Search results for: non-contact sensing

Authors: Alberto Azevedo, Ricardo Martins, André B. Fortunato, Anabela Oliveira

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Water is under severe threat today because of the rising population, increased agricultural and industrial needs, and the intensifying effects of climate change. Due to sea-level rise, erosion, and demographic pressure, the coastal regions are of significant concern to the scientific community. The Water Monitoring Sentinel Cloud platform (WORSICA) service is focused on providing new tools for monitoring water in coastal and inland areas, taking advantage of remote sensing, in situ and tidal modeling data. WORSICA is a service that can be used to determine the coastline, coastal inundation areas, and the limits of inland water bodies using remote sensing (satellite and Unmanned Aerial Vehicles - UAVs) and in situ data (from field surveys). It applies to various purposes, from determining flooded areas (from rainfall, storms, hurricanes, or tsunamis) to detecting large water leaks in major water distribution networks. This service was built on components developed in national and European projects, integrated to provide a one-stop-shop service for remote sensing information, integrating data from the Copernicus satellite and drone/unmanned aerial vehicles, validated by existing online in-situ data. Since WORSICA is operational using the European Open Science Cloud (EOSC) computational infrastructures, the service can be accessed via a web browser and is freely available to all European public research groups without additional costs. In addition, the private sector will be able to use the service, but some usage costs may be applied, depending on the type of computational resources needed by each application/user. Although the service has three main sub-services i) coastline detection; ii) inland water detection; iii) water leak detection in irrigation networks, in the present study, an application of the service to Óbidos lagoon in Portugal is shown, where the user can monitor the evolution of the lagoon inlet and estimate the topography of the intertidal areas without any additional costs. The service has several distinct methodologies implemented based on the computations of the water indexes (e.g., NDWI, MNDWI, AWEI, and AWEIsh) retrieved from the satellite image processing. In conjunction with the tidal data obtained from the FES model, the system can estimate a coastline with the corresponding level or even topography of the inter-tidal areas based on the Flood2Topo methodology. The outcomes of the WORSICA service can be helpful for several intervention areas such as i) emergency by providing fast access to inundated areas to support emergency rescue operations; ii) support of management decisions on hydraulic infrastructures operation to minimize damage downstream; iii) climate change mitigation by minimizing water losses and reduce water mains operation costs; iv) early detection of water leakages in difficult-to-access water irrigation networks, promoting their fast repair.

Keywords: remote sensing, coastline detection, water detection, satellite data, sentinel, Copernicus, EOSC

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Authors: Jigg L. Pelayo, Ricardo G. Villar, Einstine M. Opiso

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The advances of aerial laser scanning in the Philippines has open-up entire fields of research in remote sensing and machine vision aspire to provide accurate timely information for the government and the public. Rapid mapping of polygonal roads and roof boundaries is one of its utilization offering application to disaster risk reduction, mitigation and development. The study uses low density LiDAR data and high resolution aerial imagery through object-oriented approach considering the theoretical concept of data analysis subjected to machine learning algorithm in minimizing the constraints of feature extraction. Since separating one class from another in distinct regions of a multi-dimensional feature-space, non-trivial computing for fitting distribution were implemented to formulate the learned ideal hyperplane. Generating customized hybrid feature which were then used in improving the classifier findings. Supplemental algorithms for filtering and reshaping object features are develop in the rule set for enhancing the final product. Several advantages in terms of simplicity, applicability, and process transferability is noticeable in the methodology. The algorithm was tested in the different random locations of Misamis Oriental province in the Philippines demonstrating robust performance in the overall accuracy with greater than 89% and potential to semi-automation. The extracted results will become a vital requirement for decision makers, urban planners and even the commercial sector in various assessment processes.

Keywords: feature extraction, machine learning, OBIA, remote sensing

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Authors: Morné Roman, Robert van Zyl, Nishanth Parus, Nishal Mahatho

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Leakage current monitoring for high voltage transmission line insulators is of interest as a performance indicator. Presently, to the best of our knowledge, there is no commercially available, clamp-on type, non-intrusive device for measuring leakage current on energised high voltage direct current (HVDC) transmission line insulators. The South African power utility, Eskom, is investigating the development of such a hand-held sensor for two important applications; first, for continuous real-time condition monitoring of HVDC line insulators and, second, for use by live line workers to determine if it is safe to work on energised insulators. In this paper, a DC leakage current sensor based on magnetic field sensing techniques is developed. The magnetic field sensor used in the prototype can also detect alternating current up to 5 MHz. The DC leakage current prototype detects the magnetic field associated with the current flowing on the surface of the insulator. Preliminary HVDC leakage current measurements are performed on glass insulators. The results show that the prototype can accurately measure leakage current in the specified current range of 1-200 mA. The influence of external fields from the HVDC line itself on the leakage current measurements is mitigated through a differential magnetometer sensing technique. Thus, the developed sensor can perform measurements on in-service HVDC insulators. The research contributes to the body of knowledge by providing a sensor to measure leakage current on energised HVDC insulators non-intrusively. This sensor can also be used by live line workers to inform them whether or not it is safe to perform maintenance on energized insulators.

Keywords: direct current, insulator, leakage current, live line, magnetic field, sensor, transmission lines

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Authors: Tahir Ali Akbar, Hirra Jabbar

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Land cover change analysis plays a significant role in understanding the trends of urban sprawl and land use transformation due to anthropogenic activities. In this study, the spatio-temporal dynamics of major land covers were analyzed in the last twenty years (1988-2016) for District Lahore located in the Punjab Province of Pakistan. The Landsat satellite imageries were downloaded from USGS Global Visualization Viewer of Earth Resources Observation and Science Center located in Sioux Falls, South Dakota USA. The imageries included: (i) Landsat TM-5 for 1988 and 2001; and (ii) Landsat-8 OLI for 2016. The raw digital numbers of Landsat-5 images were converted into spectral radiance and then planetary reflectance. The digital numbers of Landsat-8 image were directly converted into planetary reflectance. The normalized difference vegetation index (NDVI) was used to classify the processed images into six major classes of water, buit-up, barren land, shrub and grassland, sparse vegetation and dense vegetation. The NDVI output results were improved by visual interpretation using high-resolution satellite imageries. The results indicated that the built-up areas were increased to 21% in 2016 from 10% in 1988. The decrease in % areas was found in case of water, barren land and shrub & grassland. There were improvements in percentage of areas for the vegetation. The increasing trend of urban sprawl for Lahore requires implementation of GIS based spatial planning, monitoring and management system for its sustainable development.

