Quantitative assessment of climate change impacts onto predicted erosion risks and their spatial distribution within the landcover classes of the Southern Caucasus using GIS and remote sensing

Bayramov, Emil; Schlager, Patric; Kada, Martin; Buchroithner, Manfred; Bayramov, Rafael

doi:10.1007/s40808-018-0557-3

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…Despite the high value of the maximum NDVI value observed on December 31, 2016, we found that only a few pixels have these high values, which were not significant spectrally. In accordance with the present results, previous study has demonstrated that significant areas of stressed vegetation with low NDVI values (0.1-0.2) are within croplands reducing soil productivity over years (Bayramov et al 2019). Over the NDVI map of TM5 MSS given in Fig.…”

Section: Landsat Imagery Analyses Corroborating With Field Surveysupporting

confidence: 92%

“…Recent studies have demonstrated the impact of climate weather-related disturbances in the highly populated regions resulting in significant damage and loss. Bayramov et al 2019 employed a remote sensing approach to investigate the impacts of climate change on future erosion risks and quantify erosion risks in relation to croplands and vegetation cover. Also, Abuzar et al 2019 assessed drought severity for the years 2003, 2009, and 2015 images to map up drought prevalence and its trend in Punjab province of Pakistan and the NDVI based vegetation classes showed a shift of vegetation towards the northern parts of Khushab district.…”

Section: Introductionmentioning

confidence: 99%

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Climate disturbance impact assessment in West Africa: evidence from field survey and satellite imagery analysis

Traoré

Wei

Rehman

et al. 2020

Environ Sci Pollut Res

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Extreme drought events from climate disturbances are weakening livelihood and limiting agriculture and livestock production in the Sahel region. The lack of relevant information to anticipate coping measures has exacerbated impacts leading to climate adaptation failure in most parts. In this regard, the current research paper has collected important datasets with an objective to assess the impact of extreme drought events on household's livelihoods for better understanding impacts, local people's perception, and the changes on vegetation cover in order to support a robust adaptation strategy to drought. The study conducted a household survey and collected satellite data for comparative analysis. The first survey was conducted in 2013 to collect data from 465 household heads through a structured questionnaire. Supplementary focus group discussions (FGDs) were also conducted in 2018 to collect qualitative information from targeted respondents such as village leaders and members of other key groups including women and youth. Descriptive statistics and correlation coefficient matrix were used to characterize the impact on households' main livelihoods and logistic regression to predict people's perception on pasture depletion over the last 20 years. Satellite data were used to derive spectral vegetation of land covers and unsupervised classification indexes. Both individual survey and focus group discussions identified drought as the main climate constraint which reduced crop production, water and pastures. The logistic analysis revealed that if the respondent's major occupation is livestock, the probability to perceive a depletion of pasture will increase by 28%. Concurrently, the satellite image observation in perfect agreement with the field survey showed 6.78% and 6.01% losses of water surface and vegetation cover respectively between 1986 and 2016 in the study area. These findings showed that logistic regression coupled with satellite information can inform on past and future impacts which are extremely crucial for sound adaptation planning in the Sahel region.

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Section: Landsat Imagery Analyses Corroborating With Field Surveysupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Climate disturbance impact assessment in West Africa: evidence from field survey and satellite imagery analysis

Traoré

Wei

Rehman

et al. 2020

Environ Sci Pollut Res

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“…The empirical equation based models like Universal Soil Loss Equation (USLE) developed by Wischmeier and Smith (1978) and its revised version Revised Universal Soil Loss Equation (RUSLE) developed by Renard et al (1997) have universal application for estimation of soil loss (Markose and Jayappa 2016). The investigation of erosional processes based on RS and geospatial principles is critical to make people aware about erosion-prone zones, decision making and mitigation of its risk (Bayramov et al 2019).Moreover, field based survey is used as a conventional method of soil erosion assessment which is not cost-effective and a time-consuming exercise (Dissanayake et al 2019). Therefore in the modern era of earth observation, using RS and GIS has made the task more scientific and also feasible to carry out at a large spatial scale.…”

Section: Introductionmentioning

confidence: 99%

Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India

Bagwan

Gavali

2020

Model. Earth Syst. Environ.

