Designing and testing a network of passive seismic surveying and monitoring in Dehdasht (South Western Iran)

From August 2016 to July 2017, a passive seismic survey was conducted in South Western Iran as a part of a pilot project aimed to improve the imaging in geologically complex areas. Passive seismic methods have shown to be a useful tool to infer the physical properties of the underground geological structures where traditional hydrocarbon exploration methods are challenging. For this purpose, a dense passive seismic network consisting of 119 three-component borehole seismic stations was deployed over an area of 400 km² around the city of Dehdasht. This paper focuses on the details of the network design, which was devoted to high-resolution seismological applications, including local earthquake tomography and seismic attenuation imaging. In this regard, we describe the instrument types and the station installation procedures used to obtain high-quality data that were used to retrieve three-dimensional models of P- and S-wave velocity and P-wave attenuation in the area using tomographic inversion techniques. We also assess the network performance in terms of the seismic ambient noise levels recorded at each station site, and we revise the horizontal orientation of the sensors using surface waves from teleseismic earthquakes.     

Toward mapping land-use patterns from volunteered geographic information

A large number of applications have been launched to gather geo-located information from the public. This article introduces an approach toward generating land-use patterns from volunteered geographic information (VGI) without applying remote-sensing techniques and/or engaging official data. Hence, collaboratively collected OpenStreetMap (OSM) data sets are employed to map land-use patterns in Vienna, Austria. Initially the spatial pattern of the landscape was delineated and thereafter the most relevant land type was assigned to each land parcel through a hierarchical GIS-based decision tree approach. To evaluate the proposed approach, the results are compared with the Global Monitoring for Environment and Security Urban Atlas (GMESUA) data. The results are compared in two ways: first, the texture of the resulting land-use patterns is analyzed using texture-variability analysis. Second, the attributes assigned to each land segment are evaluated. The achieved land-use map shows kappa indices of 91, 79, and 76% agreement for location in comparison with the GMESUA data set at three levels of classification. Furthermore, the attributes of the two data sets match at 81, 67, and 65%. The results demonstrate that this approach opens a promising avenue to integrate freely available VGI to map land-use patterns for environmental planning purposes.     

A Morphological Approach to Predicting Urban Expansion

Many methods for modeling urban expansion are available. Most of these computational models demand a variety of large‐scale environmental and socio‐economic data to investigate the relationship between urban expansion and its driving forces. These requirements are not always fulfilled, particularly in developing countries due to a lack of data availability. This necessitates methods not suffering from data limitations to ease their application. Consequently, this research presents a morphological approach for predicting urban expansion on the basis of spatiotemporal dynamics of urban margins by investigating the interior metropolitan area of Tehran, Iran as a case study. To assess the model's performance, urban expansion is monitored from 1976 to 2012. The proposed model is evaluated to ensure that the prediction performance for the year 2012 is acceptable. For the year 2024, the model predicts Tehran's urban expansion at an overall R² of 88%. Accordingly, it is concluded that: (1) although this approach only inputs urban margins, it represents a suitable and easy‐to‐use urban expansion model; and (2) urban planners are faced with continuing urban expansion.     

Hydrocarbon Vertical Continuity Evaluation in the Cretaceous Reservoirs of Azadegan Oilfield, Southwest of Iran: Implications for Reservoir Geochemistry

A collection of data obtained from analytical methods in geochemistry along with the reservoir engineering and geologic data were used to investigate the reservoir continuity in the Cretaceous Fahliyan, Gadavan, Kazhdumi and Sarvak reservoirs of the super-giant Azadegan oilfield, SW Iran. The geochemical data indicate that the oil samples, with medium to high level of thermal maturity, have been generated from the anoxic marine marl/carbonate source rock(s). The Sargelu (Jurassic) and Garau (Cretaceous) formations are introduced as the main source rocks for the studied oils. The dendrogram obtained from the cluster analysis of high-resolution gas chromatography data introduces two main oil groups including Fahliyan reservoir, and Kazhdumi along with Sarvak/Gadvan reservoirs. This is confirmed by C7 Halpern star diagram, indicating that, the light oil fraction from Fahliyan reservoir is distinct from the others. Also, different pressure gradient of the Fahliyan Formation (over-pressured) relative to other reservoirs (normally-pressured) show the presence of compartments. The relation between toluene/n-heptane and n-heptane/methylcyclohexane represents the compartmentalization due to maturation/evaporative fractionation for Fahliyan and water washing for other studied reservoirs. Also, the impermeable upper part of the Fahliyan Formation and thin interbedded shaly layers in the Kazhdumi, Sarvak and Gadvan formations have controlled reservoir compartmentalization.     