Keywords: land cover changes, NDVI, remote sensing, urban sprawl

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Authors: A. K. Srivastava, Sandeep K. Singh, A. K. Kulshetra

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Agriculture is the backbone of developing countries of Asian sub-continent like India. Uttar Pradesh is the most populous and fifth largest State of India. Total population of the state is 19.95 crore, which is 16.49% of the country that is more than that of many other countries of the world. Uttar Pradesh occupies only 7.36% of the total area of India. It is a well-established fact that agriculture has virtually been the lifeline of the State’s economy in the past for long and its predominance is likely to continue for a fairly long time in future. The total geographical area of the state is 242.01 lakh hectares, out of which 120.44 lakh hectares is facing various land degradation problems. This needs to be put under various conservation and reclamation measures at much faster pace in order to enhance agriculture productivity in the State. Keeping in view the above scenario Department of Agriculture, Government of Uttar Pradesh has formulated a multi-purpose project namely Bhoomi Sena for the entire state. The main objective of the project is to improve the land degradation using low cost technology available at village level. The total outlay of the project is Rs. 39643.75 Lakhs for an area of about 226000 ha included in the 12th Five Year Plan (2012-13 to 2016-17). It is expected that the total man days would be 310.60 lakh. An attempt has been made to use the space technology like remote sensing, geographical information system, at cadastral level for the overall management of agriculture engineering work which is required for the treatment of degradation of the land. After integration of thematic maps a proposed action plan map has been prepared for the future work.

Keywords: GPS, GIS, remote sensing, topographic survey, cadestral mapping

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Authors: Olalekan Sunday Alabi, Titus Adeyemi Alonge, Olumuyiwa Idowu Ojo

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Agricultural land suitability analysis and mapping play an imperative role for sustainable utilization of scarce physical land resources. The objective of this study was to prepare spatial database of physical land resources for irrigated agriculture and to assess land suitability for irrigation and developing suitable area map of the study area. The study was conducted at Kampe-Omi irrigation scheme located at Yagba West Local Government Area of Kogi State, Nigeria. Temperature and rainfall data of the study area were collected for 10 consecutive years (2005-2014). Geographic Information System (GIS) techniques were used to develop irrigation land suitability map of the study area. Attribute parameters such as the slope, soil properties, topography of the study area were used for the analysis. The available data were arranged, proximity analysis of Arc-GIS was made, and this resulted into five mapping units. The final agricultural land suitability map of the study area was derived after overlay analysis. Based on soil composition, slope, soil properties and topography, it was concluded that; Kampe-Omi has rich sandy loam soil, which is viable for agricultural purpose, the soil composition is made up of 60% sand and 40% loam. The land-use pattern map of Kampe-Omi has vegetal area and water-bodies covering 55.6% and 19.3% of the total assessed area respectively. The landform of Kampe-Omi is made up of 41.2% lowlands, 37.5% normal lands and 21.3% highlands. Kampe-Omi is adequately suitable for agricultural purpose while an extra of 20.2% of the area is highly suitable for agricultural purpose making 72.6% while 18.7% of the area is slightly suitable.

Keywords: remote sensing, GIS, Kampe–Omi, land suitability, mapping

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Authors: Alisa Salimova, Jiane Zuo, Christopher Homer

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The objective of this study is to analyse the North Canal riparian zone land use with the help of remote sensing analysis in ArcGis using 30 cloudless Landsat8 open-source satellite images from May to August of 2013 and 2017. Land cover, urban construction, heat island effect, vegetation cover, and water system change were chosen as the main parameters and further analysed to evaluate its impact on the North Canal water quality. The methodology involved the following steps: firstly, 30 cloudless satellite images were collected from the Landsat TM image open-source database. The visual interpretation method was used to determine different land types in a catchment area. After primary and secondary classification, 28 land cover types in total were classified. Visual interpretation method was used with the help ArcGIS for the grassland monitoring, US Landsat TM remote sensing image processing with a resolution of 30 meters was used to analyse the vegetation cover. The water system was analysed using the visual interpretation method on the GIS software platform to decode the target area, water use and coverage. Monthly measurements of water temperature, pH, BOD, COD, ammonia nitrogen, total nitrogen and total phosphorus in 2013 and 2017 were taken from three locations of the North Canal in Tongzhou district. These parameters were used for water quality index calculation and compared to land-use changes. The results of this research were promising. The vegetation coverage of North Canal riparian zone in 2017 was higher than the vegetation coverage in 2013. The surface brightness temperature value was positively correlated with the vegetation coverage density and the distance from the surface of the water bodies. This indicates that the vegetation coverage and water system have a great effect on temperature regulation and urban heat island effect. Surface temperature in 2017 was higher than in 2013, indicating a global warming effect. The water volume in the river area has been partially reduced, indicating the potential water scarcity risk in North Canal watershed. Between 2013 and 2017, urban residential, industrial and mining storage land areas significantly increased compared to other land use types; however, water quality has significantly improved in 2017 compared to 2013. This observation indicates that the Tongzhou Water Restoration Plan showed positive results and water management of Tongzhou district had been improved.

Keywords: North Canal, land use, riparian vegetation, river ecology, remote sensing

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Authors: D. Bhowmik, Y. Tian, Q. Yin, F. Zhu

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Cyclic GMP-AMP synthase (cGAS), recognises cytoplasmic double-stranded DNA (dsDNA), indicative of bacterial and viral infections, as well as the leakage of self DNA by cellular dysfunction and stresses, to elicit the host's immune responses. Viruses also have developed numerous strategies to antagonize the cGAS-STING pathway. Kaposi's sarcoma-associated herpesvirus (KSHV) is a human DNA tumor virus that is the causative agent of Kaposi’s sarcoma and several other malignancies. To persist in the host, consequently causing diseases, KSHV must overcome the host innate immune responses, including the cGAS-STING DNA sensing pathway. We already found that ORF52 or KicGAS (KSHV inhibitor of cGAS), an abundant and basic gamma herpesvirus-conserved tegument protein, directly inhibits cGAS enzymatic activity. To better understand the mechanism, we have performed the biochemical and structural characterization of full-length KicGAS and various mutants in regarding binding to DNA. We observed that KicGAS is capable of self-association and identified the critical residues involved in the oligomerization process. We also characterized the DNA-binding of KicGAS and found that KicGAS cooperatively oligomerizes along the length of the double stranded DNA, the highly conserved basic residues at the c-terminal disordered region are crucial for DNA recognition. Deficiency in oligomerization also affects DNA binding. Thus DNA binding by KicGAS sequesters DNA and prevents it from being detected by cGAS, consequently inhibiting cGAS activation. KicGAS homologues also inhibit cGAS efficiently, suggesting inhibition of cGAS is evolutionarily conserved mechanism among gamma herpesvirus. These results highlight the important viral strategy to evade this innate immune sensor.

