International Journal of Environment and Geoinformatics

6-22

Authors: Engin MERİÇ, Niyazi AVSAR, M. Baki YOKEŞ, İpek F. BARUT, Feyza DİNÇER, Cüneyt BİRCAN, Erol KAM

Benthic foraminifer assemblages of the Turkish Aegean coasts have been investigated. Sediment samples from Alibe and Maden Islands (Balıkesir), Kusadası (Aydın), Cesme Ilıca Bay (Izmir) and northwestern coast of Karaburun Peninsula (Izmir) have been analysed. Similarities, as well as dissimilarities were observed between the assemblages from different locations. The Red Sea orginated alien foraminifer Amphistegina lobifera Larsen was found to form a very dense population around the south and west part of a submarine spring water in Kusadası, where as it was replaced by Ammonia compacta Cushman and Elphidium crispum (Linné) assemblages on the northern and eastern parts. Euthymonacha polita (Chapman) has been first recorded in Kusadası and it is also abundant in the Ilıca Bay. The Red Sea originated Coscinospira acicularis (Batsch) has also been first recorded in the Mediterranean from Ilıca Bay. Presence of the individuals with colored tests indicates the presence of heavy metals in spring waters. Aberrant aperture morphologies observed in some of the Vertebralina striata d’Orbigny individuals attracts attention. Abundance of individuals with colored tests, morphological deformities or togetherness, as well as the Red Sea originated species indicates the special environmental and ecological conditions around the submarine springs.

33-44

Authors: Chokri BEDOUI

The study of water erosion has always been a major concern for human societies. Efforts to reduce resulting forms of degradation and to understand their extent have not ceased. This article aims to identify vulnerable and water-erosion-prone areas in the study area. This is achieved through a predictive and descriptive approach leading to the integration of main physical parameters from various sources with direct observation data. The use of remote sensing, GIS and direct observation according to PAP/RAC guidelines has made it possible to prioritize areas at risk of erosion. The basin's lands show a general predisposition to water erosion, with 80% of them experiencing significant, high or very high erosion, including 20% for the last two categories. This situation is linked to a high erodibility of the geological outcrops of the basin (81%), with a low to very low degree of soil protection through vegetation cover (80% of the surface). The descriptive phase shows eroded surfaces of about 18%, a proliferation of ravines (12% of the surfaces) of which 6% are hierarchical ravines and bad lands. The integration phase, while confirming most of the previous data, provides an overview of the trend towards the extension of gullies observed during the descriptive phase to neighboring lands that were not initially subject to erosion. This study shows that a good integration of descriptive and predictive methods can lead to a good understanding of water erosion, which will facilitate solutions to control this phenomenon. It also shows the importance of GIS, remote sensing and their integration with direct field observation in order to produce a document that can be used, at a low cost, as a decision support tool.

54-63

Authors: Demissie Tsega MALLIE, Kefelegn Getahun CHERNET, Tesfaye Debela DUGUMA

This study was aimed at assessing afro-alpine and sub-afro-alpine biodiversity habitat loss and fragmentation of Gugu Mountain Ranges using geospatial techniques. Satellite imageries, key informants and field observation were the major sources of data. The land sat images of 1989, 2001 and 2019 were used to compute change and fragmentation level. ERDAS IMAGINE 2014, ArcGIS 10.4 and FRAGSTAT version 4.2 was used to process data. Image differencing (extent and rate of change), normalized difference vegetation index (NDVI), and area, shape, and aggregation metrics were used to analyze the data. The findings showed that a decline in greenness value and shrinking of afro-alpine and sub-afro-alpine habitat by 1.49% between 1989 and 2001, and 2.62% between 2001 and 2019. Due to this, the total area covered declined from 3540.65 to 1846.98 hectares over the last 30 years. Moreover, the result further revealed that not only decline in size but also there were habitat fragmentations in the study area.

