Assessment and mapping the sensitive areas to desertification in an insular Sahelian mountain region Case study of the Ribeira Seca Watershed, Santiago Island, Cabo Verde

Tavares, Jacques de Pina; Baptista, Isaurinda; Ferreira, A. J. D.; Amiotte‐Suchet, Philippe; Coelho, Céleste; Gomes, Samuel; Amoros, Regla; Reis, Eduardo A.; Mendes, Adriano Furtado; Costa, Lenira; Bentub, Jailson; Varela, Larissa

doi:10.1016/j.catena.2014.10.005

Cited by 47 publications

(26 citation statements)

References 15 publications

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“…For this reason, we tested a set of key factors that provide information about desertification commonly used in the literature [3,5,27,28] and we then selected those that seemed to be most adequate for our study area. We also faced constraints related to the lack of data in countries subject to desertification [48], Remote Sens.…”

Section: Causal Factors and Desertificationmentioning

confidence: 99%

“…The Convention to Combat Desertification defined desertification as "land degradation in arid, semiarid, and dry sub-humid areas resulting from climatic variations and human activities" [1,3,4]. In this context, the desertification phenomenon was considered by taking into account the degree of vulnerability or the response of the ecosystem [5].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Causal Factors and Desertificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2017

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“…Where: W X is the score for each study indicator, w (1, 2, … , n) is the score for each study variable, and n is the number of study variables for each indicator. Vegetation quality (Bakhshandehmehr et al, 2013;Sepehr et al, 2007) Land management quality (Bakhshandehmehr et al, 2013;Sepehr et al, 2007;Tavares et al,…”

Section: Score Assigning and Index/indicator Calculationmentioning

confidence: 99%

Land degradation assessment in the dust hotspot of Southeastern Ahvaz, Iran

Poornazari¹,

Khalilimoghadam²,

Hazbavi

et al. 2020

Land Degrad Dev

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The present study was conducted to assess the land degradation (LD) in the largest source area for dust storms in Southeastern Ahvaz (SEA) using a Modified Mediterranean Desertification and Land‐use (MMEDALUS) method (a modification of the MEDALUS method). The LD of the dust hotspot of SEA was evaluated using the soil quality index (SQI), climate quality index (CQI), vegetation quality index (VQI), and management quality index (MQI). The study indicators and final land degradation index (LDI) index were classified into four groups of low (100–120), moderate (121–135), severe (136–153), and very severe (more than 153). The results showed that the SQI, CQI, VQI, and land management quality index (MQI), respectively were 151 ± 6, 167 ± 5, 175 ± 12, and 168 ± 18 for the whole dust hotspot of SEA. The results revealed that the SQI and VQI had the minimum and maximum effect on LD assessment, respectively. The integration of study indices and indicators and their effectiveness for LD assessment showed that the study dust hotspot was rated at a mean of 165 ± 7 in the very severe degraded class based on our MMEDALUS assessment. Whereas, only about 18% of the study area scored in the severe class, and other parts are classified in the very severe class. Based on the field surveys of the dust hotspot and the results of the MMEDALUS method, suitable strategies for revitalizing the dust hotspot of SEA, were proposed using homogeneous land‐components such as the construction of windbreaks using indigenous species of the area, forestry, planting, water spreading, wetting, protection, and grazing management.

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“…In addition, some proxies that could reflect the comprehensive impact of these driving factors were developed to predict the potential risk of future desertification (Karamesouti, Panagos, & Kosmas, 2018; Karavitis et al, 2020; Salvati & Bajocco, 2011). For example, the Environmental Sensitivity Area Index derived from the Mediterranean Desertification and Land Use Project has been widely used to predict the sensitivity of desertification or land degradation in various countries (Bakr, Weindorf, Bahnassy, & El‐Badawi, 2012; De Pina Tavares et al, 2015; Prăvălie et al, 2020). Proxies that would reflect the impact of climate change have also been used to predict the risk of desertification; for example, the dune activity index was used to investigate changes in wind erosion (Wang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Multi‐scenario simulation of desertification in North China for 2030

Zhang

2020

Land Degrad Dev

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Since desertification threatens the global environment and the security of societies, predicting its patterns and trends across space and time would inform policy‐making strategies for desertification control and sustainable development. China is among the countries most severely affected by desertification, representing 17.93% of its total land area. To better understand the future dynamics of desertification in China, a spatial system dynamics model that coupled natural and human factors was developed to simulate the desertification process in North China in 2030 under nine scenarios. The results showed the model had good applicability and satisfactory accuracy for desertification simulation at the national scale, for which temperature and rainfall were the most sensitive factors driving the process. Current ecological projects and policies were found to be effective for desertification control, which would lead to reversal of desertification in 2030 when coupled with average predicted climate conditions; further, this reversal trend would be accelerated under a humid climate scenario. Although positive ecological policies would promote the reversal of desertification, only a 5% increase of rehabilitated area was obtained when it was coupled with an arid climatic conditions. The latter (aridity) and a passive ecological protection policy would together accelerate desertification expansion, especially in the Yili Basin, in the Houshan region of Inner Mongolia and the Yinchuan Plain. Thus, the ecological projects and policies should be carried on in the long‐term to achieve sustainable control and help prevent desertification.

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Assessment and mapping the sensitive areas to desertification in an insular Sahelian mountain region Case study of the Ribeira Seca Watershed, Santiago Island, Cabo Verde

Cited by 47 publications

References 15 publications

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

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

Land degradation assessment in the dust hotspot of Southeastern Ahvaz, Iran

Multi‐scenario simulation of desertification in North China for 2030

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