bluuebunny/crossref_metadata_2025 huggingface dataset:
Satzbaupläne, Valenzwandel und das historisch syntaktische Verbwörterbuch am Beispiel des Verbs achten
Authors: Greule Albrecht
Published: October 2019

Der Beitrag befasst sich mit der historischen deutschen Syntax, insbesondere mit der Verbumgebung, ihrer systematischen Erfassung auf der Grundlage der Valenz-Theorie und ihrer Entwicklung im Verlauf der deutschen Sprachgeschichte vom Althochdeutschen bis zum Deutsch der Gegenwart. Dabei geht es um die Erfassung sowohl der Tiefenkasusrahmen als auch der Satzbaupläne der in allen sprachgeschichtlichen Perioden belegten deutschen Verben. Als Beispiel dient das Verb neuhochdeutsch achten und seine historischen Entsprechungen (ahd. ahtôn, mhd. ahten, frühnhd. achten). Es wird gezeigt, wie aus den historischen, online verfügbaren Wörterbüchern die Struktur des Eintrags unter dem Stichwort achten in einem Historisch syntaktischen Wörterbuch (kurz HSVW) abgeleitet werden kann. Abschließend wird auf der Grundlage der im Historisch syntaktischen Wörterbuch gespeicherten Informationen das diachrone Bedeutungsspektrum des Verbs achten erfasst und versucht, daraus den Valenzwandel bei achten zu beschreiben.
Poultry marketing in Ibadan–north Local Government Area of Oyo State, Nigeria
Authors: Okunlola O.O.
Published: October 2019

Some government policies in Nigeria poultry industry fail partly because there is insufficient data on all the key players in the industry. This study was therefore, to collect information about poultry meat marketers and their activities in Ibadan–North Local Government Area of Oyo State, Nigeria. Questionnaires were randomly administered to a total of 100 poultry meat marketers in the study area to elicit information on their business. The collected data were processed and subjected to descriptive statistics using frequency counts and percentage. The result obtained showed that majority of the respondents were female (74%), had one form of education or the other and were less than 10 years in the business (48%). Also, majority of the respondents obtained poultry products from local farms (46%), were retailers (62%) and had target sales around Christmas and new year (56%) festive periods with 100-199kg (52%) as the average weekly sale. Poor meat transportation facilities (50%), poor electricity supply (54%) and seasonal sales (52%) were major problems encountered by the respondents concerning purchases, storage and sales of poultry products respectively. Concerning poultry products being contraband in Nigeria, 64% of the respondents claimed they were aware while 62.5% of the culprits gave economic reasons for their illicit activities.Keywords: poultry meat, meat purchases, meat sales, meat storage, meat marketing
Effect of varying rates of organic fertilizer on the growth and fibre yield of Kenaf (Hibiscus cannabinus L.)
Authors: Olanipekun S.O. , Togun A.O. , Adejumo S.A. , Adeniyan O.N.
Published: October 2019

Hibiscus cannabinus L. or kenaf is a fast growing and multipurpose annual crop planted for its horticultural and economic importance. Little is known about its response to different rates of organic fertilizer in Nigeria. The objective of the study was therefore to evaluate the effects of different rates of commercially produced organic fertilizer (Aleshinloye organic fertilizer to be precise) on crop growth rate, vegetative growth and biomass accumulation in kenaf Five different rates of organic fertilizer equivalent to: 0, 70, 100, 130 and 160 kg N ha-1 replicated three times were used in a randomized complete block design (RCBD). There were contrasting results on the effects of fertilizer rates on growth and physiology of kenaf. Significant effects were observed for some agronomic parameters such as plant height, stem diameter and fibre yield. The application of organic fertilizer at varying rates increased the bast fibre yield of the Ifeken 400 variety of kenaf from 67.29 g/m2 (control) to 98.70, 147.88 and 157.40 g/m2, in those grown on soil amended with 70, 100 and 130 kg N ha-1 of organic fertilizer respectively. Organic fertilizer could be utilized for the cultivation of kenaf in Nigeria but the rate to apply depends on the nutrient content of the organic fertilizer.Keywords: Kenaf, Growth, Organic fertilizer, rates and fibre yield
Genetic evaluation of cowpea (Vigna unguiculata (L.) Walp.) using morphological characters
Authors: Olowolafe M.O. , Akinyosoye S.T. , Adetumbi J.A. , Ogunniyan D.J. , Owolade O.F. , Anjorin F.B. , Agbeleye O.A.
Published: October 2019