Keywords: Kaposi's sarcoma-associated herpesvirus, KSHV, cGAS, DNA binding, inhibition

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Authors: N. Demertzis, J. L. Bowen

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Sepsis is a complex and rapidly evolving condition, resulting from uncontrolled prolonged activation of host immune system due to pathogenic insult. The aim of this study is the development of a multiplex electrochemical sensing platform, capable of detecting both pathogen associated and host immune markers to enable the rapid and definitive diagnosis of sepsis. A combination of aptamers and molecular imprinting approaches have been employed to generate sensing systems for lipopolysaccharide (LPS), c-reactive protein (CRP) and procalcitonin (PCT). Gold working electrodes were mechanically polished and electrochemically cleaned with 0.1 M sulphuric acid using cyclic voltammetry (CV). Following activation, a self-assembled monolayer (SAM) was generated, by incubating the electrodes with a thiolated anti-LPS aptamer / dithiodibutiric acid (DTBA) mixture (1:20). 3-aminophenylboronic acid (3-APBA) in combination with the anti-LPS aptamer was used for the development of the hybrid molecularly imprinted sensor (apta-MIP). Aptasensors, targeting PCT and CRP were also fabricated, following the same approach as in the case of LPS, with mercaptohexanol (MCH) replacing DTBA. In the case of the CRP aptasensor, the SAM was formed following incubation of a 1:1 aptamer: MCH mixture. However, in the case of PCT, the SAM was formed with the aptamer itself, with subsequent backfilling with 1 μM MCH. The binding performance of all systems has been evaluated using electrochemical impedance spectroscopy. The apta-MIP’s polymer thickness is controlled by varying the number of electropolymerisation cycles. In the ideal number of polymerisation cycles, the polymer must cover the electrode surface and create a binding pocket around LPS and its aptamer binding site. Less polymerisation cycles will create a hybrid system which resembles an aptasensor, while more cycles will be able to cover the complex and demonstrate a bulk polymer-like behaviour. Both aptasensor and apta-MIP were challenged with LPS and compared to conventional imprinted (absence of aptamer from the binding site, polymer formed in presence of LPS) and non-imprinted polymers (NIPS, absence of LPS whilst hybrid polymer is formed). A stable LPS aptasensor, capable of detecting down to 5 pg/ml of LPS was generated. The apparent Kd of the system was estimated at 17 pM, with a Bmax of approximately 50 pM. The aptasensor demonstrated high specificity to LPS. The apta-MIP demonstrated superior recognition properties with a limit of detection of 1 fg/ml and a Bmax of 100 pg/ml. The CRP and PCT aptasensors were both able to detect down to 5 pg/ml. Whilst full binding performance is currently being evaluated, there is none of the sensors demonstrate cross-reactivity towards LPS, CRP or PCT. In conclusion, stable aptasensors capable of detecting LPS, PCT and CRP at low concentrations have been generated. The realisation of a multiplex panel such as described herein, will effectively contribute to the rapid, personalised diagnosis of sepsis.

Keywords: aptamer, electrochemical impedance spectroscopy, molecularly imprinted polymers, sepsis

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Authors: Salihah Alghamdi, Surajit Ray

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Space-time data can be observed over irregularly shaped manifolds, which might have complex boundaries or interior gaps. Most of the existing methods do not consider the shape of the data, and as a result, it is difficult to model irregularly shaped data accommodating the complex domain. We used a method that can deal with space-time data that are distributed over non-planner shaped regions. The method is based on partial differential equations and finite element analysis. The model can be estimated using a penalized least squares approach with a regularization term that controls the over-fitting. The model is regularized using two roughness penalties, which consider the spatial and temporal regularities separately. The integrated square of the second derivative of the basis function is used as temporal penalty. While the spatial penalty consists of the integrated square of Laplace operator, which is integrated exclusively over the domain of interest that is determined using finite element technique. In this paper, we applied a spatio-temporal regression model with partial differential equations regularization (ST-PDE) approach to analyze a remote sensing data measuring the greenness of vegetation, measure by an index called enhanced vegetation index (EVI). The EVI data consist of measurements that take values between -1 and 1 reflecting the level of greenness of some region over a period of time. We applied (ST-PDE) approach to irregular shaped region of the EVI data. The approach efficiently accommodates the irregular shaped regions taking into account the complex boundaries rather than smoothing across the boundaries. Furthermore, the approach succeeds in capturing the temporal variation in the data.

Keywords: irregularly shaped domain, partial differential equations, finite element analysis, complex boundray

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Authors: Levent Basayigit, Sinan Demir, Burhan Kara, Yusuf Ucar

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Cannabis is mostly used for drug production. In some countries, an excessive amount of illegal cannabis is cultivated and sold. Most of the illegal cannabis cultivation occurs on the lands far from settlements. In farmlands, it is cultivated with other crops. In this method, cannabis is surrounded by tall plants like corn and sunflower. It is also cultivated with tall crops as the mixed culture. The common method of the determination of the illegal cultivation areas is to investigate the information obtained from people. This method is not sufficient for the determination of illegal cultivation in remote areas. For this reason, more effective methods are needed for the determination of illegal cultivation. Remote Sensing is one of the most important technologies to monitor the plant growth on the land. The aim of this study is to monitor cannabis cultivation area using satellite imagery. The main purpose of this study was to develop an applicable method for monitoring the cannabis cultivation. For this purpose, cannabis was grown as single or surrounded by the corn and sunflower in plots. The morphological characteristics of cannabis were recorded two times per month during the vegetation period. The spectral signature library was created with the spectroradiometer. The parcels were monitored with high-resolution satellite imagery. With the processing of satellite imagery, the cultivation areas of cannabis were classified. To separate the Cannabis plots from the other plants, the multiresolution segmentation algorithm was found to be the most successful for classification. WorldView Improved Vegetative Index (WV-VI) classification was the most accurate method for monitoring the plant density. As a result, an object-based classification method and vegetation indices were sufficient for monitoring the cannabis cultivation in multi-temporal Earthwiev images.

