Automatic detection of residential buildings using LIDAR data and multispectral imagery

This paper presents an automatic building detection technique using LIDAR data and multispectral imagery. Two masks are obtained from the LIDAR data: a ‘primary building mask’ and a ‘secondary building mask’. The primary building mask indicates the void areas where the laser does not reach below a certain height threshold. The secondary building mask indicates the filled areas, from where the laser reflects, above the same threshold. Line segments are extracted from around the void areas in the primary building mask. Line segments around trees are removed using the normalized difference vegetation index derived from the orthorectified multispectral images. The initial building positions are obtained based on the remaining line segments. The complete buildings are detected from their initial positions using the two masks and multispectral images in the YIQ colour system. It is experimentally shown that the proposed technique can successfully detect urban residential buildings, when assessed in terms of 15 indices including completeness, correctness and quality.     

Vulnerability Assessment and Earthquake Risk Mapping in Part of North Iran Using Geospatial Techniques

Earthquakes cause huge loss of lives and infrastructure every year in Iran. Many settlement areas (urban & rural) as well as Tehran, the capital city of Iran are located in the hazardous area. This research deals with the earthquake risk assessment and mapping based on recent remote sensing information on a GIS platform. The study area is part of Central Alborz in southern Caspian Sea and north of capital city of Tehran called Marzanabad area. It is a potentially high-risk zone as several earthquakes have occurred in the past. The study’s main objective is to develop an Earthquake Risk Map at the scale of 1:25,000 to identify high-risk zone and vulnerability areas to the settlements and infrastructure of area. Digital lineaments wear extraction and analysis for identification the faults using several RADAR and optical images with spatial analysis techniques. The probable faults were detected by superimposition of the lithological and geomorphologic features and their variance over the lineaments in a GIS environment. This research work involved fault identification on the remote sensed dataset as well as field studies and the risky areas were classified in the vicinity of the faults by applying different buffer with specifying distance of the source/site of risk to fault location. Statistical analysis of Earthquake Risk Map (ERM) by GIS indicated that 32% of the total area with about 66% of settlements and 52% of population is located in strongly high-risk and high-risk zone. Moderately low risk and low risk zones cover 38.67% of total area, which is free of settlements as well as population. The Earthquake map elaborated in this research work will be a useful tool for disaster management as well as urban and regional planning of future activities in the area.     

An Introduction to MODISI and SCMOD Methods for Correction of the MODIS Snow Assessment Algorithm

Detection, monitoring and precise assessment of the snow covered regions is an important issue. Snow cover area and consequently the amount of runoff generated from snowmelt have a significant effect on water supply management. To precisely detect and monitor the snow covered area we need satellite images with suitable spatial and temporal resolutions where we usually lose one for the other. In this study, products of two sensors MODIS and ASTER both on board of TERRA platform having low and high spatial resolution respectively were used. The objective of the study was to modify the snow products of MODIS by using simultaneous images of ASTER. For this, MODIS snow index image with high temporal resolution were compared with that of ASTER, using regression and correlation analysis. To improve NDSI index two methods were developed. The first method generated from direct comparison of ASTER averaged NDSI with those of MODIS (MODISI). The second method generated by dividing MODIS NDSI index into 10 codes according to their percentage of surface cover and then compared the results with the difference between ASTER averaged and MODIS snow indices (SCMOD). Both methods were tested against some 16 MODIS pixels. It is found that the precision of the MODISI method was more than 96%. This for SCMOD was about 98%. The RMSE of both methods were as good as 0.02.     

Heavy metal pollution and human health risk assessment for exposure to surface soil of mining area: a comprehensive study

Climate change projections indicate an increase in intense rainfall events with consequent river flooding, which could lead to devastating natural disaster     

Makran ophiolitic basalts (SE Iran) record Late Cretaceous Neotethys plume-ridge interaction