The experiment was conducted to characterize cowpea (Vigna unguiculata (L.) genotypes based on morphological characters. The experiment was carried out at the screen house of the Institute of Agricultural Research and Training (IAR&T), Moor Plantation, Ibadan in 2015 and 2016 using nine (9) cowpea varieties. Soil was sieved with 2mm sieved and sterilized. The experiment was laid out in Completely Randomized Design (CRD) with four replicates. Morphological data were collected based on descriptor for cowpea (IBPGR, 1983). For quantitative traits, analysis of variance revealed that genotype and years effect mean squares from pooled data were highly significantly different for days to first and 50% flowering (P ˂ 0.01), interaction between genotype and years effect also had significant effect on days to first and days to 50% flowering (P ˂ 0.05).Higher significant values were obtained for terminal leaf width, pod weight, pod length, days to physiological maturity, total seed weight per plant, 100 seed weight and number of seed per plant in 2015 than the year 2016. However there were no significant differences obtained for terminal leaf length, pod per peduncle, number of pod per plant, seed length, seed width, seed thickness, and number of seed per pod in both years. Qualitative characters results show thatgenotypic effect, year effect and their interactions were not significant for plant vigour. Modupe, Ife 98-12, Oloyin-Pelebe, Ife BPC, Oloyin-Nla and Sokoto genotypes had intermediate twining tendency. Drum and Mulla had pronounced twining tendency while Ife Brown had no twining tendency. Based on pooled cluster analysis the dendrogram grouped the varieties in two different major clusters A and B. Cluster A comprised of seven genotypes; Oloyin-Nla, Oloyin-Pelebe, Sokoto, Ife BPC, Modupe, Ife 98-12 and Ife Brown. Cluster B comprised of two genotypes; Drum and Mulla. This implies that genotypes within the same cluster had similar genetic background, while genotypes in cluster A are distant on the chromosome as compare to genotypes on cluster B respectively.Keywords: Genetic evaluation, Vigna unguiculata, Cowpea, Morphological Characters
Divisibility in beta N and *N
Authors: Sobot Boris
Published: October 2019

The paper first covers several properties of the extension of the divisibility relation to a set *N of nonstandard integers, including an analogue of the basic theorem of arithmetic. After that, a connection is established with the divisibility in the Stone–Čech compactification βN, proving that the divisibility of ultrafilters introduced by the author is equivalent to divisibility of some elements belonging to their respective monads in an enlargement. Some earlier results on ultrafilters on lower levels on the divisibility hierarchy are illuminated by nonstandard methods. Using limits by ultrafilters we obtain results on ultrafilters above these finite levels, showing that for them a distribution by levels is not possible.
OPTIMIZATION OF EMERGENCY EVACUATION IN FIRE BUILDING BY INTEGRATED BIM AND GIS
Authors: Atyabi S. , Kiavarz Moghaddam M. , Rajabifard A.
Published: October 2019

Abstract. Building Information Model (BIM) is a database, which makes a detailed 3D geometrical model with rich semantic information of building, go beyond the standard Computer-Aided Design approach. BIM could be as an ideal source to store, formation model and analyses spatial information of internal environments. GIS is an effective tool for analysing the constant and dynamic variables in small areas such as indoor environments. Therefore, integration of the BIM and GIS is useful for integrating the internal environment and networks for designing optimal routes in emergency evacuation problems. Increasing complexity of high-rise buildings and underground structures lead to much more complication in urban disaster management. One of the main challenges in disaster management and emergency evacuation is to guide people in optimal routes to reach safe areas. In this paper, in order to create optimal routes in internal environments and create an internal geometric network model based on the internal nodes and edges, BIM of a 3D commercial building located in the 8th district of Tehran, was made and transferred to the ARCGIS that enables us to use routing algorithms to find the optimize routes between both internal positions. Flammability, occupancy load, width of exit access and distance of fire point criteria were considered to find the safest routes in emergency evacuation times. Network Analysis and Ant Colony's algorithm were applied for finding the safest routes. The results indicated that the Network Analysis was better in terms of processing time and finding short and safe paths related to the Ant Colony's algorithm.
INVESTIGATING MANGROVE FRAGMENTATION CHANGES USING LANDSCAPE METRICS
Authors: Baharlouii M. , Mafi Gholami D. , Abbasi M.
Published: October 2019

Abstract. Generally, investigation of long-term mangroves fragmentation changes can be used as an important tool in assessing sensitivity and vulnerability of these ecosystems to the multiple environmental hazards. Therefore, the aim of this study was to reveal the trend of mangroves fragmentation changes in Khamir habitat using satellite imagery and Fragstats software during a 30-year period (1986–2016). To this end, Landsat images of 1986, 1998, and 2016 were used and after computing the normalized difference vegetation index (NDVI) to distinguish mangroves from surrounding water and land areas, images were further processed and classified into two types of land cover (i.e., mangrove and non-mangrove areas) using the maximum likelihood classification method. By determining the extent of mangroves in the Khamir habitat in the years of 1986, 1998 and 2017, the trend of fragmentation changes was quantified using CA, NP, PD and LPI landscape metrics. The results showed that the extent of mangroves in Khamir habitat (CA) decreased in the period post-1998 (1998–2016). The results also showed that, the NP and PD increased in the period of post-1998 and in contrast, the LPI decrease in this period. These results revealed the high degree of vulnerability of mangroves in Khamir habitat to the drought occurrence and are thus threatened by climate change. We hope that the results of this study stimulate further climate change adaptation planning efforts and help decision-makers prioritize and implement conservative measures in the mangrove ecosystems on the northern coasts of the PG and the GO and elsewhere.
DESIGNING A CONTEXT-AWARE RECOMMENDER SYSTEM IN THE OPTIMIZATION OF THE RELIEF AND RESCUE
Authors: Bahrami N. , Argany M. , Samani N. N. , Vafaeinejad A. R.
Published: October 2019

