Spatio-temporal analysis of land use/land cover change and its effects on soil erosion (Case study in the Oplenac wine-producing area, Serbia)

Perović, Velјko; Jakšić, Darko; Jaramaz, Darko; Koković, Nikola; Čakmak, Dragan; Mitrović, Miroslava; Pavlović, Pavle

doi:10.1007/s10661-018-7025-4

Cited by 52 publications

(26 citation statements)

References 60 publications

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“…Under rapid regional socioeconomic development and urban mechanisms, the GBA experienced a transformation that has had a tremendous impact on the spatial pattern of LULC changes [71,72]. In this study, we modeled the spatiotemporal transition potential and future scenario of LULC with the help of the Modules for Land-Use Change Simulation (MOLUSCE) plugin within QGIS software [73][74][75]. The MOLUSCE plugin incorporates some well-known algorithms, such as artificial neural networks (ANNs) and Monte Carlo cellular automata (CA) modeling approaches [76].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

Abbas

Guang

Zhong

et al. 2021

Land

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Land use land cover (LULC) transition analysis is a systematic approach that helps in understanding physical and human involvement in the natural environment and sustainable development. The study of the spatiotemporal shifting pattern of LULC, the simulation of future scenarios and the intensity analysis at the interval, category and transition levels provide a comprehensive prospect to determine current and future development scenarios. In this study, we used multitemporal remote sensing data from 1980–2020 with a 10-year interval, explanatory variables (Digital Elevation Model (DEM), slope, population, GDP, distance from roads, distance from the city center and distance from streams) and an integrated CA-ANN approach within the MOLUSCE plugin of QGIS to model the spatiotemporal change transition potential and future LULC simulation in the Greater Bay Area. The results indicate that physical and socioeconomic driving factors have significant impacts on the landscape patterns. Over the last four decades, the study area experienced rapid urban expansion (4.75% to 14.75%), resulting in the loss of forest (53.49% to 50.57%), cropland (21.85% to 16.04%) and grassland (13.89% to 12.05%). The projected results (2030–2050) also endorse the increasing trend in built-up area, forest, and water at the cost of substantial amounts of cropland and grassland.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

Abbas

Guang

Zhong

et al. 2021

Land

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“…Therefore, we compared the simulated LULC maps for 2018 generated from the year 1998 and 2008, with the actual Landsat satellite-derived LULC maps of 2018 based on the comparative analytical process of Kappa validation. The overall simulation success of CA-ANN model is 67% and current kappa value is 0.62, which is considered favourable as per Perović et al, 2018. However, for validating the simulated LULC 2018 with ground reality, total number of 256 survey points are randomly selected (figure.…”

Section: Model Validationmentioning

confidence: 99%

“…Among these impacts, landuse/landcover (LULC) change has been recognized as one of the most important indicators for global and regional ecoenvironmental changes (Li et al 2006;Zhang, Zhengjun, and Xiaoxia 2009). Remote Sensing (RS) and GIS techniques incorporated with multispectral satellite data have been commonly used for the acquisition of LULC information (Singh 2016;Rahman et al 2017;Perović et al 2018). In a river-scape context, a number of studies have identified the importance of LULC change detection.…”

Section: Introductionmentioning

confidence: 99%

Prediction modelling of riverine landscape dynamics in the context of sustainable management of floodplain: a Geospatial approach

et al. 2021

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“…However, perennial plants are also endangered by these processes. In vineyards, erosion processes can be very pronounced because vineyards are usually based on steep and hilly terrain, as well as on mountains with southern exposure due to the better quality of grapes obtained [25,26]. Besides, due to specific soil properties in vineyards, such as limited soil development, coarse texture and low capacity to protect SOM binding to soil minerals, these soils are sensitive to degradation [27][28][29] and lose potentially more SOC than other agricultural soils.…”

Section: Introductionmentioning

confidence: 99%

Topographic Position, Land Use and Soil Management Effects on Soil Organic Carbon (Vineyard Region of Niš, Serbia)

et al. 2021

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Spatial distribution of soil organic carbon (SOC) is the result of a combination of various factors related to both the natural environment and anthropogenic activities. The aim of this study was to examine (i) the state of SOC in topsoil and subsoil of vineyards compared to the nearest forest, (ii) the influence of soil management on SOC, (iii) the variation in SOC content with topographic position, (iv) the intensity of soil erosion in order to estimate the leaching of SOC from upper to lower topographic positions, and (v) the significance of SOC for the reduction of soil’s susceptibility to compaction. The study area was the vineyard region of Niš, which represents a medium-sized vineyard region in Serbia. About 32% of the total land area is affected, to some degree, by soil erosion. However, according to the mean annual soil loss rate, the total area is classified as having tolerable erosion risk. Land use was shown to be an important factor that controls SOC content. The vineyards contained less SOC than forest land. The SOC content was affected by topographic position. The interactive effect of topographic position and land use on SOC was significant. The SOC of forest land was significantly higher at the upper position than at the middle and lower positions. Spatial distribution of organic carbon in vineyards was not influenced by altitude, but occurred as a consequence of different soil management practices. The deep tillage at 60–80 cm, along with application of organic amendments, showed the potential to preserve SOC in the subsoil and prevent carbon loss from the surface layer. Penetrometric resistance values indicated optimum soil compaction in the surface layer of the soil, while low permeability was observed in deeper layers. Increases in SOC content reduce soil compaction and thus the risk of erosion and landslides. Knowledge of soil carbon distribution as a function of topographic position, land use and soil management is important for sustainable production and climate change mitigation.

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Spatio-temporal analysis of land use/land cover change and its effects on soil erosion (Case study in the Oplenac wine-producing area, Serbia)

Cited by 52 publications

References 60 publications

Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

Spatiotemporal Change Analysis and Future Scenario of LULC Using the CA-ANN Approach: A Case Study of the Greater Bay Area, China

Prediction modelling of riverine landscape dynamics in the context of sustainable management of floodplain: a Geospatial approach

Topographic Position, Land Use and Soil Management Effects on Soil Organic Carbon (Vineyard Region of Niš, Serbia)

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