Search results for: on-orbit satellite payload

Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

Procedia PDF Downloads 137

Authors: M. Aboalazayem, Essam A. Gouda, Ahmed M. Moussa, Amr E. Flifl

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Field data collecting is considered one of the most difficult work due to the difficulty of accessing large zones such as large lakes. Also, it is well known that the cost of obtaining field data is very expensive. Remotely monitoring of lake water quality (WQ) provides an economically feasible approach comparing to field data collection. Researchers have shown that lake WQ can be properly monitored via Remote sensing (RS) analyses. Using satellite images as a method of WQ detection provides a realistic technique to measure quality parameters across huge areas. Landsat (LS) data provides full free access to often occurring and repeating satellite photos. This enables researchers to undertake large-scale temporal comparisons of parameters related to lake WQ. Satellite measurements have been extensively utilized to develop algorithms for predicting critical water quality parameters (WQPs). The goal of this paper is to use RS to derive WQ indicators in Aswan High Dam Reservoir (AHDR), which is considered Egypt's primary and strategic reservoir of freshwater. This study focuses on using Landsat8 (L-8) band surface reflectance (SR) observations to predict water-quality characteristics which are limited to Turbidity (TUR), total suspended solids (TSS), and chlorophyll-a (Chl-a). ArcGIS pro is used to retrieve L-8 SR data for the study region. Multiple linear regression analysis was used to derive new correlations between observed optical water-quality indicators in April and L-8 SR which were atmospherically corrected by values of various bands, band ratios, and or combinations. Field measurements taken in the month of May were used to validate WQP obtained from SR data of L-8 Operational Land Imager (OLI) satellite. The findings demonstrate a strong correlation between indicators of WQ and L-8 .For TUR, the best validation correlation with OLI SR bands blue, green, and red, were derived with high values of Coefficient of correlation (R2) and Root Mean Square Error (RMSE) equal 0.96 and 3.1 NTU, respectively. For TSS, Two equations were strongly correlated and verified with band ratios and combinations. A logarithm of the ratio of blue and green SR was determined to be the best performing model with values of R2 and RMSE equal to 0.9861 and 1.84 mg/l, respectively. For Chl-a, eight methods were presented for calculating its value within the study area. A mix of blue, red, shortwave infrared 1(SWR1) and panchromatic SR yielded the greatest validation results with values of R2 and RMSE equal 0.98 and 1.4 mg/l, respectively.

Keywords: remote sensing, landsat 8, nasser lake, water quality

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Authors: Meng-Hao Chen, Jeng-Der Huang, Chia-Ray Chen

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With the new space mission need of high power dissipation, low thermal inertia and cyclical operation unit, such as high power amplifier (HPA) for synthetic aperture radar (SAR) satellite, the development of phase change material (PCM) technology seems to be a proper solution. Generally, the expected benefit of PCM solution is to eliminate temperature variation and maintain the stability of electronic units by using the latent heat during phase change process. It can also result in advantages of decreased radiator area and heater power. However, the PCMs have a drawback of low thermal conductivity that leads to large temperature gradient between the heat source and PCM. This paper thus presents both experimental and simplified numerical investigations on configuration design of PCM’s container. A comparison was carried out between the container with and without internal pin-fins structure. The results showed the benefit of pin-fins that act as the heat transfer enhancer to improve the temperature uniformity during phase transition. Furthermore, thermal testing and measurements were presented for four PCM candidates (i.e. n-octadecane, n-eicosane, glycerin and gallium). The solidification and supercooling behaviors on different PCMs were compared with available literature data and discussed in this study

Keywords: phase change material (PCM), thermal control, solidification, supercooling

Procedia PDF Downloads 354

Authors: Varis Ransibrahmanakul, Doug Pirhalla, Scott Sheridan, Cameron Lee

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U.S. coastal areas and ecosystems are facing multiple sea level rise threats and effects: heavy rain events, cyclones, and changing wind and weather patterns all influence coastal flooding, sedimentation, and erosion along critical barrier islands and can strongly impact habitat resiliency and water quality in protected habitats. These impacts are increasing over time and have accelerated the need for new tracking techniques, models and tools of flood risk to support enhanced preparedness for coastal management and mitigation. To address this issue, NOAA National Ocean Service (NOS) evaluated new metrics from satellite altimetry AVISO/Copernicus and MODIS IR flood extents to isolate nodes atmospheric variability indicative of elevated sea level and nuisance flood events. Using de-trended time series of cross-shelf sea surface heights (SSH), we identified specific Self Organizing Maps (SOM) nodes and transitions having a strongest regional association with oceanic spatial patterns (e.g., heightened downwelling favorable wind-stress and enhanced southward coastal transport) indicative of elevated coastal sea levels. Results show the impacts of the inverted barometer effect as well as the effects of surface wind forcing; Ekman-induced transport along broad expanses of the U.S. eastern coastline. Higher sea levels and corresponding localized flooding are associated with either pattern indicative of enhanced on-shore flow, deepening cyclones, or local- scale winds, generally coupled with an increased local to regional precipitation. These findings will support an integration of satellite products and will inform seasonal outlook model development supported through NOAAs Climate Program Office and NOS office of Center for Operational Oceanographic Products and Services (CO-OPS). Overall results will prioritize ecological areas and coastal lab facilities at risk based on numbers of nuisance flood projected and inform coastal management of flood risk around low lying areas subjected to bank erosion.

Keywords: AVISO satellite altimetry SSHA, MODIS IR flood map, nuisance flood, remote sensing of flood

Procedia PDF Downloads 116

Authors: Avinash Kumar Ranjan, Akash Anand

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The Land Surface Temperature (LST) is an essential factor accompanying to rise urban heat and climate warming within a city in micro level. It is also playing crucial role in global change study as well as radiation budgets measuring in heat balance studies. The information of LST is very substantial to recognize the urban climatology, ecological changes, anthropological and environmental interactions etc. The Chief motivation of present study focus on time series of ANN model that taken a sequence of LST values of 2000, 2008 and 2016, realize the pattern of variation within the data set and predict the LST values for 2024 and 2032. The novelty of this study centers on evaluation of LST using series of multi-temporal MODIS (MOD 11A2) satellite data by Maximum Value Composite (MVC) techniques. The results derived from this study endorse the proficiency of Geoinformatics Technology with integration of ANN to gain knowledge, understanding and building of precise forecast from the complex physical world database. This study will also focus on influence of Land Use/ Land Cover (LU/LC) variation on Land Surface Temperature.

Keywords: LST, geoinformatics technology, ANN, MODIS satellite imagery, MVC

Procedia PDF Downloads 214

Authors: Wang Yang

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Shenzhen is a modern city of China's reform and opening-up policy, the development of urban morphology has been established on the administration of the Chinese government. This city`s planning paradigm is primarily affected by the spatial structure and human behavior. The subjective urban agglomeration center is divided into several groups and centers. In comparisons of this effect, the city development law has better to be neglected. With the continuous development of the internet, extensive data technology has been introduced in China. Data mining and data analysis has become important tools in municipal research. Data mining has been utilized to improve data cleaning such as receiving business data, traffic data and population data. Prior to data mining, government data were collected by traditional means, then were analyzed using city-relationship research, delaying the timeliness of urban development, especially for the contemporary city. Data update speed is very fast and based on the Internet. The city's point of interest (POI) in the excavation serves as data source affecting the city design, while satellite remote sensing is used as a reference object, city analysis is conducted in both directions, the administrative paradigm of government is broken and urban research is restored. Therefore, the use of data mining in urban analysis is very important. The satellite remote sensing data of the Shenzhen city in July 2018 were measured by the satellite Modis sensor and can be utilized to perform land surface temperature inversion, and analyze city heat island distribution of Shenzhen. This article acquired and classified the data from Shenzhen by using Data crawler technology. Data of Shenzhen heat island and interest points were simulated and analyzed in the GIS platform to discover the main features of functional equivalent distribution influence. Shenzhen is located in the east-west area of China. The city’s main streets are also determined according to the direction of city development. Therefore, it is determined that the functional area of the city is also distributed in the east-west direction. The urban heat island can express the heat map according to the functional urban area. Regional POI has correspondence. The research result clearly explains that the distribution of the urban heat island and the distribution of urban POIs are one-to-one correspondence. Urban heat island is primarily influenced by the properties of the underlying surface, avoiding the impact of urban climate. Using urban POIs as analysis object, the distribution of municipal POIs and population aggregation are closely connected, so that the distribution of the population corresponded with the distribution of the urban heat island.

Keywords: POI, satellite remote sensing, the population distribution, urban heat island thermal map

Procedia PDF Downloads 82

Authors: Saul A. Torres Z., Eduardo Liceaga C., Alfredo Arias M.

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We seek to develop a UAV for agricultural spraying at a maximum altitude of 5000 meters above sea level, with a payload of 100 liters of fumigant. For the developing the aerodynamic design of the aircraft is using computational tools such as the "Vortex Lattice Athena" software, "MATLAB", "ANSYS FLUENT", "XFoil" package among others. Also methods are being used structured programming, exhaustive analysis of optimization methods and search. The results have a very low margin of error, and the multi-objective problems can be helpful for future developments. Also we developed method for Stability Analysis (Lateral-Directional and Longitudinal).