Abstract. The context-aware is the knowledge that leads to better cognition and recognition of the environment, objects and factors, and the way of communication and interactions between them. As a result, it can have a great impact in providing appropriate solutions to various problems. It is possible to integrate consciousness into relief and rescue discussions and to take steps to improve and make realistic solutions. In this study, this issue was addressed in the earthquake crisis, due to a large number of seismic faults in Iran, is one of the major crises in Iran and many parts of the world. Hence, the contexts of rescuers, teams, and environment as the main textures in the above-mentioned issue are investigated and their relationship with each other and the priorities of activities and locations by identifying specialties and the physical and situational conditions of the relief workers, and an algorithm was designed and optimized to optimize the allocation of the relief workers to the affected areas and the necessary activities. Finally, the improvement of the 2.4 fold results of the algorithm and the proposed structure of this research resulted in the ratio of non-use of this algorithm.
INDIVIDUAL TREE DETECTION AND DETERMINATION OF TREE PARAMETERS USING UAV-BASED LIDAR DATA
Authors: Bayat F. , Arefi H. , Alidoost F.
Published: October 2019

Abstract. Forest inventory provides comprehensive information about the geometric and biometric state of forests as well as vegetated areas. In this study, a point-based 3D method is presented for tree detection as well as measuring of structural properties of forests such as the tree height, tree position and canopy area using high resolution point cloud which is provided by an Unmanned Aerial Vehicle (UAV)-based LiDAR sensor. The proposed method is based on the density of point cloud and 2D and 3D distance measurements. It includes three main steps as pre-processing, tree detection, and extraction of tree structural attributes. After generating a canopy height model, an image is created based on the density of point cloud. Next, points are classified based on 2D and 3D distance measurements, sequentially, from the highest to the lowest. According to the results, the rate of tree detection is about 95% and the main structural parameters of a tree such as the position, height, area and length of the canopy are estimated with the accuracy of 1.97 m, 0.36 m, 12.78 m2 and 0.79 m, respectively.
COMBINATION OF METEOROLOGICAL INDICES AND SATELLITE DATA FOR DROUGHT MONITORING IN TWO DIFFERENT ENVIRONMENTS IN IRAN
Authors: Behifar M. , Kakroodi A. A. , Kiavarz M. , Amiraslani F.
Published: October 2019

Abstract. The main problem using meteorological drought indices include inappropriate distribution of meteorological stations. Satellite data have reliable spatial and temporal resolution and provide valuable information used in many different applications. The Standardized precipitation index has several advantages. The SPI is based on rainfall data alone and has a variable time scale and is thus conducive to describing drought conditions for different application.This study aims to calculate SPI using satellite precipitation data and compare the results with traditional methods. To do this, satellite-based precipitation data were assessed against station data and then the standardized precipitation index was calculated. The results have indicated that satellite-based SPI could illustrate drought spatial characteristic more accurate than station-based index. Also, the standardized property of the SPI index allows comparisons between different locations, which is one of the remote sensing drought indices limitations.
ESTIMATING WATER LEVEL IN THE URMIA LAKE USING SATELLITE DATA: A MACHINE LEARNING APPROACH
Authors: Boueshagh M. , Hasanlou M.
Published: October 2019

Abstract. Lakes play a pivotal role in the development of cities and have major impacts on the ecosystem balancing of the area. Remote sensing techniques and advanced modeling methods make it possible to monitor natural phenomena, such as lakes’ water level. The ecosystem of Urmia Lake is one of the most momentous ecosystems in Iran, which is almost close-ended and has become a global environmental issue in recent years. One of the parameters affecting this lake water level is snowfall, which has a key role in the fluctuations of its water level and water resources management. Hence, the purpose of this paper is the Urmia Lake water level estimation during 2000–2006 using observed water level, snow cover, direct precipitation, and evaporation. For this purpose, Support Vector Regression (SVR), which is the most outstanding kernel method (with various kernel types), has been used. Furthermore, four scenarios are considered with different variables as inputs, and the output of all scenarios is the water level of the lake. The results of training and testing data indicate the substantial impact of snow on retrieving the water level of the Urmia Lake at the desired period, and due to the complexity of the data relationships, the Gaussian kernel generally had better results. On the other hand, Quadratic and Cubic kernels did not work well. The fourth scenario, with RBF kernel has the best results [Training: R2 = 97% and RMSE = 0.09 m, Testing: R2 = 96.97% and RMSE = 0.08 m].
A REVIEW ON SPATIAL QUALITY ASSESSMENT METHODS FOR EVALUATION OF PAN-SHARPENED SATELLITE IMAGERY
Authors: Dadras Javan F. , Samadzadegan F. , Mehravar S. , Toosi A.
Published: October 2019

Abstract. Nowadays, high-resolution fused satellite imagery is widely used in multiple remote sensing applications. Although the spectral quality of pan-sharpened images plays an important role in many applications, spatial quality becomes more important in numerous cases. The high spatial quality of the fused image is essential for extraction, identification and reconstruction of significant image objects, and will result in producing high-quality large scale maps especially in the urban areas. This paper introduces the most sensitive and effective methods in detecting the spatial distortion of fused images by implementing a number of spatial quality assessment indices that are utilized in the field of remote sensing and image processing. In this regard, in order to recognize the ability of quality assessment indices for detecting the spatial distortion quantity of fused images, input images of the fusion process are affected by some intentional spatial distortions based on non-registration error. The capabilities of the investigated metrics are evaluated on four different fused images derived from Ikonos and WorldView-2 initial images. Achieved results obviously explicate that two methods namely Edge Variance Distortion and the spatial component of QNR metric called Ds are more sensitive and responsive to the imported errors.
THERMAL 3D MODELS ENHANCEMENT BASED ON INTEGRATION WITH VISIBLE IMAGERY
Authors: Dadras Javan F. , Savadkouhi M.
Published: October 2019