Keywords: Cannabis, drug, remote sensing, object-based classification

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Authors: Ivo Zution Goncalves, Christopher M. U. Neale, Hiran Medeiros, Everardo Mantovani, Natalia Souza

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Multispectral and thermal infrared imagery from satellite sensors coupled with climate and soil datasets were used to estimate evapotranspiration and biomass in center pivots planted to maize in Brazil during the 2016 season. The hybrid remote sensing based model named Spatial EvapoTranspiration Modelling Interface (SETMI) was applied using multispectral and thermal infrared imagery from the Landsat Thematic Mapper instrument. Field data collected by the IRRIGER center pivot management company included daily weather information such as maximum and minimum temperature, precipitation, relative humidity for estimating reference evapotranspiration. In addition, soil water content data were obtained every 0.20 m in the soil profile down to 0.60 m depth throughout the season. Early season soil samples were used to obtain water-holding capacity, wilting point, saturated hydraulic conductivity, initial volumetric soil water content, layer thickness, and saturated volumetric water content. Crop canopy development parameters and irrigation application depths were also inputs of the model. The modeling approach is based on the reflectance-based crop coefficient approach contained within the SETMI hybrid ET model using relationships developed in Nebraska. The model was applied to several fields located in Minas Gerais State in Brazil with approximate latitude: -16.630434 and longitude: -47.192876. The model provides estimates of real crop evapotranspiration (ET), crop irrigation requirements and all soil water balance outputs, including biomass estimation using multi-temporal satellite image inputs. An interpolation scheme based on the growing degree-day concept was used to model the periods between satellite inputs, filling the gaps between image dates and obtaining daily data. Actual and accumulated ET, accumulated cold temperature and water stress and crop water requirements estimated by the model were compared with data measured at the experimental fields. Results indicate that the SETMI modeling approach using data assimilation, showed reliable daily ET and crop water requirements for maize, interpolated between remote sensing observations, confirming the applicability of the SETMI model using new relationships developed in Nebraska for estimating mainly ET and water requirements in Brazil under tropical conditions.

Keywords: basal crop coefficient, irrigation, remote sensing, SETMI

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Authors: Mahesh Kumar Jat, Manisha Choudhary

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Rapid population and economic growth resulted in changes in large-scale land use land cover (LULC) changes. Changes in the biophysical properties of the Earth's surface and its impact on climate are of primary concern nowadays. Different approaches, ranging from location-based relationships or modelling earth surface - atmospheric interaction through modelling techniques like surface energy balance (SEB) have been used in the recent past to examine the relationship between changes in Earth surface land cover and climatic characteristics like temperature and precipitation. A remote sensing-based model i.e., Surface Energy Balance Algorithm for Land (SEBAL), has been used to estimate the surface heat fluxes over Mahi Bajaj Sagar catchment (India) from 2001 to 2020. Landsat ETM and OLI satellite data are used to model the SEB of the area. Changes in observed precipitation and temperature, obtained from India Meteorological Department (IMD) have been correlated with changes in surface heat fluxes to understand the relative contributions of LULC change in changing these climatic variables. Results indicate a noticeable impact of LULC changes on climatic variables, which are aligned with respective changes in SEB components. Results suggest that precipitation increases at a rate of 20 mm/year. The maximum and minimum temperature decreases and increases at 0.007 ℃ /year and 0.02 ℃ /year, respectively. The average temperature increases at 0.009 ℃ /year. Changes in latent heat flux and sensible heat flux positively correlate with precipitation and temperature, respectively. Variation in surface heat fluxes influences the climate parameters and is an adequate reason for climate change. So, SEB modelling is helpful to understand the LULC change and its impact on climate.

Keywords: remote sensing, GIS, object based, classification

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Authors: Lican Guzmán, José Manuel Lattus, Mariana Cervetto, Mauricio Calderón

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Today the estimation of effects produced by climate change in high Andean wetland environments is confronted by big challenges. This study provides a way to an analysis by remote sensing how some Ambiental aspects have evolved on the Maricunga salt flat in the last 30 years, divided into the summer and winter seasons, and if global warming is conditioning these changes. The first step to achieve this goal was the recompilation of geological, hydrological, and morphometric antecedents to ensure an adequate contextualization of its environmental parameters. After this, software processing and analysis of Landsat 5,7 and 8 satellite imagery was required to get the vegetation, water, surface temperature, and soil moisture indexes (NDVI, NDWI, LST, and SMI) in order to see how their spatial-temporal conditions have evolved in the area of study during recent decades. Results show a tendency of regular increase in surface temperature and disponibility of water during both seasons but with slight drought periods during summer. Soil moisture factor behaves as a constant during the dry season and with a tendency to increase during wintertime. Vegetation analysis shows an areal and quality increase of its surface sustained through time that is consistent with the increase of water supply and temperature in the basin mentioned before. Roughly, the effects of climate change can be described as positive for the Maricunga salt flat; however, the lack of exact correlation in dates of the imagery available to remote sensing analysis could be a factor for misleading in the interpretation of results.

Keywords: global warming, geology, SIG, Atacama Desert, Salar de Maricunga, environmental geology, NDVI, SMI, LST, NDWI, Landsat

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Authors: Hsyi-En Cheng, Ying-Yi Liou

