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Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin, Turkey

Zadbagher E. | Becek K. | Berberoglu S.

Article | 2018 | Environmental Monitoring and Assessment190 ( 8 )

Land use and land cover (LULC) changes affect several natural environmental factors, including soil erosion, hydrological balance, biodiversity, and the climate, which ultimately impact societal well-being. Therefore, LULC changes are an important aspect of land management. One method used to analyze LULC changes is the mathematical modeling approach. In this study, Cellular Automata and Markov Chain (CA-MC) models were used to predict the LULC changes in the Seyhan Basin in Turkey that are likely to occur by 2036. Satellite multispectral imagery acquired in the years 1995, 2006, and 2016 were classified using the object-based class . . .ification method and used as the input data for the CA-MC model. Subsequently, the post-classification comparison technique was used to determine the parameters of the model to be simulated. The Markov Chain analyses and the multi-criteria evaluation (MCE) method were used to produce a transition probability matrix and land suitability maps, respectively. The model was validated using the Kappa index, which reached an overall level of 77%. Finally, the LULC changes were mapped for the year 2036 based on transition rules and a transition area matrix. The LULC prediction for the year 2036 showed a 50% increase in the built-up area class and a 7% decrease in the open spaces class compared to the LULC status of the reference year 2016. About an 8% increase in agricultural land is also likely to occur in 2036. About a 4% increase in shrub land and a 5% decrease in forest areas are also predicted. © 2018, Springer Nature Switzerland AG Daha fazlası Daha az

Spatio-temporal dynamics of phytoplankton functional groups in the South China Sea and their relative contributions to marine primary production

Ndah A.B. | Dagar L. | Becek K. | Odihi J.O.

Article | 2019 | Regional Studies in Marine Science29

The sea-surface distribution of four phytoplankton functional groups in the South China Sea (SCS) namely Coccolithophores (Coc), Diatoms (Dia), Cyanobacteria (Cya) and Chlorophytes (Chlo) was studied across spatial and temporal scales. The time-series datasets derived from the National Aeronautic and Space Administration (NASA) Ocean Biogeochemical Model and retrieved via the Giovanni Portal were analysed statistically with well-known methods including Pearson's correlation coefficient, ordinary least square regression (OLR), Maximum Entropy Spectral Analysis (MESA) and Principal Components Analysis (PCA). The data were analysed for . . . relative abundance, seasonality, cyclicity, long-term trends, spatial variability, inter-relationships, and relative contributions of individual phytoplankton groups to primary production (PP, using Chl-a as a proxy). The results reveal that the numerically dominant phytoplankton group in terms of relative abundance is Cya, comprising about 57% of the planktonic biomass, followed closely by Coc (about 40%), while Dia constitutes almost 3% of the total abundance. Dia was found to have the strongest annual cycle, and hence displayed the highest rate of seasonal variability. Clear spatial segregation patterns have also been uncovered; Coc and Dia are strongly correlated in the Northern SCS (NSCS) while Cya dominate in Southern SCS (SSCS). Coc and Dia display a strong positive correlation with PP, whereas the seasonal relationships between Cya and Chlo with PP are non-linear, and hence appear statistically non-significant. The rise in the trend of Cya and decrease in Coc, since 2004, may be indicative of changes in successional patterns of phytoplankton functional groups in the SCS. This study, therefore, sets the precedence for more robust research to uncover the immediate and remote causes of the observed spatiotemporal changes in marine phytoplankton dynamics and to develop predictive models of marine primary productivity (MPP) and ecological health. © 201 Daha fazlası Daha az

Is vegetation collapse on Borneo already in progress?

Becek K. | Horwath A.B.

Article | 2017 | Natural Hazards85 ( 2 ) , pp.1279 - 1290

Vegetation and tropical forests in particular have a central role in mitigating the effects of increasing levels of atmospheric CO 2 . Photosynthesis is the fundamental process during which CO 2 is taken up by plants and fixed into carbohydrates. The effect of temperature on the rate of photosynthesis in different plant species is directly related to degree-days (D-D) as well as the leaf area index (LAI). Throughout the dry season, the reduced net primary productivity is tightly correlated with increasing D-D, while the reduction in soil moisture leads to progressive canopy thinning, indicated by decreasing LAI. Forest degradation e . . .xacerbated by soil erosion and depletion of nutrients in response to high rainfall intensities during the rainy season further disturbs the ecological balance of the entire ecosystem, destabilising it beyond its natural resilience. Given this fact, ground-based evidence and remote sensing-based findings, we propose a climatically induced cascade of events leading to a gradual alteration of the tropical forest ecosystems on Borneo with a diminishing ability to absorb CO 2 and release O 2 . Such a feedback loop, which is primarily triggered by increases in temperature, has potentially dangerous outcome for tropical ecosystems and has already been observed in the north-western state of Brunei Darussalam. The island of Borneo as a whole seems to have reached a level of forest degradation that is beyond a point of no return. In the worst-case scenario, the next niche of stability may be a destruction of tropical forests and the loss of a major proportion of Earth’s biodiversity. Our aim is to stimulate further research on such occurrences and inspire the implementation of future preventative measures. © 2016, The Author(s) Daha fazlası Daha az

How well can spaceborne digital elevation models represent a man-made structure: A runway case study

Becek K. | Akgül V. | Inyurt S. | Mekik Ç. | Pochwatka P.

Article | 2019 | Geosciences (Switzerland)9 ( 9 ) , pp.1279 - 1290

In this case study, an active runway of a civilian airport in Zonguldak, Turkey was used to assess the suitability of spaceborne digital elevation models (DEMs) to model an anthropogenic structure. The tested DEMs include the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30), the Shuttle Radar Topography Mission (SRTM)-1”, the SRTM-3”, the SRTM-X, the TanDEM-3”, and the WorldDEMTM. A photogrammetric high accuracy DEM was also available for the tests. As a reference dataset, a line-leveling survey of the runway using a Leica Sprinter 150/150M in . . .strument was performed. The selection of a runway as a testbed for this type of investigation is justified by its unique characteristics, including its flat surface, homogenous surface material, and availability for a ground survey. These characteristics are significant because DEMs over similar structures are free from environment- and target-induced error sources. For our test area, the most accurate DEM was the WorldDEMTM followed by the SRTM-3” and TanDEM-3”, with vertical errors (LE90) equal to 1.291 m, 1.542 m, and 1.56 m, respectively. This investigation uses a method, known as the runway method, for identifying the vertical errors in DEMs. © 2019 by the authors Daha fazlası Daha az

On the vertical accuracy of the ALOS world 3D-30m digital elevation model

Caglar B. | Becek K. | Mekik C. | Ozendi M.

Article | 2018 | Remote Sensing Letters9 ( 6 ) , pp.607 - 615

In this contribution, we assess the vertical accuracy of the Advanced Land Observing Satellite (ALOS) World 3D 30 m (AW3D30) digital elevation model (DEM) using the runway method (RWYM). The RWYM utilizes the longitudinal profiles of runways which are reliable and ubiquitous reference data. A reference dataset used in this project consists of 36 runways located at various points throughout the world. We found that AW3D30 has a remarkably low root mean square error (RMSE) of 1.78 m (one sigma). However, while analysing the results, it has become apparent that it also contains a widespread elevation anomaly. We conclude that this anom . . .aly is the result of uncompensated sensor noise and the data processing algorithm. Also, we note that the traditional accuracy assessment of a DEM does not allow for identification of these type of anomalies in a DEM. © 2018 Informa UK Limited, trading as Taylor & Francis Group Daha fazlası Daha az

Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method

Arca D. | Kutoğlu, Hakan Şenol | Becek K.

Article | 2018 | Environmental Monitoring and Assessment190 ( 12 ) , pp.607 - 615

Landslides are geomorphological phenomena that affect anthropogenic and natural features on the Earth’s surface. Many previous studies have identified several factors that have contributed to landslides. Among these factors are physical characteristics, such as slope, aspect, and land cover, of Earth’s surface. Moreover, landslides can be triggered by human activities such as underground mining. This study aims to identify landslide susceptibility areas by analyzing landslide-related factors, including land subsidence triggered by underground mining. The area of interest was Kozlu, Turkey, where underground mining has been in progre . . .ss for the past 100 years. Thus, to identify landslide risk zones, the multicriteria decision analysis method, together with the analytical hierarchy method, was used. The datasets included were topography, land cover, geological settings, and mining-induced land subsidence. The spatial extent of land subsidence was estimated using a previously published model. A landslide susceptibility map (LSM) was developed using a purposely developed GIS-based software. The results were compared with a terrain deformation map, which was developed in a separate study using the differential synthetic aperture radar interferometry (DInSAR) technique. The results showed a substantial correlation between the LSM and DInSAR map. Furthermore, it was found that ~ 88% of the very high and high landslide risk areas coincided with location of the past landslide events. These facts suggest that the algorithm and data sources used were sufficient to produce a sufficiently accurate LSM, which may be used for various purposes such as urban planning. © 2018, The Author(s) Daha fazlası Daha az

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