Abstract. In the last few years, Unmanned Aerial Vehicles (UAVs) are being frequently used to acquire high resolution photogrammetric images and consequently producing Digital Surface Models (DSMs) and orthophotos in a photogrammetric procedure for topography and surface processing applications. Thermal imaging sensors are mostly used for interpretation and monitoring purposes because of lower geometric resolution. But yet, thermal mapping is getting more important in civil applications, as thermal sensors can be used in condition that visible sensors cannot, such as foggy weather and night times which is not possible for visible cameras. But, low geometric quality and resolution of thermal images is a main drawback that 3D thermal modelling are encountered with. This study aims to offer a solution for to fixing mentioned problem and generating a thermal 3D model with higher spatial resolution based on thermal and visible point clouds integration. This integration leads to generate a more accurate thermal point cloud and DEM with more density and resolution which is appropriate for 3D thermal modelling. The main steps of this study are: generating thermal and RGB point clouds separately, registration of them in two course and fine level and finally adding thermal information to RGB high resolution point cloud by interpolation concept. Experimental results are presented in a mesh that has more faces (With a factor of 23) which leads to a higher resolution textured mesh with thermal information.
A HYBRID OPTIMIZATION METHOD FOR VEHICLE ROUTING PROBLEM USING ARTIFICIAL BEE COLONY AND GENETIC ALGORITHM
Authors: Davoodi M. , Malekpour Golsefidi M. , Mesgari M. S.
Published: October 2019

Abstract. Vehicle Routing Problem is one of the classic problems in GIS (Geospatial Information System) which had been studied for long times. An answer can be accepted as a good solution if it would be able to optimize the total length of the route or decrease the number of vehicles. A VRP defines finding the optimum route for some vehicles that serve to some customers and return to the service center. This problem is economically important because the cost and the time of serving to costumers are related to optimization of the problem’s answer. Furthermore, there are many problems like BUS management, Post pickup and delivery system and other servicing systems, which are technically similar to VRP. The aim of these problems is finding a composition of optimum routes between server and costumers. In addition, as the cost is related to time, finding shortest path means decreasing cost serving and decreasing time. In this article, a hybrid model using Artificial Bee Colony and Genetic Algorithm is proposed to solve VRP. In the first step, Artificial Bee Colony has been used to find a solution for five vehicles. The scout and the onlooker bees produced in 8 modes by two methods including the nearest neighborhood and the wide neighborhood. In the second step, the Genetic Algorithm helps to optimize the solutions. The results show that the production of the scout bees is the most effective factor in the answers to the problem and helps greatly converging the answers as soon as possible.
STRATEGIES OF DIGITAL KNOWLEDGE’S REPRESENTATION: FROM DE-MORPHOGENESIS AT MSH-BIM. OPTICAL, THEATRICAL ILLUSION AND PERSPECTIVE IN THE NEAPOLITAN SCHOOL’S ARCHITECTURE DRAWING
Authors: De Masi A.
Published: October 2019

Abstract. The study illustrates the architectural-scenographic construction and drawing techniques related to theatrical scenic illusionism of the seventeenth and eighteenth centuries in relationship with some Neapolitan monumental buildings. In addition the develop of methodologies of intervention DSH (De-Morphogenesis, Semantic, Hypermedia Systems) – BIM (Building Information Modelling) in the historical reality as new concept of “Smart Heritage”. Therefore, the objective of the research is the construction of the visual model of documentation defined as “Digital Platform for the Knowledge’s Representation” of classification and comparison data. The methodological operations will be based on the “Morphology of the Form”, the “De-Morphogenesis of the Architecture”, “Semantic Values of the Architecture by On-Morphogenesis and “Hypermedia Systems and Cloud Computing Platforms”. The research highlighted for architectural elements: 1) Lexicon, Syntax and Semantics. 2) Ripercussion on representative categories. 3) Solutions adopted to check the conservation condition. 4) manipulation based on dimensional parameters. 5) 3D parametric digital models with appropriate stylistic features in the same way as reverse engineering solutions.
ESTIMATION OF SOIL MOISTURE AND EARTH’S SURFACE TEMPERATURE USING LANDSAT-8 SATELLITE DATA
Authors: Entezari M. , Esmaeily A. , Niazmardi S.
Published: October 2019

Abstract. Soil moisture estimation is essential for optimal water and soil resources management. Surface soil moisture is an important variable in the natural water cycle, which plays an important role in the global equilibrium of water and energy due to its impact on hydrological, ecological and meteorological processes. Soil moisture changes due to the variability of soil characteristics, topography and vegetation in time and place. Soil moisture measurements are performed directly using in situ methods and indirect, by means of transfer functions or remote sensing. Since in-site measurements are usually costly and time-consuming in large areas, we can use methods such as remote sensing to estimate soil moisture at very large scales. The purpose of this study is to estimate soil moisture using surface temperature and vegetation indices for large areas. In this paper, ground temperature was calculated using Landsat-8 thermal band for Mashhad city and was used to estimate the soil moisture content of the study area. The results showed that urban areas had the highest temperature and less humidity at the time of imaging. For this purpose, using the LANDSAT 8 images, the indices were extracted and validated with soil moisture data. In this research, the study area was described and then, using the extracted indices, the estimated model was obtained. The results showed that there is a good correlation between surface soil moisture content with LST and NDVI indices (95%). The results of the verification of the soil moisture estimation model also showed that this model with a mean error of less than 0.001 can predict the surface moisture content, this small amount of error indicates the precision of the proposed model for estimating surface moisture.
AN INTEGRATED NETWORK-CONSTRAINED SPATIAL ANALYSIS FOR CAR ACCIDENTS: A CASE STUDY OF TEHRAN CITY, IRAN
Authors: EslamiNezhad S. A. , Delavar M. R.
Published: October 2019

Abstract. Research on determination of spatial patterns in urban car accidents plays an important role in improving urban traffic safety. While traditional methods of spatial clustering of car accidents mostly rely on the two dimensional assumption, many spatial events defy this assumption. For instance, car accidents are constrained by the road network and rely on the one dimensional assumption of street network. The aim of this study is to detect and statistically prioritize the car accident-prone segments of an urban road network by a network-based point pattern analysis. The first step involves estimating the density of car accidents in the one dimensional space of the road network using the network kernel density estimation (NKDE) method with equal-split continuous and discontinuous kernel functions. In the second step, due to the lack of statistical prioritization of the accident-prone segments with NKDE method, the output of the NKDE method is integrated with network-constrained Getis-Ord Gi* statistics to measure and compare the accident-prone segments based on the statistical parameter of Z-Score. The integration of these two methods can improve identification of accident-prone segments which is effective in the enhancing of urban safety and sustainability. These methods were tested using the data of damage car accidents in Tehran District 3 during 2013–2017. We also performed the Network K-Function to display the significant clustering of damage car accident points in the network space at different scales. The results have demonstrated that the damage car accidents are significantly clustered.
A NEW STRATEGY FOR PHASE UNWRAPPING IN INSAR TIME SERIES OVER AREAS WITH HIGH DEFORMATION RATE: CASE STUDY ON THE SOUTHERN TEHRAN SUBSIDENCE
Authors: Ajourlou P. , Samiei Esfahany S. , Safari A.
Published: October 2019

Abstract. The primary step in all timeseries interferometric synthetic aperture radar (T-InSAR) algorithms is the phase unwrapping step to resolve the inherent cycle ambiguities of interferometric phases. In areas with a high spatio-temporal deformation gradient, phase unwrapping fails due to the aliasing problem, and so it can result in an underestimation of deformation signal. One way to handle this problem is to use the so called Small-Baseline Subset (SBAS) algorithms; in these algorithms, by using only small-baseline interferograms – hence interferograms with small deformation gradients – the chance of unwrapping error gets reduced. However, due to more number of the used interferograms, SBAS method is computationally more expensive and more time-consuming compared to algorithms that exploit Single-Master (SM) stacks. Moreover, the existence of sufficiently small temporal baseline interferograms is not guaranteed in all SAR stacks. In this paper, we propose a new method to take advantage of short temporal baseline interferograms but effectively using SM approach. We treat the phase unwrapping step as a Bayesian estimation problem while the prior information, required by the Bayesian estimator, is extracted from few short coherent interferograms that are unwrapped separately. Results from the proposed approach and a case study over the southwest of Tehran, with a high subsidence rate (reaching to 25 cm/year), demonstrates that utilizing the proposed method overcomes the aliasing problem and produces the results equal to the conventional SBAS results, while the proposed method is computationally much more efficient than SBAS.
MODELING THE IMPACT OF SURFACE CHARACTERISTICS ON THE NEAR SURFACE TEMPERATURE LAPSE RATE
Authors: Firozjaei M. K. , Fathololuomi S. , Alavipanah S. K. , Kiavarz M. , Vaezi A. , Biswas A. , Ghorbani A.
Published: October 2019

Abstract. Modeling of Near-Surface Temperature Lapse Rate (NSTLR) is very important in various environmental applications. The Land Surface Temperature (LST) is influenced by many properties and conditions including surface biophysical and topographic characteristics. Some researches have considered the LST - Digital Elevation Model (DEM) feature space to model NSTLR. However, the influence of detailed surface characteristics is rare. This study investigated the impact of surface characteristics on the LST-DEM feature space for NSTLR modeling. A set of remote sensing data including Landsat 8 images, MODIS products, and surface features including DEM and land use of the Balikhli-Chay on 01/07/2018, 18/08/2018 and 03/09/2018 were collected and used in this study. First, Split Window (SW) algorithm was used to estimate LST, and spectral indices were employed to model surface biophysical characteristics. Owing to the impact of surface biophysical and topographic characteristics on the LST-DEM feature space, the NSTLR was calculated for different classes of surface biophysical characteristics, land use, and solar local incident angle. The modeled NSTLR values based on the LST-DEM feature space on 01/07/2018, 18/08/2018 and 03/09/2018 were 8.5, 1.5 and 2.4 °C Km−1; respectively. The NSTLR in different classes of surface biophysical characteristics, land use type and topographical parameters were variable between 0.5 to 14 °C Km−1. This clearly showed the dependence of NSTLR on topographic and biophysical conditions. This provides a new way of calculating surface characteristic specific NSTLR.
URBAN’S RIVER FLOOD ANALYSING USING SENTINEL-1 DATA CASE STUDY: (GORGANROOD, AQ’QALA)
Authors: Ganji K. , Gharachelou S. , Ahmadi A.
Published: October 2019