Keywords: aerodynamics design, optimization, algorithm genetic, multi-objective problem, longitudinal stability, lateral-directional stability

Procedia PDF Downloads 563

Authors: Rodrigo T. Sepulveda-Hirose, Ana B. Carrera-Aguilar, Francisco Andree Ramirez-Casas, Alondra Orozco-Gomez, Miguel Angel Sanchez-Caro, Carlos Herrera-Ventosa

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High concentrations of particulate matter in the atmosphere pose a threat to human health, especially over areas with high concentrations of population; however, field air pollution monitoring is expensive and time-consuming. In order to achieve reduced costs and global coverage of the whole urban area, remote sensing can be used. This study evaluates PM2.5 concentrations, over the Mexico City´s metropolitan area, are estimated using atmospheric reflectance from LANDSAT 8, satellite imagery and historical PM2.5 measurements of the Automatic Environmental Monitoring Network of Mexico City (RAMA). Through the processing of the available satellite images, a preliminary model was generated to evaluate the optimal bands for the generation of the final model for Mexico City. Work on the final model continues with the results of the preliminary model. It was found that infrared bands have helped to model in other cities, but the effectiveness that these bands could provide for the geographic and climatic conditions of Mexico City is still being evaluated.

Keywords: air pollution modeling, Landsat 8, PM2.5, remote sensing

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Authors: Shreedevi Moharana, Subashisa Dutta

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The present study focuses on the estimation of crop biophysical parameters like crop chlorophyll, nitrogen and water stress at plot scale in the crop fields. To achieve these, we have used high-resolution satellite LISS IV imagery. A new methodology has proposed in this research work, the spectral shape function of paddy crop is employed to get the significant wavelengths sensitive to paddy crop parameters. From the shape functions, regression index models were established for the critical wavelength with minimum and maximum wavelengths of multi-spectrum high-resolution LISS IV data. Moreover, the functional relationships were utilized to develop the index models. From these index models crop, biophysical parameters were estimated and mapped from LISS IV imagery at plot scale in crop field level. The result showed that the nitrogen content of the paddy crop varied from 2-8%, chlorophyll from 1.5-9% and water content variation observed from 40-90% respectively. It was observed that the variability in rice agriculture system in India was purely a function of field topography.

Keywords: crop parameters, index model, LISS IV imagery, plot scale, shape function

Procedia PDF Downloads 139

Authors: E. K. Naseela, B. M. Dodamani, Chaithra Chandran

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The GIS and remote sensing techniques facilitate accurate estimation of surface runoff from watershed. In the present study an attempt has been made to evaluate the applicability of Natural Resources Service Curve Number method using GIS and Remote sensing technique in the upper Krishna basin (69,425 Sq.km). Landsat 7 (with resolution 30 m) satellite data for the year 2012 has been used for the preparation of land use land cover (LU/LC) map. The hydrologic soil group is mapped using GIS platform. The weighted curve numbers (CN) for all the 5 subcatchments calculated on the basis of LU/LC type and hydrologic soil class in the area by considering antecedent moisture condition. Monthly rainfall data was available for 58 raingauge stations. Overlay technique is adopted for generating weighted curve number. Results of the study show that land use changes determined from satellite images are useful in studying the runoff response of the basin. The results showed that there is no significant difference between observed and estimated runoff depths. For each subcatchment, statistically positive correlations were detected between observed and estimated runoff depth (0.6Keywords: curve number, GIS, remote sensing, runoff

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Authors: Neetu Manocha

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Image segmentation is a technique where a picture is parted into distinct parts having similar features which have a place with similar items. Various segmentation strategies have been proposed as of late by prominent analysts. But, after ultimate thorough research, the novelists have analyzed that generally, the old methods do not decrease the segmentation error rate. Then author finds the technique HIST-SI to decrease the segmentation error rates. In this technique, cluster-based and threshold-based segmentation techniques are merged together. After then, to improve the result of HIST-SI, the authors added the method of filtering and linking in this technique named Imp_HIST-SI to decrease the segmentation error rates. The goal of this research is to find a new technique to decrease the segmentation error rates and produce much better results than the HIST-SI technique. For testing the proposed technique, a dataset of Bhuvan – a National Geoportal developed and hosted by ISRO (Indian Space Research Organisation) is used. Experiments are conducted using Scikit-image & OpenCV tools of Python, and performance is evaluated and compared over various existing image segmentation techniques for several matrices, i.e., Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR).

Keywords: satellite image, image segmentation, edge detection, error rate, MSE, PSNR, HIST-SI, linking, filtering, imp_HIST-SI

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Authors: Ishrath, Tapas Kumar Chatterjee

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The present study aims developing interpretation and evaluation to integrate various data types for management of existing water resources for sustainable use. Proper study should be followed based on the geomorphology of the area. Thematic maps such as lithology, base map, land use/land cover, geomorphology, drainage and lineaments maps are prepared to study the area by using area toposheet, IRS P6 and LISIII Satellite imagery. These thematic layers are finally integrated by using Arc GIS, Arc View, and software to prepare a ground water potential zones map of the study area. In this study, an integrated approach involving remote sensing and GIS techniques has successfully been used in identifying groundwater potential zones in the study area to classify them as good, moderate and poor. It has been observed that Pediplain shallow (PPS) has good recharge, Pediplain moderate (PPM) has moderately good recharge, Pediment Inselberg complex (PIC) has poor recharge and Inselberg (I) has no recharge. The study has concluded that remote sensing and GIS techniques are very efficient and useful for identifying ground water potential zones.

Keywords: satellite remote sensing, GIS, ground water potential zones, Miyapur

Procedia PDF Downloads 418

Authors: R. Lhissou , A. El Harti , K. Chokmani, E. Bachaoui, A. El Ghmari

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Soil salinity is a serious environmental hazard in many countries around the world especially the arid and semi-arid countries like Morocco. Salinization causes negative effects on the ground; it affects agricultural production, infrastructure, water resources and biodiversity. Remote sensing can provide soil salinity information for large areas, and in a relatively short time. In addition, remote sensing is not limited by extremes in terrain or hazardous condition. Contrariwise, experimental methods for monitoring soil salinity by direct measurements in situ are very demanding of time and resources, and also very limited in spatial coverage. In the irrigated perimeter of Tadla plain in central Morocco, the increased use of saline groundwater and surface water, coupled with agricultural intensification leads to the deterioration of soil quality especially by salinization. In this study, we assessed several spectral indices of soil salinity cited in the literature using Landsat TM satellite images and field measurements of electrical conductivity (EC). Three Landsat TM satellite images were taken during 3 months in the dry season (September, October and November 2011). Based on field measurement data of EC collected in three field campaigns over the three dates simultaneously with acquisition dates of Landsat TM satellite images, a two assessment techniques are used to validate a soil salinity spectral indices. Firstly, the spectral indices are validated locally by pixel. The second validation technique is made using a window of size 3x3 pixels. The results of the study indicated that the second technique provides getting a more accurate validation and the assessment has shown its limits when it comes to assess across the pixel. In addition, the EC values measured from field have a good correlation with some spectral indices derived from Landsat TM data and the best results show an r² of 0.88, 0.79 and 0.65 for Salinity Index (SI) in the three dates respectively. The results have shown the usefulness of spectral indices as an auxiliary variable in the spatial estimation and mapping salinity in irrigated land.

Keywords: remote sensing, spectral indices, soil salinity, irrigated land

Procedia PDF Downloads 365

Authors: Ghasem Sharifi, Hamed Shahmohamadi Ousaloo, Milad Azimi, Mehran Mirshams

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The ever-increasing use of satellites demands a search for increasingly accurate and reliable pointing systems. Reaction wheels are rotating devices used commonly for the attitude control of the spacecraft since provide a wide range of torque magnitude and high reliability. The numerical modeling of this device can significantly enhance the accuracy of the satellite control in space. Modeling the wheel rotation in the presence of the various frictions is one of the critical parts of this approach. This paper presents a Dynamic Model Control of a Reaction Wheel (DMCR) in the current control mode. In current-mode, the required current is delivered to the coils in order to achieve the desired torque. During this research, all the friction parameters as viscous and coulomb, motor coefficient, resistance and voltage constant are identified. In order to model identification of a reaction wheel, numerous varying current commands apply on the particular wheel to verify the estimated model. All the parameters of DMCR are identified by classical Levenberg-Marquardt (CLM) optimization method. The experimental results demonstrate that the developed model has an appropriate precise and can be used in the satellite control simulation.

Keywords: experimental modeling, friction parameters, model identification, reaction wheel

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Authors: Tridipa Biswas, Kamal Pandey

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A Digital Elevation Model (DEM) is a simple representation of a surface in 3 dimensional space with elevation as the third dimension along with X (horizontal coordinates) and Y (vertical coordinates) in rectangular coordinates. DEM has wide applications in various fields like disaster management, hydrology and watershed management, geomorphology, urban development, map creation and resource management etc. Cartosat-1 or IRS P5 (Indian Remote Sensing Satellite) is a state-of-the-art remote sensing satellite built by ISRO (May 5, 2005) which is mainly intended for cartographic applications.Cartosat-1 is equipped with two panchromatic cameras capable of simultaneous acquiring images of 2.5 meters spatial resolution. One camera is looking at +26 degrees forward while another looks at –5 degrees backward to acquire stereoscopic imagery with base to height ratio of 0.62. The time difference between acquiring of the stereopair images is approximately 52 seconds. The high resolution stereo data have great potential to produce high-quality DEM. The high-resolution Cartosat-1 stereo image data is expected to have significant impact in topographic mapping and watershed applications. The objective of the present study is to generate high-resolution DEM, quality evaluation in different elevation strata, generation of ortho-rectified image and associated accuracy assessment from CARTOSAT-1 data based Ground Control Points (GCPs) for Aglar watershed (Tehri-Garhwal and Dehradun district, Uttarakhand, India). The present study reveals that generated DEMs (10m and 30m) derived from the CARTOSAT-1 stereo pair is much better and accurate when compared with existing DEMs (ASTER and CARTO DEM) also for different terrain parameters like slope, aspect, drainage, watershed boundaries etc., which are derived from the generated DEMs, have better accuracy and results when compared with the other two (ASTER and CARTO) DEMs derived terrain parameters.

Keywords: ASTER-DEM, CARTO-DEM, CARTOSAT-1, digital elevation model (DEM), ortho-rectified image, photogrammetry, RPC, stereo pair, terrain parameters

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Authors: Dhanuj M. Gandikota

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Sensor-derived three-dimensional (3D) point clouds of trees are invaluable in remote sensing analysis for the accurate measurement of key structural metrics, bio-inventory values, spatial planning/visualization, and ecological modeling. Machine learning (ML) holds the potential in addressing the restrictive tradeoffs in cost, spatial coverage, resolution, and information gain that exist in current point cloud sensing methods. Terrestrial laser scanning (TLS) remains the highest fidelity source of both canopy and below-canopy structural features, but usage is limited in both coverage and cost, requiring manual deployment to map out large, forested areas. While aerial laser scanning (ALS) remains a reliable avenue of LIDAR active remote sensing, ALS is also cost-restrictive in deployment methods. Space-borne photogrammetry from high-resolution satellite constellations is an avenue of passive remote sensing with promising viability in research for the accurate construction of vegetation 3-D point clouds. It provides both the lowest comparative cost and the largest spatial coverage across remote sensing methods. However, both space-borne photogrammetry and ALS demonstrate technical limitations in the capture of valuable below-canopy point cloud data. Looking to minimize these tradeoffs, we explored a class of powerful ML algorithms called Deep Learning (DL) that show promise in recent research on 3-D point cloud reconstruction and interpolation. Our research details the efficacy of applying these DL techniques to reconstruct accurate below-canopy point clouds from space-borne and aerial remote sensing through learned patterns of tree species fractal symmetry properties and the supplementation of locally sourced bio-inventory metrics. From our dataset, consisting of tree point clouds obtained from TLS, we deconstructed the point clouds of each tree into those that would be obtained through ALS and satellite photogrammetry of varying resolutions. We fed this ALS/satellite point cloud dataset, along with the simulated local bio-inventory metrics, into the DL point cloud reconstruction architectures to generate the full 3-D tree point clouds (the truth values are denoted by the full TLS tree point clouds containing the below-canopy information). Point cloud reconstruction accuracy was validated both through the measurement of error from the original TLS point clouds as well as the error of extraction of key structural metrics, such as crown base height, diameter above root crown, and leaf/wood volume. The results of this research additionally demonstrate the supplemental performance gain of using minimum locally sourced bio-inventory metric information as an input in ML systems to reach specified accuracy thresholds of tree point cloud reconstruction. This research provides insight into methods for the rapid, cost-effective, and accurate construction of below-canopy tree 3-D point clouds, as well as the supported potential of ML and DL to learn complex, unmodeled patterns of fractal tree growth symmetry.

Keywords: deep learning, machine learning, satellite, photogrammetry, aerial laser scanning, terrestrial laser scanning, point cloud, fractal symmetry

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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

Procedia PDF Downloads 116

Authors: Chen Chuanzhi, Guo Yunyun

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The large linear array feeding parabolic cylindrical antenna of the satellite has the ability of large-area line focusing, multi-directional beam clusters simultaneously in a certain azimuth plane and elevation plane, corresponding quickly to different orientations and different directions in a wide frequency range, dual aiming of frequency and direction, and combining space power. Therefore, the large-diameter parabolic cylindrical antenna has become one of the new development directions of spaceborne antennas. Limited by the size of the rocked fairing, the large-diameter spaceborne antenna is required to be small mass and have a deployment function. After being orbited, the antenna can be deployed by expanding and be stabilized. However, few types of structures can be used to construct large cylindrical shell structures in existing structures, which greatly limits the development and application of such antennas. Aiming at high structural efficiency, the geometrical characteristics of parabolic cylinders and mechanism topological mapping law to the expandable truss are studied, and the basic configuration of deployable truss with cylindrical shell is structured. Then a modular truss parabolic cylindrical antenna is designed in this paper. The antenna has the characteristics of stable structure, high precision of reflecting surface formation, controllable motion process, high storage rate, and lightweight, etc. On the basis of the overall configuration comprehensive theory and optimization method, the structural stiffness of the modular truss parabolic cylindrical antenna is improved. And the bearing density and impact resistance of support structure are improved based on the internal tension optimal distribution method of reflector forming. Finally, a truss-type cylindrical deployable support structure with high constriction-deployment ratio, high stiffness, controllable deployment, and low mass is successfully developed, laying the foundation for the application of large-diameter parabolic cylindrical antennas in satellite antennas.

Keywords: linear array feed antenna, truss type, parabolic cylindrical antenna, spaceborne antenna

Procedia PDF Downloads 117

Authors: Berhanu Keno Terfa

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To understand the consequences of urbanization, accurate, and long-term representation of urban dynamics is essential. Remote sensing data from various multi-temporal satellite images viz., TM (1987), TM (1995), ETM+ (2005) and OLI (2017) were used. An integrated method, landscape metrics, built-up density, and urban growth type analysis were employed to analyze the pattern, process, and overall growth status in the city. The result showed that the built-up area had increased by 541.3% between 1987 and 2017, at an average annual increment of 8.9%. The area of urban expansion in a city has tripled during the 2005-2017 period as compared to 187- 1995. The major growth took place in the east and southeast directions during 1987–1995 period, whereas predominant built-up development was observed in south and southeast direction during 1995–2017 period. The analysis using landscape metrics and urban typologies showed that Hawassa experienced a fragmented and irregular spatiotemporal urban growth patterns, mostly by extension, suggesting a strong tendency towards sprawl in the past three decades.

Keywords: Hawassa, spatial patterns, remote sensing, multi-temporal, urban sprawl

Procedia PDF Downloads 116

Authors: Yumei Wu, Xiaoyan Dang, Shenglong Yang, Shengmao Zhang

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The East China Sea is one of four main seas in China, where there are many fishery resources. Some important fishing grounds, such as Zhousan fishing ground important to society. But the eco-environment is destroyed seriously due to the rapid developing of industry and economy these years. In this paper, about twenty-year satellite data from MODIS and the statistical information of marine environment from the China marine environmental quality bulletin were applied to do the research. The chlorophyll-a concentration data from MODIS were dealt with in the East China Sea and then used to analyze the features and variations of plankton biomass in recent years. The statistics method was used to obtain their spatial and temporal features. The plankton biomass in the Yangtze River estuary and the Taizhou region were highest. The high phytoplankton biomass usually appeared between the 88th day to the 240th day (end-March - August). In the peak time of phytoplankton blooms, the Taizhou islands was the earliest, and the South China Sea was the latest. The intensity and period of phytoplankton blooms were connected with the global climate change. This work give us confidence to use satellite data to do more researches about the China Sea, and it also provides some help for us to know about the eco-environmental variation of the East China Sea and regional effect from global climate change.

Keywords: the East China Sea, phytoplankton biomass, temporal and spatial variation, phytoplankton bloom

Procedia PDF Downloads 297

Authors: Ali J. Ghandour, Houssam A. Krayem, Abedelkarim A. Jezzini

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High-resolution satellite images and remote sensing can provide global information in a fast way compared to traditional methods of data collection. Under such high resolution, a road is not a thin line anymore. Objects such as cars and trees are easily identifiable. Automatic vehicles enumeration can be considered one of the most important applications in traffic management. In this paper, autonomous vehicle detection and classification approach in highway environment is proposed. This approach consists mainly of three stages: (i) first, a set of preprocessing operations are applied including soil, vegetation, water suppression. (ii) Then, road networks detection and delineation is implemented using built-up area index, followed by several morphological operations. This step plays an important role in increasing the overall detection accuracy since vehicles candidates are objects contained within the road networks only. (iii) Multi-level Otsu segmentation is implemented in the last stage, resulting in vehicle detection and classification, where detected vehicles are classified into cars and trucks. Accuracy assessment analysis is conducted over different study areas to show the great efficiency of the proposed method, especially in highway environment.

Keywords: remote sensing, object identification, vehicle and road extraction, vehicle and road features-based classification

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Authors: Sunghun Jung

Abstract:

The existing web map service providers contract with the satellite operators to update their maps by paying an astronomical amount of money, but the cost could be minimized by operating a cheap and small UAV. In contrast to the satellites, we only need to replace aged battery packs from time to time for the usage of UAVs. Utilizing both a regular camera and an infrared camera mounted on a small, solar-powered, long-endurance, and hoverable UAV, daytime ground surface photographs, and nighttime infrared photographs will be continuously and repeatedly uploaded to the web map server and overlapped with the existing ground surface photographs in real-time. The real-time web map service using a small, solar-powered, long-endurance, and hoverable UAV can also be applied to the surveillance missions, in particular, to detect border area intruders. The improved real-time image stitching algorithm is developed for the graphic map data overlapping. Also, a small home server will be developed to manage the huge size of incoming map data. The map photographs taken at tens or hundreds of kilometers by a UAV would improve the map graphic resolution compared to the map photographs taken at thousands of kilometers by satellites since the satellite photographs are limited by weather conditions.

Keywords: long-endurance, real-time web map service (RWMS), solar-powered, unmanned aerial vehicle (UAV)

Procedia PDF Downloads 247

Authors: Y. Fekir, K. Mederbal, M. A. Hammadouche, D. Anteur

Abstract:

The cultural park of the Tassili, occupying a large area of Algeria, is characterized by a rich vegetative biodiversity to be preserved and managed both in time and space. The management of a large area (case of Tassili), by its complexity, needs large amounts of data, which for the most part, are spatially localized (DEM, satellite images and socio-economic information etc.), where the use of conventional and traditional methods is quite difficult. The remote sensing, by its efficiency in environmental applications, became an indispensable solution for this kind of studies. Multispectral imaging sensors have been very useful in the last decade in very interesting applications of remote sensing. They can aid in several domains such as the de¬tection and identification of diverse surface targets, topographical details, and geological features. In this work, we try to extract vegetative areas using fusion techniques between data acquired from sensor on-board the Earth Observing 1 (EO-1) satellite and Landsat ETM+ and TM sensors. We have used images acquired over the Oasis of Djanet in the National Park of Tassili in the south of Algeria. Fusion technqiues were applied on the obtained image to extract the vegetative fraction of the different classes of land use. We compare the obtained results in vegetation end member extraction with vegetation indices calculated from both Hyperion and other multispectral sensors.

Keywords: Landsat ETM+, EO1, data fusion, vegetation, Tassili, Algeria

Procedia PDF Downloads 407

Authors: Heewon Jeong, Seongpyo Kim, Joon Ha Kim

Abstract:

Over the last few tens of years, the coastal regions of Korea have experienced red tide algal blooms, which are harmful and toxic to both humans and marine organisms due to their potential threat. It was accelerated owing to eutrophication by human activities, certain oceanic processes, and climate change. Previous studies have tried to monitoring and predicting the algae concentration of the ocean with the bio-optical algorithms applied to color images of the satellite. However, the accurate estimation of algal blooms remains problems to challenges because of the complexity of coastal waters. Therefore, this study suggests a new method to identify the concentration of red tide algal bloom from images of geostationary ocean color imager (GOCI) which are representing the water environment of the sea in Korea. The method employed GOCI images, which took the water leaving radiances centered at 443nm, 490nm and 660nm respectively, as well as observed weather data (i.e., humidity, temperature and atmospheric pressure) for the database to apply optical characteristics of algae and train deep learning algorithm. Convolution neural network (CNN) was used to extract the significant features from the images. And then artificial neural network (ANN) was used to estimate the concentration of algae from the extracted features. For training of the deep learning model, backpropagation learning strategy is developed. The established methods were tested and compared with the performances of GOCI data processing system (GDPS), which is based on standard image processing algorithms and optical algorithms. The model had better performance to estimate algae concentration than the GDPS which is impossible to estimate greater than 5mg/m³. Thus, deep learning model trained successfully to assess algae concentration in spite of the complexity of water environment. Furthermore, the results of this system and methodology can be used to improve the performances of remote sensing. Acknowledgement: This work was supported by the 'Climate Technology Development and Application' research project (#K07731) through a grant provided by GIST in 2017.

Keywords: deep learning, algae concentration, remote sensing, satellite

Procedia PDF Downloads 152

Authors: Takashi Yamaguchi, Ichio Asanuma, Jong G. Park, Kenneth J. Mackin, John Mittleman

Abstract:

In this paper, vessel detection using the artificial neural network is proposed in order to automatically construct the vessel detection model from the satellite imagery of day/night band (DNB) in visible infrared in the products of Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (Suomi-NPP).The goal of our research is the establishment of vessel detection method using the satellite imagery of DNB in order to monitor the change of vessel activity over the wide region. The temporal vessel monitoring is very important to detect the events and understand the circumstances within the maritime environment. For the vessel locating and detection techniques, Automatic Identification System (AIS) and remote sensing using Synthetic aperture radar (SAR) imagery have been researched. However, each data has some lack of information due to uncertain operation or limitation of continuous observation. Therefore, the fusion of effective data and methods is important to monitor the maritime environment for the future. DNB is one of the effective data to detect the small vessels such as fishery ships that is difficult to observe in AIS. DNB is the satellite sensor data of VIIRS on Suomi-NPP. In contrast to SAR images, DNB images are moderate resolution and gave influence to the cloud but can observe the same regions in each day. DNB sensor can observe the lights produced from various artifact such as vehicles and buildings in the night and can detect the small vessels from the fishing light on the open water. However, the modeling of vessel detection using DNB is very difficult since complex atmosphere and lunar condition should be considered due to the strong influence of lunar reflection from cloud on DNB. Therefore, artificial neural network was applied to learn the vessel detection model. For the feature of vessel detection, Brightness Temperature at the 3.7 μm (BT3.7) was additionally used because BT3.7 can be used for the parameter of atmospheric conditions.

Keywords: artificial neural network, day/night band, remote sensing, Suomi National Polar-orbiting Partnership, vessel detection, Visible Infrared Imaging Radiometer Suite

Procedia PDF Downloads 212

Authors: Ankur P. Sati, Manju Mohan

Abstract:

The hazardous combination of smoke and pollutant gases, smog, is harmful for health. There are strong evidences that the Agricultural waste burning (AWB) in the Northern India leads to adverse air quality in Delhi and its surrounding regions. A severe smog episode was observed over Delhi, India during November 2012 which resulted in very low visibility and various respiratory problems. Very high values of pollutants (PM10 as high as 989 µg m-3, PM2.5 as high as 585 µg m-3 an NO2 as high as 540 µg m-3) were measured all over Delhi during the smog episode. Ultra Violet Aerosol Index (UVAI) from Aura satellite and Aerosol Optical Depth (AOD) are used in the present study along with the output trajectories from HYSPLIT model and the in-situ data. Satellite data also reveal that AOD, UVAI are always at its highest during the farmfires duration in Punjab region of India and the extent of these farmfires may be increasing. It is observed that during the smog episode all the AOD, UVAI, PM2.5 and PM10 values surpassed those of the Diwali period (one of the most polluted events in the city) by a considerable amount at all stations across Delhi. The parameters used from the remote sensing data and the ground based observations at various stations across Delhi are very well in agreement about the intensity of Smog episode. The analysis clearly shows that regional pollution can have greater contributions in deteriorating the air quality than the local under adverse meteorological conditions.

Keywords: smog, farmfires, AOD, remote sensing

Procedia PDF Downloads 217

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

Abstract:

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

Procedia PDF Downloads 95

Authors: R. Vidhya, M. Navamuniyammal M. Sivakumar, S. Reeta

Abstract:

The unplanned urbanization affects the environment due to pollution, conditions of the atmosphere, decreased vegetation and the pervious and impervious soil surface. Considered to be a cumulative effect of all these impacts is the Urban Heat Island. In this paper, the urban heat island effect is studied for the Chennai city, TamilNadu, South India using satellite remote sensing data. LANDSAT 8 OLI and TIRS DATA acquired on 9th September 2014 were used to Land Surface Temperature (LST) map, vegetation fraction map, Impervious surface fraction, Normalized Difference Water Index (NDWI), Normalized Difference Building Index (NDBI) and Normalized Difference Vegetation Index (NDVI) map. The relationship among LST, Vegetation fraction, NDBI, NDWI, and NDVI was calculated. The Chennai city’s Urban Heat Island effect is significant, and the results indicate LST has strong negative correlation with the vegetation present and positive correlation with NDBI. The vegetation is the main factor to control urban heat island effect issues in urban area like Chennai City. This study will help in developing measures to land use planning to reduce the heat effects in urban area based on remote sensing derivatives.

Keywords: land surface temperature, brightness temperature, emissivity, vegetation index

Procedia PDF Downloads 245

Authors: Maria A. Castillo H., Andres R. Leandro, Jose F. Bienvenido B.

Abstract:

During the last 20 years, the globalization of economies, population growth, and the increase in the consumption of fresh agricultural products have generated greater demand for ornamentals, flowers, fresh fruits, and vegetables, mainly from tropical areas. This market situation has demanded greater competitiveness and control over production, with more efficient protected agriculture technologies, which provide greater productivity and allow us to guarantee the quality and quantity that is required in a constant and sustainable way. Guatemala, located in the north of Central America, is one of the largest exporters of agricultural products in the region and exports fresh vegetables, flowers, fruits, ornamental plants, and foliage, most of which were grown in greenhouses. Although there are no official agricultural statistics on greenhouse production, several thesis works, and congress reports have presented consistent estimates. A wide range of protection structures and roofing materials are used, from the most basic and simple ones for rain control to highly technical and automated structures connected with remote sensors for monitoring and control of crops. With this breadth of technological models, it is necessary to analyze georeferenced data related to the cultivated area, to the different existing models, and to the covering materials, integrated with altitude, climate, and soil data. The georeferenced registration of the production units, the data collection with digital tools, the use of satellite images, and geographic information systems (GIS) provide reliable tools to elaborate more complete, agile, and dynamic information maps. This study details a methodology proposed for gathering georeferenced data of high protection structures (greenhouses) in Guatemala, structured in four phases: diagnosis of available information, the definition of the geographic frame, selection of satellite images, and integration with an information system geographic (GIS). It especially takes account of the actual lack of complete data in order to obtain a reliable decision-making system; this gap is solved through the proposed methodology. A summary of the results is presented in each phase, and finally, an evaluation with some improvements and tentative recommendations for further research is added. The main contribution of this study is to propose a methodology that allows to reduce the gap of georeferenced data in protected agriculture in this specific area where data is not generally available and to provide data of better quality, traceability, accuracy, and certainty for the strategic agricultural decision öaking, applicable to other crops, production models and similar/neighboring geographic areas.

Keywords: greenhouses, protected agriculture, GIS, Guatemala, satellite image, digital tools, precision agriculture

Procedia PDF Downloads 165

Authors: Yang Li, Xiaorong Guan, Cheng Xu

Abstract:

In order to design a suitable control scheme for human extremity exoskeleton, the interaction force control scheme with traditional PI controller was presented, and the simulation study of the electromechanical system of the human extremity exoskeleton was carried out by using a MATLAB/Simulink module. By analyzing the simulation calculation results, it was shown that the traditional PI controller is not very suitable for every movement speed of human body. So, at last the fuzzy self-adaptive PI controller was presented to solve this problem. Eventually, the superiority and feasibility of the fuzzy self-adaptive PI controller was proved by the simulation results and experimental results.

Keywords: human extremity exoskeleton, interaction force control scheme, simulation study, fuzzy self-adaptive pi controller, man-machine coordinated walking, bear payload

Procedia PDF Downloads 334