Spatial assessment of the areas sensitive to degradation in the rural area of the municipality Čukarica

Momirović, Natalija; Kadović, Ratko; Perović, Velјko; Marjanović, Miloš; Baumgertel, Aleksandar

doi:10.1016/j.iswcr.2018.12.004

Cited by 24 publications

(34 citation statements)

References 15 publications

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“…However, significant spatial variability between different classes of degradation was reported by them, based on all indicators. According to our analysis carried‐out in the dust hotspot of SEA, the MMEDALUS method showed a general capability to determine the severity of LD, which is consistent with the findings of other researchers (e.g., Ait Lamqadem et al, 2018; Bakhshandehmehr et al, 2013; Momirović et al, 2019). Since, the MMEDALUS method takes into account appropriate and relatively sufficient indices in arid regions, and has ease of use and weighting, it seems superior to other methods of evaluating LD.…”

Section: Resultssupporting

confidence: 89%

“…However, significant spatial variability between different classes of degradation was reported by them, based on all indicators. According to our analysis carried-out in the dust hotspot of SEA, the MMEDALUS method showed a general capability to determine the severity of LD, which is consistent with the findings of other researchers (e.g., Ait Lamqadem et al, 2018;Bakhshandehmehr et al, 2013;Momirovi c et al, 2019).…”

Section: Ldi Analysissupporting

confidence: 89%

“…MEDALUS developed in three‐steps over 8 years (1991–1999). Numerous studies (e.g., Ait Lamqadem, Pradhan, Saber, & Rahimi, 2018; Benabderrahman & Chenchouni, 2010; Fozooni, Fakhrieh, & Ekhtesasi, 2012; Kazeminia, Rangzan, & Abadi, 2017; Mohamed, 2013; Momirović, Kadović, Perović, Marjanović, & Baumgertel, 2019; Prăvălie, Patriche, Săvulescu, Sîrodoev, & Sfîcă, 2020; Tavares, Baptista, Ferreira, & Amiotte‐Suchet, 2015; Uzuner & Dengiz, 2020; Xie et al, 2020) have been done using the MEDALUS method to evaluate LD, yielding positive results. In most of these studies, the relative simplicity, flexibility, and rapid implementation were reported as notable advantages of MEDALUS.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultssupporting

confidence: 89%

Section: Ldi Analysissupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Poornazari¹,

Khalilimoghadam²,

Hazbavi

et al. 2020

Land Degrad Dev

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show abstract

“…Finally, each criterion's score is derived from the geometric mean weight of its indices. The desertification intensity (severity) map is also calculated from the geometric mean of the criteria and classified into four classes from low to very severe [37–38].…”

Section: Methodsmentioning

confidence: 99%

Hierarchical cluster analysis to identify the homogeneous desertification management units

Zolfaghari¹,

Khosravi

Shahriyari

et al. 2019

PLoS ONE

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Since in most mapping models geometric mean of different criteria are used to determine the desertification intensity, one of the most important issues in desertification studies is understanding the similar areas, which require similar management after determining the desertification intensity map. Two similar classes of desertification intensity may require different management due to differences in the criteria that affect its desertification severity. Therefore, after determining the geomorphological facies as the working units in Sistan plain, we used hierarchical cluster analysis to identify the homogeneous environmental management units (HEMUs) based on indices of MEDALUS model. According to the MEDALUS model, the studied area was divided into two categories namely medium and high desertification classes. Working units (geomorphological facies) are classified into five clusters according to HEMUs analysis based on climate, soil, vegetation, and wind erosion criteria. The first cluster (C11) include six facies with moderate and severe desertification; in all of these units the main effective factor was wind erosion, so they need the same management decisions controlling wind erosion. Two working units (1 and 4) with the same desertification severity were placed in two different clusters due to the main factors affecting each other. The results of the Mann-Whitney test showed that the value of the test statistics was 79. Also, the value of Asymp.Sig was obtained to be 0.018, which is less than 0.025 (two-tailed test), and it can be concluded that the classification of work units in the two models, clustering and desertification, is not equal (P<0.05). So It seems that using cluster analysis to identify the same units, which need the same management decision after preparing the desertification intensity, is necessary.

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“…De forma paralela y como línea base, se realiza la delimitación espacial del proceso de desertificación en la cuenca a partir del modelo environmental sensitive area index (ESAI) creado por el proyecto internacional Mediterranean Desertification and Land Use (MEDALUS), el cual se adapta a diferentes áreas basándose en la medición de parámetros del suelo, el clima, la vegetación y el manejo de la tierra (Lahlaoi et al, 2017;Huaico, 2018;Natalija et al, 2019) Las estrategias propuestas para prevenir y mitigar el proceso de desertificación en la CMRCh permiten, en primera instancia, visibilizar esta problemática que puede tener efectos negativos en el bienestar de la población en el mediano y largo plazo y, a su vez, identificar las alternativas de acción de forma espacializada, así como algunos elementos socioeconómicos y político-institucionales que, en conjunto, pueden facilitar la toma de decisiones en torno a su implementación. De tal manera, se aporta al fortalecimiento del PAN a nivel nacional y en general al cumplimiento de los compromisos ambientales relacionados con la lucha contra la desertificación y el paradigma de la degradación neutra de las tierras, como los contenidos en los Objetivos de Desarrollo Sostenible (ODS).…”

Section: Introductionunclassified

Estrategias para la prevención y mitigación del proceso de desertificación. Estudio en la cuenca media del río Chicamocha, Colombia

2021

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La insuficiencia de instrumentos orientados a la prevención y la mitigación del proceso de desertificación es uno de los factores que incide en la agudización de esta problemática ambiental presente en el noreste del departamento de Boyacá, en la cuenca media del río Chicamocha. Con base en lo anterior, se proponen algunas estrategias que contribuyen al manejo de dicho proceso, considerando elementos biofísicos, socioeconómicos y político-institucionales de la cuenca que apuntan a la consolidación de una herramienta para la toma de decisiones al respecto. El desarrollo del estudio se sintetiza en tres etapas: 1) identificación de las posibles estrategias para prevenir y mitigar la desertificación, 2) representación y distribución espacial de las y 3) evaluación teórico-práctica de las estrategias. Como resultado se definieron cuatro estrategias que abarcaron los sistemas y las prácticas de producción sostenible, los esquemas de pago por servicios ambientales, los sistemas de monitoreo y alerta temprana, y las figuras reglamentarias enfocadas en la declaración de áreas protegidas. Las condiciones biofísicas de la cuenca brindan varias alternativas para la localización de las estrategias, en las cuales son determinantes el uso y el tipo de cobertura del suelo y los marcos nacionales respectivos; sin embargo, las condiciones socioeconómicas se constituyeron en una posible limitante para su implementación. Finalmente, la mayoría de las estrategias están incluidas en los instrumentos de planeación regionales, lo que representa un punto de partida para su eventual ejecución.

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Spatial assessment of the areas sensitive to degradation in the rural area of the municipality Čukarica

Cited by 24 publications

References 15 publications

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

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

Hierarchical cluster analysis to identify the homogeneous desertification management units

Estrategias para la prevención y mitigación del proceso de desertificación. Estudio en la cuenca media del río Chicamocha, Colombia

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