Abstract. Flood is one of the greatest disasters in the world, and the cause of a lot of damages to buildings and Agricultural products every year. Gorganrood river crossing the city of Aq’qala and it is always under flood risk. In the spring, due to the high intensity rainfall and melting of the snow, upstream areas bring much water into the Gorganrood river. On 23rd March, 2019 occurred a terrible flood in Aq’qala passing discharge 650 (m^3/s), it would occur every 100 years in this river. This river in normal time is passing discharge approximately 120 (m^3/s). A large of an urban and non-urban area was affected by this flood and mapping and analyzing of this flood have a key role for river and disaster management. Remote sensing is one of the best ways to flood mapping, especially in flood time weather is cloudy, Therefore, Synthetic Aperture Radar (SAR) images had high potentiality for flood analysis. In this study the Sentinel-1 data used for flood studying due to free available and shorter revisit time. After the processing has done, by selecting the VV band the flooded areas detected. After that overlapped the images and combination of RGB bands and the change the value of pixels, at last, we will be able to obtain the flood mapping images for Gorganrood river. In the primary days of the flooding, almost all the northern regions of the city were flooded, and during a week about 96.8 (km^2) city flooded.
APPLICATION OF MACHINE AND DEEP LEARNING STRATEGIES FOR THE CLASSIFICATION OF HERITAGE POINT CLOUDS
Authors: Grilli E. , Özdemir E. , Remondino F.
Published: October 2019

Abstract. The use of heritage point cloud for documentation and dissemination purposes is nowadays increasing. The association of semantic information to 3D data by means of automated classification methods can help to characterize, describe and better interpret the object under study. In the last decades, machine learning methods have brought significant progress to classification procedures. However, the topic of cultural heritage has not been fully explored yet. This paper presents a research for the classification of heritage point clouds using different supervised learning approaches (Machine and Deep learning ones). The classification is aimed at automatically recognizing architectural components such as columns, facades or windows in large datasets. For each case study and employed classification method, different accuracy metrics are calculated and compared.
SEISMO-MAGNETIC FIELD ANOMALIES DETECTION USING SWARM SATELLITES (ALPHA, BRAVO AND CHARLIE)
Authors: Akhoondzadeh M.
Published: October 2019

Abstract. Among the lithospheric, atmospheric and ionospheric earthquake precursors, it seems that the ionospheric anomalies showing a meaningful association with seismic activities during absence of solar and geomagnetic activities. Unfortunately there are a limited number of satellite sensors to survey the ionosphere and study on seismo-ionospheric anomalies. This paper represents the data analysis results of Swarm satellites including Alpha, Bravo and Charlie data around the Mexico (September 8, 2017) earthquake. The orbital analysis and time series of magnetic field parameters (magnetic scalar and vectors (X, Y, Z) components) inside the Dobrovolsky’s area show anomalous variations close to the time and locations of the Mexico earthquake. There is a concavity or convexity variations in the some of the time-series to the centre of the earthquake day. In other words, from about 90 days before the event a decreasing or increasing trend in variations of parameters is observed and exactly after the earthquake day its trend changes. It should be noted that the variations of the solar and geomagnetic indices must indicate a normal behaviour during the observed seismo-ionospheric anomalies. Therefore this study indicates that the Swarm satellites measurements play an undeniable role in progress the studies of the ionospheric precursors.
C-ARM POSE ESTIMATION AND NAVIGATION IN SURGERIES FOR AUGMENTED REALITY APPLICATION
Authors: Hosseinian S. , Arefi H. , Navab N.
Published: October 2019

Abstract. C-arm X-ray imaging systems are widely applied in surgeries. Overlaying X-ray with optical images during the surgery has been shown to be an efficient approach. Moreover, overlaying needed data from different modalities in an augmented reality (AR) manner can improve the accuracy of surgical procedures, decrease the variability of surgical outcomes, reduce trauma to the critical structures, increase the reproducibility of surgeons’ performance, and reduce radiation exposure. C-Arm geometric calibration and recovering the C-arm pose are essential for surgical navigation and AR applications in operating rooms. Therefore, in this paper, existing researches for calibration and pose estimation of C-arm devices in surgical AR applications are evaluated from photogrammetric point of view. Then, a proposed marker-based method for C-arm pose estimation is introduced. For this purpose, a marker is designed to facilitate tracking and pose estimation in mixed reality based on golden section principle, and perspective invariants such as cross-ratios, collinearity, and intersection. Moreover, a procedure is also proposed for fast determination of these fiducial markers. The experiments show benefits of such a structure which has a limited occlusion with consistency to different conditions such as narrow field of view and at the same time, even in images with high projectivity. It also results that the distortion correction step is important and the effect of distortion of X-ray images can cause inconsistency in the perspective invariants.
HYPERSPECTRAL IMAGE CLASSIFICATION BY EXPLOITING CONVOLUTIONAL NEURAL NETWORKS
Authors: Hosseiny B. , Rastiveis H. , Daneshtalab S.
Published: October 2019

