Land transformation assessment using the integration of remote sensing and GIS techniques: a case study of Al-Anbar Province,Iraq

Human activities and climate changes significantly affect our environment, altering hydrologic cycles. Several environmental, social, political, and economical factors contribute to land transformation as well as environmental changes. This study first identified the most critical factors that affect the environment in Al-Anbar city including population growth, urbanization expansion, bare land expansion, and reduction in vegetation cover. The combination of remote sensing data and fuzzy analytic hierarch process (Fuzzy AHP) enabled exploration of land transformations and environmental changes in the study area during 2001 to 2013 in terms of long and short-term changes. Results of land transformation showed that the major changes in water bodies increased radically (94 %) from the long-term change in 2001 to 2013 because of water policies. In addition, the urban class expanded in two short-term periods (2001–2007 and 2007–2013), representing net changes of 46 and 60 %, respectively. Finally, barren land showed 25 % reduction in the first period because of the huge expansion of water in the lake; a small percentage of growth gain was observed in the second period. Based on the land transformation results, the environmental degradation assessment showed that the study area generally had high level of environmental degradation. The degradation was mostly in the center and the north part of the study area. This study suggested for further studies to include other factors that also responsible for environmental degradation such as water quality and desertification threatening.     

Using high-resolution satellite imagery to provide a relief priority map after earthquake

After the earthquake occurrence, collecting correct information about the extent of damage is essential for managing critical conditions and allocating limited resources. The prepared building damage maps sometimes bring about waste of time required for rescuing individuals under the rubble by wrongly conducting rescue teams toward regions with a lower rescue priority. In this research, an algorithm based on using a proposed standard at database level was developed to prioritize damaged buildings by considering five key elements of land use type, the degree of damage to buildings, the land use differentiation index, time of the highest population density in each land use, and time of disaster’s incidence. The steps of the proposed method which was implemented in the MATLAB environment include: detecting buildings on the pre- and post-event imagery, implementing texture features for each candidate building, choosing the optimal features by genetic algorithm, determining the degree of building damage in three classes of negligible damage, substantial damage, and heavy damage by using the difference between chosen features as inputs of the designed neurofuzzy inference system. Data collected from field observations were compared to the output obtained from the proposed algorithm. This comparison presented a general accuracy of 88% and Kappa coefficient of 79% in the classification of buildings into three damage classes. The proposed standard then was used for classifying damaged buildings into relief priorities of high, medium, and low. Findings revealed that the relief priority map could be a basis for correct guidance of relief and rescue teams during crucial times following earthquakes.     

GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique

Toroud Watershed in Semnan Province, Iran is a prone area to gully erosion that causes to soil loss and land degradation. To consider the gully erosion, a comprehensive map of gully erosion susceptibility is required as useful tool for decreasing losses of soil. The purpose of this research is to generate a reliable gully erosion susceptibility map (GESM) using GIS-based models including frequency ratio (FR), weights-of-evidence (WofE), index of entropy (IOE), and their comparison to an expert knowledge-based technique, namely, Analytic Hierarchy Process (AHP). At first, 80 gully locations were identified by extensive field surveys and Google Earth images. Then, 56 (70%) gully locations were randomly selected for modeling process, and the remaining 26 (30%) gully locations were used for validation of four models. For considering geo-environmental factors, VIF and tolerance indices are used and among 18 factors, 13 factors including elevation, slope degree, slope aspect, plan curvature, distance from river, drainage density, distance from road, lithology, land use/land cover, topography wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), and slope–length (LS) were selected for modeling aims. After preparing GESMs through the mentioned models, final maps divided into five classes including very low, low, moderate, high, and very high susceptibility. The receiver operating characteristic (ROC) curve and the seed cell area index (SCAI) as two validation techniques applied for assessment of the built models. The results showed that the AUC (area under the curve) in training data are 0.973 (97.3%), 0.912 (91.2%), 0.939 (93.9%), and 0.926 (92.6%) for AHP, FR, IOE, and WofE models, respectively. In contrast, the prediction rates (validating data) were 0.954 (95.4%), 0.917 (91.7), 0.925 (92.5%), and 0.921 (92.1%) for above models, respectively. Results of AUC indicated that four model have excellent accuracy in prediction of prone areas to gully erosion. In addition, the SCAI values showed that the produced maps are generally reasonable, because the high and very high susceptibility classes had very low SCAI values. The results of this research can be used in soil conservation plans in the study area.     

Experimental study of the effect of material and arrangement of electrodes and voltage on the electro-remediation of saturated clays containing chloride and sulfate ions

Electro-remediation process (ERP) is used for dewatering and decontaminating of the soil and improving its mechanical properties. In this study, the effect of voltage, electrode materials, and their arrangement on ERP were studied. The saturated clayey soil was remolded in four boxes at a dry density of 16.2 kN/m³. The process was performed in three stages. The chemical tests at the end of ERP indicated that the absorption of chloride ions in the anode may differ from 89 to 642% due to electro-migration phenomenon. The pH of the soil decreases in anode zone and increases towards the cathode zone. Up to 92% of the sulfate ions were transported and accumulated in the anode. However, in highly contaminated soil, the absorption ratio is 5% in the cathode due to the domination of electro-osmosis process. It was indicated that the electro-osmosis permeability coefficient (K_e) depends on the applied voltage, the soil type, and temperature and its reduction versus time is inversely related to applied voltage. Also, sulfate ions reduced by 74% in the anode due to a change in absorption direction. The results showed that the optimum configuration is linear along with a voltage of 18 V.     

Vertical distribution and source identification of polycyclic aromatic hydrocarbons in anoxic sediment cores of Chini Lake, Malaysia: Perylene as indicator of land plant-derived hydrocarbons

Four anoxic sediment cores were collected from Chini Lake, Malaysia in order to investigate the variability of polycyclic aromatic hydrocarbon (PAH) and perylene concentrations. The study also determined significant differences of perylene concentrations in different sediment layers. Total PAH concentrations ranged from 248 to 8098 ng g⁻¹ in the samples. Diagnostic PAH ratios such as methylphenanthrenes/phenanthrene (MP/P), phenanthrene/anthracene (P/A) and fluoranthene/(fluoranthene + pyrene) (Fl/(Fl + Py) revealed a dominance of pyrogenic influences and partial petrogenic inputs to the top sediment layers. Perylene concentrations were high in the top layers (<12 cm) and increased with increasing depth. There is a significant positive correlation (r = 0.705, p = 0.01) between perylene concentrations and TOC. Analysis of variance (ANOVA) and LSD revealed significant differences (p < 0.05) in TOC-normalized perylene concentrations between the upper (<12 cm) and bottom layers (>12 cm). The average perylene concentrations accounted for 26–50% (0–12 cm) and 50–77% (12–36 cm) of pentacyclic-aromatic hydrocarbon isomers (PAI) present whereas it made up 10–34% (0–12 cm) and 46–66% (12–36 cm) of the total PAH. The average pyrene concentrations decreased with increasing depth and accounted for 62% (0–3 cm), 20–23% (3–12 cm) and 3–1.4% (12–36 cm) of perylene present. The results of hierarchical cluster analysis based on these ratios suggested different input sources for the top and bottom layers. It is concluded that the activity of termites on woody plants produced perylene which is supplied to the lake by run-off from the heavy and frequent rains in this Asian tropical climate. In addition, there was also in situ formation of perylene in the bottom layers due to diagenetic processes.     

Is climate change impacting Rideau Canal Skateway,the world’s longest skating rink?

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