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Metal-oxide semiconductor (MOS) gas sensors are widely used in the gas-detection market due to their high sensitivity, fast response, and simple device structures. However, the high working temperature of MOS gas sensors makes them difficult to integrate with the appliance or consumer goods. One-dimensional (1-D) nanostructures are considered to have the potential to lower their working temperature due to their large surface-to-volume ratio, confined electrical conduction channels, and small feature sizes. Unfortunately, the difficulty of fabricating 1-D nanostructure electrodes has hindered the development of low-temperature MOS gas sensors. In this work, we proposed a method to fabricate nanotube-arrays, and the SnO₂ nanotube-array sensors with different wall thickness were successfully prepared and examined. The fabrication of SnO₂ nanotube arrays incorporates the techniques of barrier-free anodic aluminum oxide (AAO) template and atomic layer deposition (ALD) of SnO₂. First, 1.0 µm Al film was deposited on ITO glass substrate by electron beam evaporation and then anodically oxidized by five wt% phosphoric acid solution at 5°C under a constant voltage of 100 V to form porous aluminum oxide. As the Al film was fully oxidized, a 15 min over anodization and a 30 min post chemical dissolution were used to remove the barrier oxide at the bottom end of pores to generate a barrier-free AAO template. The ALD using reactants of TiCl4 and H₂O was followed to grow a thin layer of SnO₂ on the template to form SnO₂ nanotube arrays. After removing the surface layer of SnO₂ by H₂ plasma and dissolving the template by 5 wt% phosphoric acid solution at 50°C, upright standing SnO₂ nanotube arrays on ITO glass were produced. Finally, Ag top electrode with line width of 5 μm was printed on the nanotube arrays to form SnO₂ nanotube-array sensor. Two SnO₂ nanotube-arrays with wall thickness of 30 and 60 nm were produced in this experiment for the evaluation of gas sensing ability. The flat SnO₂ films with thickness of 30 and 60 nm were also examined for comparison. The results show that the properties of ALD SnO₂ films were related to the deposition temperature. The films grown at 350°C had a low electrical resistivity of 3.6×10-3 Ω-cm and were, therefore, used for the nanotube-array sensors. The carrier concentration and mobility of the SnO₂ films were characterized by Ecopia HMS-3000 Hall-effect measurement system and were 1.1×1020 cm-3 and 16 cm3/V-s, respectively. The electrical resistance of SnO₂ film and nanotube-array sensors in air and in a 5% H₂-95% N₂ mixture gas was monitored by Pico text M3510A 6 1/2 Digits Multimeter. It was found that, at 200 °C, the 30-nm-wall SnO₂ nanotube-array sensor performs the highest responsivity to 5% H₂, followed by the 30-nm SnO₂ film sensor, the 60-nm SnO₂ film sensor, and the 60-nm-wall SnO₂ nanotube-array sensor. However, at temperatures below 100°C, all the samples were insensitive to the 5% H₂ gas. Further investigation on the sensors with thinner SnO₂ is necessary for improving the sensing ability at temperatures below 100 °C.

Keywords: atomic layer deposition, nanotube arrays, gas sensor, tin dioxide

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Authors: M. M. Hefnawy, A. M. A. Homoda, M. A. Abounassif, A. M. Alanazia, A. Al-Majed, Gamal A. E. Mostafa

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PVC membrane sensors using different approach e.g. ion-pair, ionophore, and Schiff-base has been used as testing membrane sensor. Analytical applications of membrane sensors for direct measurement of variety of different ions in complex biological and environmental sample are reported. The most important step of such PVC membrane sensor is the sensing active material. The potentiometric sensors have some outstanding advantages including simple design, operation, wide linear dynamic range, relative fast response time, and rotational selectivity. The analytical applications of these techniques to pharmaceutical compounds in dosage forms are also discussed. The construction and electrochemical response characteristics of Poly (vinyl chloride) membrane sensors for moxifloxacin HCl (MOX) are described. The sensing membranes incorporate ion association complexes of moxifloxacin cation and sodium tetraphenyl borate (NaTPB) (sensor 1), phosphomolybdic acid (PMA) (sensor 2) or phosphotungstic acid (PTA) (sensor 3) as electroactive materials. The sensors display a fast, stable and near-Nernstian response over a relative wide moxifloxacin concentration range (1 ×10-2-4.0×10-6, 1 × 10-2-5.0×10-6, 1 × 10-2-5.0×10-6 M), with detection limits of 3×10-6, 4×10-6 and 4.0×10-6 M for sensor 1, 2 and 3, respectively over a pH range of 6.0-9.0. The sensors show good discrimination of moxifloxacin from several inorganic and organic compounds. The direct determination of 400 µg/ml of moxifloxacin show an average recovery of 98.5, 99.1 and 98.6 % and a mean relative standard deviation of 1.8, 1.6 and 1.8% for sensors 1, 2, and 3 respectively. The proposed sensors have been applied for direct determination of moxifloxacin in some pharmaceutical preparations. The results obtained by determination of moxifloxacin in tablets using the proposed sensors are comparable favorably with those obtained using the US Pharmacopeia method. The sensors have been used as indicator electrodes for potentiometric titration of moxifloxacin.

Keywords: potentiometry, PVC, membrane sensors, ion-pair, ionophore, schiff-base, moxifloxacin HCl, sodium tetraphenyl borate, phosphomolybdic acid, phosphotungstic acid

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Authors: Bhavani Veasuvalingam

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Case-based learning (CBL) is a popular instructional method aimed to bridge theory to clinical practice. This study aims to explore CBL mixed modality curriculum in influencing students’ learning styles and strategies that support learning. An explanatory sequential mixed method study was employed with initial phase, 44-itemed Felderman’s Index of Learning Style (ILS) questionnaire employed across year one medical students (n=142) using convenience sampling to describe the preferred learning styles. The qualitative phase utilised three focus group discussions (FGD) to explore in depth on the multimodal learning style exhibited by the students. Most students preferred combination of learning stylesthat is reflective, sensing, visual and sequential i.e.: RSVISeq style (24.64%) from the ILS analysis. The frequency of learning preference from processing to understanding were well balanced, with sequential-global domain (66.2%); sensing-intuitive (59.86%), active- reflective (57%), and visual-verbal (51.41%). The qualitative data reported three major themes, namely Theme 1: CBL mixed modalities navigates learners’ learning style; Theme 2: Multimodal learners active learning strategies supports learning. Theme 3: CBL modalities facilitating theory into clinical knowledge. Both quantitative and qualitative study strongly reports the multimodal learning style of the year one medical students. Medical students utilise multimodal learning styles to attain the clinical knowledge when learning with CBL mixed modalities. Educators’ awareness of the multimodal learning style is crucial in delivering the CBL mixed modalities effectively, considering strategic pedagogical support students to engage and learn CBL in bridging the theoretical knowledge into clinical practice.

Keywords: case-based learning, learnign style, medical students, learning

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Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

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Authors: Diptiman Dinda, Shyamal Kumar Saha

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In the 21st century, sensitive and selective detection of trace amounts of explosives has become a serious problem. Generally, nitro compound and its derivatives are being used worldwide to prepare different explosives. Recently, TNP (2, 4, 6 trinitrophenol) is the most commonly used constituent to prepare powerful explosives all over the world. It is even powerful than TNT or RDX. As explosives are electron deficient in nature, it is very difficult to detect one separately from a mixture. Again, due to its tremendous water solubility, detection of TNP in presence of other explosives from water is very challenging. Simple instrumentation, cost-effective, fast and high sensitivity make fluorescence based optical sensing a grand success compared to other techniques. Graphene oxide (GO), with large no of epoxy grps, incorporate localized nonradiative electron-hole centres on its surface to give very weak fluorescence. In this work, GO is functionalized with 2, 6-diamino pyridine to remove those epoxy grps. through SN2 reaction. This makes GO into a bright blue luminescent fluorophore (DAP/rGO) which shows an intense PL spectrum at ∼384 nm when excited at 309 nm wavelength. We have also characterized the material by FTIR, XPS, UV, XRD and Raman measurements. Using this as fluorophore, a large fluorescence quenching (96%) is observed after addition of only 200 µL of 1 mM TNP in water solution. Other nitro explosives give very moderate PL quenching compared to TNP. Such high selectivity is related to the operation of FRET mechanism from fluorophore to TNP during this PL quenching experiment. TCSPC measurement also reveals that the lifetime of DAP/rGO drastically decreases from 3.7 to 1.9 ns after addition of TNP. Our material is also quite sensitive to 125 ppb level of TNP. Finally, we believe that this graphene based luminescent material will emerge a new class of sensing materials to detect trace amounts of explosives from aqueous solution.

Keywords: graphene, functionalization, fluorescence quenching, FRET, nitroexplosive detection

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Authors: Zhengyu Yan, Yan Yu, Jianqiu Chen

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A novel sensing system has been designed for naphthalene detection based on the quenched fluorescence signal of CdS quantum dots. The fluorescence intensity of the system reduced significantly after adding CdS quantum dots to the water pollution model because of the fluorescent static quenching f mechanism. Herein, we have demonstrated the facile methodology can offer a convenient and low analysis cost with the recovery rate as 97.43%-103.2%, which has potential application prospect.

Keywords: CdS quantum dots, modification, detection, naphthalene

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Authors: Saeid Gharechelou, Stuart Green, Fiona Cawkwell

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Hedgerow has an important role for a wide range of ecological habitats, landscape, agriculture management, carbon sequestration, wood production. Hedgerow detection accurately using satellite imagery is a challenging problem in remote sensing techniques, because in the special approach it is very similar to line object like a road, from a spectral viewpoint, a hedge is very similar to a forest. Remote sensors with very high spatial resolution (VHR) recently enable the automatic detection of hedges by the acquisition of images with enough spectral and spatial resolution. Indeed, recently VHR remote sensing data provided the opportunity to detect the hedgerow as line feature but still remain difficulties in monitoring the characterization in landscape scale. In this research is used the TerraSAR-x Spotlight and Staring mode with 3-5 m resolution in wet and dry season in the test site of Fermoy County, Ireland to detect the hedgerow by acquisition time of 2014-2015. Both dual polarization of Spotlight data in HH/VV is using for detection of hedgerow. The varied method of SAR image technique with try and error way by integration of classification algorithm like texture analysis, support vector machine, k-means and random forest are using to detect hedgerow and its characterization. We are applying the Shannon entropy (ShE) and backscattering analysis in single and double bounce in polarimetric analysis for processing the object-oriented classification and finally extracting the hedgerow network. The result still is in progress and need to apply the other method as well to find the best method in study area. Finally, this research is under way to ahead to get the best result and here just present the preliminary work that polarimetric image of TSX potentially can detect the hedgerow.

Keywords: TerraSAR-X, hedgerow detection, high resolution SAR image, dual polarization, polarimetric analysis

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Authors: Eyad H. R. Fadda, Jawaher K. Al Rashdieah, Aysha H. Al Rashdieh

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Urban Sprawl is a phenomenon that many regions in the Sultanate of Oman suffer from in general and in Welayat Barka in particular. It is considered a human phenomenon that causes many negative effects as it has increased in the last time clearly, and this study aims to diagnose the current status of urban growth taking place in Walayat Barka. The objective of this study is to monitor and follow up on the most prominent changes and developments taking place in Barka in the period from 2002 to 2019 and provide suggestions to the decision-makers to reduce the negative effects of the phenomenon. The study methodology depends on the descriptive and analytical approach to describe the phenomenon and its analysis and knowledge of the factors that helped in urban expansion in the Barka, using a number of studies and interviews with the specialists, both in governmental and private institutions, as well as with individuals who own land, real estate, and others. Geographic Information Systems (GIS) and Remote Sensing (ERDAS software) have been used to analyze the satellite images that helped in obtaining results that reflect the changes Barka, in addition to knowing the natural and human determinants that stand on Urban Sprawl Expansion. The study concluded that the geographical location of Barka has a significant role in its urban expansion, as it is the closest state to the capital Muscat, as this expansion continues toward the southern and south-western directions, as this expansion has significant negative effects represented in the low number of agricultural lands due to the continuous change in land use. In addition, it was found that there are two types of natural determinants of urban expansion in Barka, which are consumed land from the Sea of Oman and from the western sands.

Keywords: GIS applications, remote sensing, urbanization, urban sprawl expansion trends

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Authors: Koyar Rane

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Nano- to submicron size particles of narrow particle size distribution of semi-conducting TiO₂, ZnO, NiO, CuO, Fe₂O₃ have been synthesized by novel hydrazine method and tested for their gas sensing, photocatalytic and bactericidal activities and the behavior found to be enhanced when the oxides in the thin film forms, that obtained in a specially built spray pyrolysis reactor. Hydrazine method is novel in the sense, say, the UV absorption edge of the white pigment grade wide band gap (~3.2eV) TiO₂ and ZnO shifted to the visible region turning into yellowish particles, indicating modification occurring the band structure. The absorption in the visible region makes these oxides visible light sensitive photocatalysis in degrading pollutants, especially the organic dyes which otherwise increase the chemical oxygen demand of the drinking water, enabling the process feasible not under the harsh energetic UV radiation regime. The electromagnetic radiations on irradiation produce electron-hole pairs Semiconductor + hν → e⁻ + h⁺ The electron-hole pairs thus produced form Reactive Oxygen Species, ROS, on the surface of the semiconductors, O₂(adsorbed)+e⁻ → O₂• - superoxide ion OH-(surface)+h⁺ →•OH - Hydroxyl radical The ROS attack the organic material and micro-organisms. Our antibacterial studies indicate the metal oxides control the Biological Oxygen Demand (BOD) of drinking water which had beyond the safe level normally found in the municipal supply. Metal oxides in the thin film form show overall enhanced properties and the films are reusable. The results of the photodegradation and antibactericidal studies are discussed. Gas sensing studies too have been done to find the versatility of the multifunctional metal oxides.