68-79

Authors: Olanrewaju LAWAL

Creation and differentiation of regions are some of the basic tasks in geographic analysis. Regionalisation attempts to create a generalised representation of the processes which is taking place at the level of the amalgamated geographic units. To this end, this study examined the combined use of demographic, economic and poverty characteristics of States across Nigeria to create regions relevant for economic and development planning. The study utilised dependency ratios derived from gridded age structure data, Gross domestic product (GDP), poverty index. K-Means and Max-p algorithm were used for identification of regions. Correlation analysis showed that Youth dependency and total dependency have a strong statistically significant positive relationship (r=0.998, p<0.01) indicating that dependency in the country is driven by youth. The best K-Mean clustering implementation without considering contiguity identified 12 regions with a ratio of between and total sum of squares (RBTSS) of 0.789. The Max-p algorithm was tested with population constrain, the best result identified 9 regions with RBTSS of 0.611 constrained by a minimum population of 8% and implemented with the greedy local search algorithm, this was the same for the simulated annealing approach (SA). With high dissimilarity still common across a handful of the regions identified, a further test was carried out using a minimum bound of 3 States and the SA local search approach. The best result identified 11 contiguous regions with only one region having a relatively high within region dissimilarity and a RBTSS of 0.626. The results confirmed that there are more than 6 regions as currently defined for the country. The analyses showcased an example of knowledge discovery from a spatial dataset which could support regional development planning. From the results, there is a clear need for re-examination of current regions and designing of better-defined regions to ensure that development is guided by evidence.

88-92

Authors: Berkay BAHADUR, Metin NOHUTÇU

Traditionally, ground control points (GCPs) are utilized to determine absolute image orientations indirectly in aerial triangulation. For a long time, differential and relative GNSS (Global Navigation Satellite System) positioning techniques have been extensively used to establish GCPs. In our country, the establishment and measurement of GCPs are instructed in the related regulation based on differential GNSS techniques. One of the two methods described in the related regulation is based on establishing, at least, C3 level networks with maximum base length of 10 km and with minimum 35-minute observation time. In an alternative method, without base length restriction, GCP coordinates can be determined being connected to at least 3 TUSAGA-Active stations and with minimum 120-minute static observation. The expected precision for the coordinates of GCPs are described to be better than 5 cm in horizontal and 6 cm in vertical within the regulation. Although differential techniques can provide highly accurate positioning solutions, they are required at least two receivers to mitigate GNSS error sources. Additionally, positioning accuracy obtained from these techniques are strictly dependent on the distance from reference stations. It is clear that all these raise the operational cost and system complexity of differential GNSS techniques. In recent years, Precise Point Positioning (PPP), which enables centimeter- or millimeter-level positioning accuracy with only one receiver on a global scale, has emerged as an alternative positioning method. Over the last decade, PPP has attracted considerable attention within the GNSS community due to its exceptional benefits such as operational simplicity, cost-effectiveness, elimination of base station requirement. However, the main drawback of PPP is relatively long observation period required to achieve a specific positioning accuracy, for example, nearly 50 min to reach 10 cm or better horizontal accuracy with 30 seconds sampling rate. On the other hand, the completion of GLONASS constellation and the emergence of new satellite systems, such as Galileo and BeiDou, offers considerable opportunities to improve the PPP performance. The combinations of different GNSS constellations, namely multi-GNSS, strength the number and geometry of visible satellites, and therefore, reduces the convergence time significantly. Additionally, the new generation GNSS receivers make possible to collect more observations (even up to 100Hz), which provides abundant data for PPP processing. Taking all these into account, the principal objective of this study is to investigate the usability of PPP in establishing GCPs for aerial triangulation. For this purpose, an experimental test was conducted to evaluate the positioning performance of multi-GNSS PPP with high-frequency GNSS receivers (1 Hz). The results indicate that 5 cm or better horizontal and vertical positioning accuracy can be achieved by multi-GNSS PPP process within approximately 30 minutes using highfrequency GNSS receivers. Considering these results and its operational simplicity, it can be said that PPP is a robust alternative for the establishment of GCPs.

102-107

Authors: Baran GULMEZ, Fatih DEMIRTAS, Irem YILDIRIM, Uğur, M. LELOĞLU, Mustafa YAMAN, Eylem T. GUNEYI

Features are distinctive landmarks of an image. There are various feature detection and description algorithms. Many computer vision algorithms require matching of features from two images. Large number of correct matches with homogeneous distribution in the images is needed for robustness of the image matching. The matches are generally obtained using a feature distance threshold and ambiguous matches are rejected using a ratio test. This paper proposes a method that can be added to image matching pipeline for enhancing homogeneous distribution and increasing the number of matched feature points. After successfully matching an image pair, spatially close feature points go through an elimination process which aims to decrease ambiguity at the second matching step. Then, a coarse geometric transformation between two images is calculated, through which the detected feature points in one image (i.e. the moving image) are projected to the other image (i.e. the fixed image). Then, feature points from the moving image are matched to neighboring feature points of the fixed image within a pre-determined spatial distance. This narrows down the possible candidates and enables less correct matches being rejected because of the ratio test. The effectiveness and feasibility of our method is demonstrated with experiments on images acquired from a drone camera during flight.