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Soil loss by water erosion is a common type of land degradation issue in the hilly regions of the world. The present study investigates the soil erosion risk due to change in Land Use Land Cover (LULC) brought in by the construction of dam in a hilly watershed of the River Urmodi embracing Kaas Plateau, a world heritage site. The Revised Universal Soil Loss Equation (RUSLE) model was used with change detection analysis for soil erosion estimation. Thematic layers required for the computation of factors required in RUSLE were gathered using remote sensing (RS) and Geographical Information Science techniques. The changes in LULC affect the soil erosion phenomena and therefore, the present study analyzes drastic alteration in the hydrological nature of the watershed brought by the construction of reservoir through damming the Urmodi River. For assessment of the impact of the dam we have used RS data with 30 m spatial resolution, which includes Digital Elevation Model and Landsat TM and Landsat 8 bands. Using RUSLE, soil risk zone was mapped and the Change Detection in zones were computed. The very simple logic of 'soil erosion class as a type of land cover' was successfully applied to perform the change transition for the Urmodi river watershed. Confusion matrix technique used for analyzing the change, has the ability to show the previous and next condition of zone type very affectively with good precision. Along with Change Detection technique, soil erosion class transition operation was also performed to assess the conversion of zones. This gave the important output regarding increased conversion from very slight to very severe risk zone by 70.22 km 2 . The investigation concludes that there was extensive change in very severe (> 80 ton ha −1 year −1 ) soil vulnerable risk zone in the last 17 years with the rise of 14.87%. The conversion of lower risk category areas to higher risk category areas of soil erosion was detected all over the watershed. Undertaking such studies revealed the changes brought in the soil erosion susceptible zones by building of dams. This study will be a crucial help to policy and decision makers for proper planning of watershed with consideration to LULC in the event of dam construction on the rivers flowing in hilly terrain.

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“…The abundant data produced by optical remote sensing technology are the key times and spatially information on the understanding of the processes of forest ecosystems in the micro and macro-region (Bayramov et al, 2019). The principle of operation of remotely sensed imagery is interpreted on the differential absorption, transmission, and energy reflection of the electromagnetic spectrum, such as red, blue, green, and infrared using passive sensors (Fescenko and Wohlgemuth, 2017).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the forest covers dynamics in the Samur-Yalama National Park of Azerbaijan

Abiyev

Karslı

Gümüş

et al. 2020

European Journal of Forest Engineering

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Most forests in Azerbaijan are distributed in mountain ecosystems, and lowland forests cover very small area. The biodiversity of Caspian coastal lowlands has come face-to-face with the danger of depletion over the last 100 years. At the present, their main broad-leaved forest example remains in the Samur-Yalama zone. Due to a strong anthropogenic impact, forest-covered areas are decreasing. From this point of view, a comprehensive study of the forests of the Samur-Yalama National Park is of special importance. In the studying of the dynamics of the forest cover, the spectral vegetation indices are main technique. The aim of the research is to analyze the forest covers dynamics by using the Normalized Difference Vegetation Index for the Samur-Yalama National Park of Azerbaijan. For this study, multi-spectral images were obtained from the Landsat 5 TM and 8 OLI program of the United States between 1984-2019 years. According to the analysis, the dynamics of forest cover development show a negative value and gradually replacement of the densely forested areas by sparse forest-shrub vegetation. Although the overall condition of forests has deteriorated in recent years and it has been considered to fit the category of moderately healthy forests. If this decrease will continue with current speed, then next decades’ forests will move towards the weak healthy forests. That is why it needed to increase management, protection and awareness activities.

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Quantitative assessment of climate change impacts onto predicted erosion risks and their spatial distribution within the landcover classes of the Southern Caucasus using GIS and remote sensing

Cited by 6 publications

References 32 publications

Climate disturbance impact assessment in West Africa: evidence from field survey and satellite imagery analysis

Climate disturbance impact assessment in West Africa: evidence from field survey and satellite imagery analysis

Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed, Maharashtra, India

Analysis of the forest covers dynamics in the Samur-Yalama National Park of Azerbaijan

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