Keywords: hydrazine method, visible light sensitive, photo-degradation of dyes, water/airborne pollutant

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Authors: Sumaira Zafar, Arjumand Zaidi, Muhammad Arslan Hafeez

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Indus Basin Irrigation System (IBIS) is the largest contiguous irrigation system of the world comprising Indus River and its tributaries, canals, distributaries, and watercourses. A big challenge faced by IBIS is transmission losses through seepage and leaks that account to 41 percent of the total water derived from the river and about 40 percent of that is through watercourses. Irrigation system rehabilitation programs in Pakistan are focused on improvement of canal system at the watercourse level (tertiary channels). Under these irrigation system management programs more than 22,800 watercourses have been improved or lined out of 43,000 (12,900 Kilometers) watercourses. The evaluation of the improvement work is required at this stage to testify the success of the programs. In this paper, emerging technologies of GIS and satellite remote sensing are used for impact assessment of watercourse rehabilitation work in Sindh. To evaluate the efficiency of the improved watercourses, few parameters are selected like soil moisture along watercourses, availability of water at tail end and changes in cultivable command areas. Improved watercourses details and maps are acquired from National Program for Improvement of Watercourses (NPIW) and Space and Upper Atmospheric Research Commission (SUPARCO). High resolution satellite images of Google Earth for the year of 2004 to 2013 are used for digitizing command areas. Temporal maps of cultivable command areas show a noticeable increase in the cultivable land served by improved watercourses. Field visits are conducted to validate the results. Interviews with farmers and landowners also reveal their overall satisfaction in terms of availability of water at the tail end and increased crop production.

Keywords: geospatial, impact assessment, watercourses, GIS, remote sensing, seepage, canal lining

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Authors: Mohamed Aboelghar, Ayman Abou Hadid, Usama Albehairy, Asmaa Khater

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Sustainable agricultural development of the desert areas of Egypt under the pressure of irrigation water scarcity is a significant national challenge. Existing water harvesting techniques on the northwest coast of Egypt do not ensure the optimal use of rainfall for agricultural purposes. Basin-scale hydrology potentialities were studied to investigate how available annual rainfall could be used to increase agricultural production. All data related to agricultural production included in the form of geospatial layers. Thematic classification of Sentinal-2 imagery was carried out to produce the land cover and crop maps following the (FAO) system of land cover classification. Contour lines and spot height points were used to create a digital elevation model (DEM). Then, DEM was used to delineate basins, sub-basins, and water outlet points using the Soil and Water Assessment Tool (Arc SWAT). Main soil units of the study area identified from Land Master Plan maps. Climatic data collected from existing official sources. The amount of precipitation, surface water runoff, potential, and actual evapotranspiration for the years (2004 to 2017) shown as results of (Arc SWAT). The land cover map showed that the two tree crops (olive and fig) cover 195.8 km2 when herbaceous crops (barley and wheat) cover 154 km2. The maximum elevation was 250 meters above sea level when the lowest one was 3 meters below sea level. The study area receives a massive variable amount of precipitation; however, water harvesting methods are inappropriate to store water for purposes.

Keywords: water catchements, remote sensing, GIS, sustainable agricultural development

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Authors: Norihiro Kato, Yuriko Takayama

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Some gram-negative bacteria enable the simultaneous activation of gene expression involved in N-acylhomoserine lactone (AHL) dependent cell-to-cell communication system. Such regulatory system for the bacterial group behavior is termed as quorum sensing (QS) because a diffusible AHL signal can accumulate around the cell during the increase of the cell density and trigger activation of the sequential QS process. By blocking the QS, the expression of diverse genes related to infection, antibiotic production, and biofilm formation is inhibited. Conditioning of QS by regulation of the DNA-receptor-AHL interaction is a potential target for enhancing host defenses against pathogenicity. We focused on engineered application of transcriptional regulator SpnR produced in opportunistic human pathogen Serratia marcescens. The SpnR can interact with AHL signals at an N-terminal domain and also with a promoter region of a QS target gene at a C-terminal domain. As the initial process of the QS activation, the SpnR forms a complex with the AHL to enhance the expression of pig cluster; the SpnR normally acts as an activator for the expression of the QS-dependent gene. In this research, we attempt to artificially control QS by changing the role of SpnR. The QS-dependent prodigiosin production is expected to inhibit by externally added SpnR in the culture broth of AS-1 strain because the AHL concentration was kept below the threshold by AHL-SpnR complex formation. Maltose-binding protein (MBP)-tagged SpnR (MBP-SpnR) was overexpressed in Escherichia coli and purified using an affinity chromatography equipped with an amylose resin column. The specific interaction between AHL and MBP-SpnR was demonstrated by quartz crystal microbalance (QCM) sensor. AHL with amino end-group was coupled with COOH-terminated self-assembled monolayer prepared on a gold electrode of 27-MHz quartz crystal sensor using water-soluble carbodiimide. After the injection of MBP-SpnR into a cup-type sensor cell filled with the buffer solution, time course of resonant frequency change (ΔFs) was determined. A decrease of ΔFs clearly showed the uptake of MBP-SpnR onto the AHL-immobilized electrode. Furthermore, no binding affinity was observed after the heat-inactivation of MBP-SpnR at 80ºC. These results suggest that MBP-SpnR possesses a specific affinity for AHL. MBP-SpnR was added to the culture medium as an AHL trap to study inhibitory effects on intracellularly accumulated prodigiosin. With approximately 2 µM MBP-SpnR, the amount of prodigiosin induced was half that of the control without any additives. In conclusion, the function of SpnR could be switched by adding it to the cell culture. Exogenously added MBP-SpnR possesses high affinity for AHL derived from cells and acts as an inhibitor of AHL-mediated QS.