Determination of Key Beds from the Cap Rocks of Oil Reservoirs Using a Novel Method,Case Study: The Gachsaran Formation,Southwest Iran

Lithology and Mineral Resources - The use of key beds in the cap rocks of the oil reservoirs is crucial. Lack of awareness of these key beds will have serious risks and damages. The Gachsaran oil...     

An Exploration of Future Patterns of the Contributions to OpenStreetMap and Development of a Contribution Index

OpenStreetMap (OSM) represents one of the most well‐known examples of a collaborative mapping project. Major research efforts have so far dealt with data quality analysis but the modality of OSM's evolution across space and time has barely been noted. This study aims to analyze spatio‐temporal patterns of contributions in OSM by proposing a contribution index (CI) in order to investigate the dynamism of OSM. The CI is based on a per cell analysis of the node quantity, interactivity, semantics, and attractivity (the ability to attract contributors). Additionally this research explores whether OSM has been constantly attracting new users and contributions or if OSM has experienced a decline in its ability to attract continued contributions. Using the Stuttgart region of Germany as a case study the empirical findings of the CI over time confirm that since 2007, OSM has been constantly attracting new users, who create new features, edit the existing spatial objects, and enrich them with attributes. This rate has been dramatically growing since 2011. The utilization of a Cellular Automata‐Markov (CA‐Markov) model provides evidence that by the end of 2016 and 2020, the rise of CI will spread out over the study area and only a few cells without OSM features will remain.     

Fine-resolution population mapping using OpenStreetMap points-of-interest

Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.     

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Global land cover (LC) maps have been widely employed as the base layer for a number of applications including climate change, food security, water quality, biodiversity, change detection, and environmental planning. Due to the importance of LC, there is a pressing need to increase the temporal and spatial resolution of global LC maps. A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery, which has been developed by the National Geomatics Center of China (NGCC). Although overall accuracy is greater than 80%, the NGCC would like help in assessing the accuracy of the product in different regions of the world. To assist in this process, this study compares the GlobeLand30 product with existing public and online datasets, that is, CORINE, Urban Atlas (UA), OpenStreetMap, and ATKIS for Germany in order to assess overall and per class agreement. The results of the analysis reveal high agreement of up to 92% between these datasets and GlobeLand30 but that large disagreements for certain classes are evident, in particular wetlands. However, overall, GlobeLand30 is shown to be a useful product for characterizing LC in Germany, and paves the way for further regional and national validation efforts.     

Characterizing and monitoring global landscapes using GlobeLand30 datasets: the first decade of the twenty-first century

Global land cover maps are important sources of information for a wide range of studies including land change analysis and climate change research. While the global land cover maps attempt to present a consistent and homogenous data in terms of the production process, the existing datasets offer coarse resolution data, e.g. 1000 m for IGBP DISCover and 300 m for GlobeCover 2009 that is oftentimes challenging. Recently, GlobeLand30 data based on Landsat archive for two timestamps of 2000 and 2010 has been released. It presents a finer spatial resolution of 30 m, which provides numerous opportunities for a wide range of studies. The main objective of this study is to use this dataset for characterizing global land cover patterns, monitoring, and identifying extreme land change cases with their types and magnitude. The findings reveal massive land change patterns including deforestation, desertification, shrinkage of water bodies, and urbanization across the globe. The results and discussions of this research can help policy-makers, environmental planners, ecosystem services providers and climate change researchers to gain finer insights about the forms of global land change. Future research calls for further investigation of the underlying causes of the massive changes and their consequences on our ecosystems and human populations.