Abstract. High spectral dimensionality of hyperspectral images makes them useful data resources for earth observation in many remote sensing applications. In this case, the convolutional neural network (CNN) can help to extract deep and robust features from hyperspectral images. The main goal of this paper is to use deep learning concept to extract deep features from hyperspectral datasets to achieve better classification results. In this study, after pre-processing step, data is fed to a CNN in order to extract deep features. Extracted features are then imported in a multi-layer perceptron (MLP) network as our selected classifier. Obtained classification accuracies, based on training sample size, vary from 94.3 to 97.17% and 92.35 to 98.14% for Salinas and Pavia datasets, respectively. These results expressed more than 10% improvements compared to the classic MLP classification technique.
AUTOMATIC BUILDING EXTRACTION FROM LIDAR POINT CLOUD DATA IN THE FUSION OF ORTHOIMAGE
Authors: Hujebri B. , Ebrahimikia M. , Enayati H.
Published: October 2019

Abstract. Three-dimensional building models are important in various applications such as disaster management and urban planning. In this paper, a method based on the fusion of LiDAR point cloud and aerial image data sources has been proposed. The first step of the proposed method is to separate ground and non-ground (that contain 3d objects like buildings, trees, …) points using cloth simulation filtering and then normalize the non-ground points. This research experiment applied a 0.1 threshold for the z component of the normal vector to remove wall points, and 2-meter height threshold to remove off-terrain objects lower than the minimum building height. It is possible to discriminate vegetation and building based on spectral information from orthoimage. After elimination of vegetation points, the mean shift algorithm applied on remaining points to detect buildings. This method provides good performance in dense urban areas with complex ground covering such as trees, shrubs, short walls, and vehicles.
FIRE MODELLING TO ASSESS SPATIAL PATTERNS OF WILDFIRE EXPOSURE IN ARDABIL, NW IRAN
Authors: Jahdi R. , Salis M. , Arabi M. , Arca B.
Published: October 2019

Abstract. Fire exposure describes the spatial juxtaposition of values with fire behaviour in terms of likelihood and intensity. Wildfire exposure analysis is based on the estimation of the potential wildfire intensity and on the burn probability. Fire modelling can produce spatially explicit information on fire spread and behaviour, and offers a feasible method to simulate, map, and analyse fire exposure. FlamMap Minimum Travel Time (MTT) algorithm (Finney, 2006) was used to conduct wildfire simulations considering historical data of fuel moisture conditions and winds, as well as the most frequent wind directions and historical ignition locations (2005-2018). Analysis was conducted on spatial and quantitative variations in selected fire hazard and exposure factors, namely Burn Probability (BP), Conditional Flame Length (CFL) and Fire Size (F). We observed pronounced spatial variations among and between municipalities in the factors, especially for those in the northern and southern parts of Ardabil. The variations across the burnable area of the municipalities can be fundamentally related to a number of factors, including spatial variation in ignition locations, fuel moisture and load, weather conditions, and topography of the terrain. The findings can provide information and support in wildfire management planning and fire risk mitigation activities.
APPLICATION OF WORLDVIEW-3 DATA IN ALTERATION MINERAL MAPPING IN CHADORMALU AREA, CENTRAL IRAN
Authors: Karimzadeh Z. , Tangestani M. H.
Published: October 2019

Abstract. WorldView-3 (WV-3), launched in August 2014, is a commercial imaging satellite with high spatial resolution that measures reflected radiation in VNIR-SWIR regions in 16 bands. The main objective of this study was to discriminate the alteration minerals in Chadormalu iron deposit area, central Iran, using band ratio techniques on the selected bands of this satellite. To identify the appropriate band ratios the spectral bands of WV-3 were investigated and assessed. Regarding to Fe3+, Fe2+, Al-OH, Fe-OH, Mg-OH and CO32− spectral absorption features, band ratios B5/B2, B13/B9, B13/B14 and B14/B16 were proposed for enhancement of ferrous and ferric iron bearing minerals, sericite, epidote, chlorite, and dolomite. Combination of field observations, geological map and high resolution spectra of rock samples were used to verify the results. Band ratio B5/B2, B13/B9, B13/B14 gray scale images respectively illustrated that Fe3+-rich minerals are occurring in granite, the Fe2+-rich minerals are enhanced in iron ore, green schist, and diorite units, and sericite is occurring in hydrothermally altered granite and iron ore. Since the Fe/Mg-OH vibrational mode in chlorite-epidote and spectral absorption feature of CO32− in dolomite occur in B16, the B14/B16 ratio highlighted chlorite-epidote-rich green schist, diorite and dolomitic marble. The false color composite image of B13/B14, B13/B9, and B14/B16 as RGB produced a large scale map which successfully discriminated the rock types and accompanying alteration minerals. This study demonstrated that WV-3 data have efficient spectral resolution to discriminate alteration minerals with Fe3+, Fe2+, Al-OH and Fe/Mg-OH or CO32− spectral absorption features.
HARMFUL ALGAL BLOOMS MONITORING USING SENTINEL-2 SATELLITE IMAGES
Authors: Khalili M. H. , Hasanlou M.
Published: October 2019