Keywords: intracellular signaling, microbial biotechnology, quorum sensing, transcriptional regulator

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Authors: M. Agostini, A. Sonato, G. Greco, M. Travagliati, G. Ruffato, E. Gazzola, D. Liuni, F. Romanato, M. Cecchini

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Since their first demonstration in the early 1980s, surface plasmon resonance (SPR) sensors have been widely recognized as useful tools for detecting chemical and biological species, and the interest of the scientific community toward this technology has known a rapid growth in the past two decades owing to their high sensitivity, label-free operation and possibility of real-time detection. Recent works have suggested that a turning point in SPR sensor research would be the combination of SPR strategies with other technologies in order to reduce human handling of samples, improve integration and plasmonic sensitivity. In this light, microfluidics has been attracting growing interest. By properly designing microfluidic biochips it is possible to miniaturize the analyte-sensitive areas with an overall reduction of the chip dimension, reduce the liquid reagents and sample volume, improve automation, and increase the number of experiments in a single biochip by multiplexing approaches. However, as the fluidic channel dimensions approach the micron scale, laminar flows become dominant owing to the low Reynolds numbers that typically characterize microfluidics. In these environments mixing times are usually dominated by diffusion, which can be prohibitively long and lead to long-lasting biochemistry experiments. An elegant method to overcome these issues is to actively perturb the liquid laminar flow by exploiting surface acoustic waves (SAWs). With this work, we demonstrate a new approach for SPR biosensing based on the combination of microfluidics, SAW-induced mixing and the real-time phase-interrogation grating-coupling SPR technology. On a single lithium niobate (LN) substrate the nanostructured SPR sensing areas, interdigital transducer (IDT) for SAW generation and polydimethylsiloxane (PDMS) microfluidic chambers were fabricated. SAWs, impinging on the microfluidic chamber, generate acoustic streaming inside the fluid, leading to chaotic advection and thus improved fluid mixing, whilst analytes binding detection is made via SPR method based on SPP excitation via gold metallic grating upon azimuthal orientation and phase interrogation. Our device has been fully characterized in order to separate for the very first time the unwanted SAW heating effect with respect to the fluid stirring inside the microchamber that affect the molecules binding dynamics. Avidin/biotin assay and thiol-polyethylene glycol (bPEG-SH) were exploited as model biological interaction and non-fouling layer respectively. Biosensing kinetics time reduction with SAW-enhanced mixing resulted in a ≈ 82% improvement for bPEG-SH adsorption onto gold and ≈ 24% for avidin/biotin binding—≈ 50% and 18% respectively compared to the heating only condition. These results demonstrate that our biochip can significantly reduce the duration of bioreactions that usually require long times (e.g., PEG-based sensing layer, low concentration analyte detection). The sensing architecture here proposed represents a new promising technology satisfying the major biosensing requirements: scalability and high throughput capabilities. The detection system size and biochip dimension could be further reduced and integrated; in addition, the possibility of reducing biological experiment duration via SAW-driven active mixing and developing multiplexing platforms for parallel real-time sensing could be easily combined. In general, the technology reported in this study can be straightforwardly adapted to a great number of biological system and sensing geometry.

Keywords: biosensor, microfluidics, surface acoustic wave, surface plasmon resonance

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Moses Obenade, Henry U. Okeke, Francis I. Okpiliya, Eugene J. Aniah

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Population growth in Eleme has been rapid over the past 75 years with its attendant pressure on the natural resources of the area. Between 1937 and 2006 the population of Eleme grew from 2,528 to 190,194 and is projected to be above 265,707 in 2016 based on an annual growth rate of 3.4%. Using the combined technologies of Geographic Information Systems (GIS), remote sensing (RS) and Demography techniques as its methodology, this paper examines the environmental implications of rapid population growth on land use dynamics in Eleme between 1986 and 2015. The study reveals that between 1986 and 2006, Built-up area and Farmland increased by 72.67 and 12.77% respectively, while light and thick vegetation recorded a decrease of -6.92 and -61.64% respectively. Water body remains fairly constant with minimal changes. Also, between 2006 and 2015 covering a period of 9 years, Built-up area further increased by 53% with an annual growth rate of 2.32 km2 gaining more land area on the detriment of other land uses. Built-up area has an annual growth rate of 2.32km2 and is expected to increase from 18.67km2 in 2006 to 41.87km2 in 2016.The observed Land used/Land cover dynamics is derived by the demographic characteristics of the Study area. Eleme has a total area of 138km2 out of which the Federal Government of Nigeria compulsorily acquired an estimated area of 59.34km2 for industrial purposes excluding acquisitions by the Rivers State Government. It is evident from the findings of this study that the carrying capacity of Eleme ecosystem is under threat due to the current population growth and land consumption rates. Therefore, measures such as use of appropriate technologies in farming techniques, waste management; investment in family planning and female empowerment, maternal health and education, afforestation programs; and amendment of Land Use Act of 1978 are recommended.

Keywords: population growth, Eleme, land use, GIS and remote sensing

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Authors: D. Courault, L. Hossard, V. Demarez, E. Ndikumana, D. Ho Tong Minh, N. Baghdadi, F. Ruget

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In the global change context, efficient management of the available resources has become one of the most important topics, particularly for sustainable crop development. Timely assessment with high precision is crucial for water resource and pest management. Rice cultivated in Southern France in the Camargue region must face a challenge, reduction of the soil salinity by flooding and at the same time reduce the number of herbicides impacting negatively the environment. This context has lead farmers to diversify crop rotation and their agricultural practices. The objective of this study was to evaluate this crop diversity both in crop systems and in agricultural practices applied to rice paddy in order to quantify the impact on the environment and on the crop production. The proposed method is based on the combined use of crop models and multispectral data acquired from the recent Sentinel 2 satellite sensors launched by the European Space Agency (ESA) within the homework of the Copernicus program. More than 40 images at fine spatial resolution (10m in the optical range) were processed for 2016 and 2017 (with a revisit time of 5 days) to map crop types using random forest method and to estimate biophysical variables (LAI) retrieved by inversion of the PROSAIL canopy radiative transfer model. Thanks to the high revisit time of Sentinel 2 data, it was possible to monitor the soil labor before flooding and the second sowing made by some farmers to better control weeds. The temporal trajectories of remote sensing data were analyzed for various rice cultivars for defining the main parameters describing the phenological stages useful to calibrate two crop models (STICS and SAFY). Results were compared to surveys conducted with 10 farms. A large variability of LAI has been observed at farm scale (up to 2-3m²/m²) which induced a significant variability in the yields simulated (up to 2 ton/ha). Observations on more than 300 fields have also been collected on land use. Various maps were elaborated, land use, LAI, flooding and sowing, and harvest dates. All these maps allow proposing a new typology to classify these paddy crop systems. Key phenological dates can be estimated from inverse procedures and were validated against ground surveys. The proposed approach allowed to compare the years and to detect anomalies. The methods proposed here can be applied at different crops in various contexts and confirm the potential of remote sensing acquired at fine resolution such as the Sentinel2 system for agriculture applications and environment monitoring. This study was supported by the French national center of spatial studies (CNES, funded by the TOSCA).

Keywords: agricultural practices, remote sensing, rice, yield

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