. Finally, the factors driving desertification change were quantitatively studied. The results showed that random forest with the best feature combination had better recognition performance than other monitoring models. Its accuracy was 87.2%, kappa was 0.825, Macro-F1 was 0.851, and AUC was 0.961. In 2003–2017, desertification land in Ningdong increased first and then slowly improved. In 2021, the desertification situation deteriorated. The driving force analysis showed that human economic activities such as coal mining have become the dominant factor in controlling the change of desert in Ningdong coal base, and the change of rainfall plays an auxiliary role. The study comprehensively analyzed the spatial-temporal characteristics and driving factors of desertification in Ningdong coal base. It can provide a scientific basis for combating desertification and for the construction of green mines.

show abstract

“…Assuming the increased impact of both exogenous processes (e.g., climate warming) and endogenous forces (e.g., social, cultural, institutional and political change, evolution of productive structures, global economic and financial crises) on land resource quality (e.g., Barbati et al., 2013), a more precise identification of the multiple factors underlying desertification risk is a policy‐relevant issue (Egidi et al., 2021; Kacem et al., 2021; Karamesouti et al., 2018). Human dimensions are demonstrated to play a major role shaping desertification risk in Southern Europe (Egidi et al., 2022; Morianou et al., 2021; Wilson & Juntti, 2005).…”

Section: Introductionmentioning

confidence: 99%

Framing socioecological complexity: The long‐term evolution of multiple dimensions of desertification risk in Italy

Salvati

2022

Risk Analysis

Self Cite

View full text Add to dashboard Cite

Desertification risk depends on the interplay of biophysical and socioeconomic drivers, among which climate change, soil depletion, landscape modifications, and biodiversity decline are key factors of change in Southern Europe. The present study introduces a diachronic analysis of desertification risk in Italy adopting a multidimensional approach based on four dimensions (ecological, economic, demographic, and administrative) assessed at three dates (1961, 1991, and 2011). These risk components were evaluated separately in Southern Italy, a formerly affected region (sensu United Nations Convention to Combat Desertification), and Northern/Central Italy, a nonaffected region in the country. All risk measures document how the divide between affected and nonaffected regions in Italy has gradually reduced. Because of local warming and rising human pressure, Northern Italy has recently displayed a level of desertification risk close to those observed in Southern Italy over the last 30 years. These results suggest a thorough revision of the national classification of risky areas, that may inform more specific mitigation and adaptation policies responding effectively to recent socioenvironmental trends and local (economic) dynamics. The intrinsic system's evolution observed at both regional and national level in Italy may be generalized to a broader European context. Our work finally documents the appropriateness of a multidimensional definition of desertification risk grounded on the joint analysis of ecological, demographic, economic, and administrative indicators. A comprehensive knowledge of socioeconomic patterns and processes of change contributes to more precise scenario modeling and design of integrated strategies mitigating desertification risk.

show abstract

Assessing the impact of multiple drivers of land sensitivity to desertification in a Mediterranean country

Cited by 27 publications

References 74 publications

Spatiotemporal Changes and Driving Force Analysis of Land Sensitivity to Desertification in Xinjiang Based on GEE

Spatiotemporal Changes and Driving Force Analysis of Land Sensitivity to Desertification in Xinjiang Based on GEE

Dynamic Monitoring of Desertification in Ningdong Based on Landsat Images and Machine Learning

Framing socioecological complexity: The long‐term evolution of multiple dimensions of desertification risk in Italy

Contact Info

Product

Resources

About