Abstract. Over the last few decades in coastal areas, the occurrence of Harmful Algal Blooms (HAB) has increased. The phenomenon is harmful to the health of coastal residents as well as marine organisms and can cause damage to the economy of the region. In this article, considering the need of a method for detecting red tide phenomenon using high spatial resolution satellite images, we tried to test the capability of spectral features, which can be generated using Sentinel-2 satellite images, in detecting red tide phenomenon. For this purpose, we generated an algorithm for detecting spectral features, which the red tide phenomenon causes a noticeable change in their value compared with the non-blooming condition. The ability of the selected spectral features in detecting HABs has been evaluated using statistical methods such as type I and II error, overall accuracy, kappa coefficient, and ROC curves. The best case, for the spectral feature, is (R4−R8A)/(R4+R8A), 5% for type I and 6% for type II error were achieved where R4 stands for reflectance in band 4 and R8A is the reflectance in band 8A of a Sentinel-2 satellite image.
DROUGHT ASSESSMENT OF IRAN USING THE MDI INDEX
Authors: Kordpour I. , Farzaneh S. , Shahhoseini R.
Published: October 2019

Abstract. Drought is one of the most common natural phenomena. Many indices using multiple data types have been created, and their success at recognizing periods of extreme wetness and dryness has been well documented. The merit of the method is the utilization of terrestrial water storage (TWS) variations from Gravity Recovery and Climate Experiment (GRACE) quantification of drought intensity. Alongside with these observations, we add precipitation data to equations. In this study, we analyze Merged-dataset Drought index (MDI) using GRACE-derived TWSA and precipitation in Iran, where most of the area is desert and mountain in the middle and South of the country. Our sample period is from January 2003 to December 2014. MDI shows a strong correlation with existing drought indices, especially with the Palmer Drought Severity Index (PDSI). Based on the obtained results, MDI indicates a moderate Drought event in 2008 and 2012–2015, which is compatible with the recorded result of PDSI. The longest drought took 22 months (from January 2008 to October 2009). Interestingly, the coefficient of correlation between MDI and PDSI is 0.67.
IMPACT OF GAP FILLING ON QUALITY OF ROAD NETWORKS
Authors: Maboudi M. , Amini J. , Gerke M.
Published: October 2019

Abstract. High quality and updated road network maps provide important information for many domains. Many small segments appear on the road surface in VHR images. Most road extraction systems have problem in extraction of these small segments and usually they appear as gaps in the final extracted road networks. However, most approaches skip filling these gaps. This is on account of the fact that usually overall length of the missing parts of the road extraction results is very short relative to the total length of the whole road network. This leads to an indiscernible impact of filling these gaps on geometrical quality criteria. In this paper, using two different VHR satellite datasets and a gap-filling approach which is based on tensor voting, we show that utilizing an effective road gap filling can result in a quite tangible topological improvement in the final road network which is highly demanded in many applications.
DEVELOPING A CELLULAR AUTOMATA MODEL FOR SIMULATING RAINFALL-RUNOFF PROCESS (CASE STUDY: BABOL CATCHMENT)
Authors: Mahdizadeh Gharakhanlou N. , Mesgari M. S.
Published: October 2019

Abstract. The rainfall-runoff process is one of the central aspects of hydrology. In the rainfall-runoff process, the fast accumulation of rainwater in the catchments and rivers may lead to a flood disaster. To prevent and manage such disasters, it is essential to model and simulate the runoff process. The cellular automata (CA) is a computational model that can represent and simulate complex and dynamic natural phenomena. The main objective of this research is to develop a CA model of the rainfall-runoff process and to use it for simulating and representation of the runoff in the Babol catchment. The study is carried out in two main steps. First, several data manipulation and analyses in ArcGIS 10.3 software are used to prepare the input data of the CA model. In the second step, the CA model is developed and implemented in the NetLogo 6.0.4 platform. The total amount of runoff in each cell during the year 2016 was calculated and presented as the output of the model. A map of the total generated runoff can be an important tool for flood management decision-makers and it can also be used for managing the catchment and assessing the water resources in the study area.
DENSE MULTI-VIEW IMAGE MATCHING FOR DSM GENERATION FROM SATELLITE IMAGES
Authors: Mahphood A. , Arefi H. , Hosseininaveh A. , Naeini A. A.
Published: October 2019

Abstract. Digital Surface Model (DSM) can be generated from stereo pairs of satellite or aerial images. Among the most state-of-the-art matching algorithms, Semi-Global Matching (SGM) has widely been used for generating DSM from both satellite and aerial images. This paper presents an approach to improve the accuracy of DSM generated by SGM from multi-view satellite images using a novel technique including several filters. The filters are used for deleting mismatches between very tall buildings in urban areas and removing the sea regions. The technique, in contrast to the recent multi-view matching approaches, considers some of the points generated with only a pair of images in the final DSM. The approach is implemented on five sequential high resolution images acquired by the Worldview-2 satellite. The results are locally evaluated in shape and quantitative terms in comparison with commercial software to reveal the capability of the approach to generate a reliable and dense point cloud. Experiments show that the proposed method can achieve below half-pixel accuracy.