ABSTRACT

The Makran complex in southeast Iran provides a spectacular subduction-related accretionary complex to understand the mechanism of oceanic accretion and the evolution of subduction zones. In this paper, we present new major and trace element data as well as isotopic compositions of mafic volcanic blocks from the Makran ophiolitic mélange complex (OMC). Our aim is to assess the genesis of these rocks and discuss their implications on the evolution of Neotethys Ocean. These volcanic blocks are composed mainly of basalts with minor trachytes. The Makran lavas are occasionally interlayered with tuff layers. Zircons from these tuffs give U-Pb ages of 95 Ma, which is well in accordance with the reposted microfossil data for the interlayered pelagic limestones with pillow lavas. Makran basalts can be geochemically subdivided into four groups; normal to transitional MORB, enriched-MORB, Plume-type MORB and alkaline (-OIB-like) basalts. The OIB-like pillow lavas are represented by high values of Th/Tb (6.3–7.4) which are higher than other basalts (group 1 = 0.3–0.8; groups 2 = 0.7–1.6; group 3 = 1.58–1.36).¹⁴³Nd/¹⁴⁴Nd_(t) ratios for basalts ranges from 0.51247 to 0.51292, whereas ⁸⁷Sr/⁸⁶Sr_(t) isotopic composition of the OMC lavas varies from 0.704433 to 0.709466. The Pb isotopic composition of the lavas are quite high, ranging from 15.49–15.66 for ²⁰⁷Pb/²⁰⁴Pb_(t), 18.09–19.12 for ²⁰⁶Pb/²⁰⁴Pb_(t) and 37.80–39.23 for ²⁰⁸Pb/²⁰⁴Pb_(t). The chemistry of these rocks suggests that they were formed most likely in an oceanic setting with clear plume-ridge interaction. These rocks can form from partial melting of a highly heterogeneous mantle source, which is extensively metasomatized with deep mantle OIB-type components. We suggest these rocks have been generated in an oceanic ridge with plume-ridge interaction, similar to the Iceland-Reykjanes Ridge, before being fragmented and accreted into the Makran accretionary complex.     

Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network

Al-Mansourieh zone is a part of Al-Khalis City within the province of Diyala and located in the Diyala River Basin in eastern Iraq with a total area about 830 km2.Groundwater is the main water source for agriculture in this zone.Random well drilling without geological and hydraulic information has led the most of these wells to dry up quickly.Therefore,it is necessary to estimate the levels of groundwater in wells through observed data.In this study,Alyuda NeroIntelligance 2.1 software was applied to predict the groundwater levels in 244 wells using sets of measured data.These data included the coordinates of wells(x,y),elevations,well depth,discharge and groundwater levels.Three ANN structures(5-3-3-1,5-10-10-1 and 5-11-11-1)were used to predict the groundwater levels and to acquire the best matching between the measured and ANN predicted values.The coefficient of correlation,coefficient determination(R2)and sum-square error(SSE)were used to evaluate the performance of the ANN models.According to the ANN results,the model with the three structures has a good predictability and proves more effective for determining groundwater level in wells.The best predictor was achieved in the structure 5-3-3-1,with R2 about 0.92,0.89,0.84 and 0.91 in training,validation,testing and all processes respectively.The minimum average error in the best predictor is achieved in validation and testing processes at about 0.130 and 0.171 respectively.On the other hand,the results indicated that the model has the potential to determine the appropriate places for drilling the wells to obtain the highest level of groundwater.     

Comparison and Hydrochemical Characterization of Groundwater Resources in the Arabian Peninsula: A Case Study of Al-Baha and Al-Qassim in Saudi Arabia

Water Resources - The objective of this study was to compare and assess the quality and study the hydrochemistry of groundwater in the Al-Baha and Al-Qassim areas, representing the Arabian Shield...     

Nonlinear dynamic tests of a reinforced concrete frame building at different damage levels

This paper discusses the dynamic tests of a two-story infilled reinforced concrete (RC) frame building using an eccentric-mass shaker. The building, located in El Centro, CA, was substantially damaged prior to the tests due to the seismic activity in the area. During the testing sequence, five infill walls were removed to introduce additional damage states and to investigate the changes in the dynamic properties and the nonlinear response of the building to the induced excitations. The accelerations and displacements of the structure under the forced and ambient vibrations were recorded through an array of sensors, while lidar scans were obtained to document the damage. The test data provide insight into the nonlinear response of an actual building and the change of its resonant frequencies and operational shapes due to varying damage levels and changes of the excitation amplitude, frequency, and orientation.     

Location of Areas Prone to Flood Spreading Using GIS (Ilam Province,Iran)

Water Resources - The present study aimed to locate the areas prone to flood spreading in order to manage surface water resources. Therefore, the information layers of slope, land capability,...     

Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model

Stochastic Environmental Research and Risk Assessment - Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely...