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Reliable and accurate crop classification maps are an important data source for agricultural monitoring and food security assessment studies. For many years, crop type classification and monitoring were focused on single-source optical satellite data classification. With advancements in sensor technologies and processing capabilities, the potential of multi-source satellite imagery has gained increasing attention. The combination of optical and radar data is particularly promising in the context of crop type classification as it allows explaining the advantages of both sensor types with respect to e.g. vegetation structure and biochemical properties. This review article gives a comprehensive overview of studies on crop type classification using optical and radar data fusion approaches. A structured review of fusion approaches, classification strategies and potential for mapping specific crop types is provided. Finally, the partially untapped potential of radar-optical fusion approaches, research gaps and challenges for upcoming future studies are highlighted and discussed. [ABSTRACT FROM AUTHOR]

The use of the rotor equivalent wind speed for determination of power curves and annual energy production for wind turbines is advocated in the second edition of the IEC 61400‐12‐1 standard. This requires the measurements of wind speeds at different heights, for which remote sensing equipment is recommended in addition to meteorological masts. In this paper, we present a theoretical analysis that shows that the relevance of the rotor equivalent wind speed method depends on turbine dimensions and wind shear regime. For situations where the ratio of rotor diameter and hub height is smaller than 1.8, the rotor equivalent wind speed method is not needed if the wind shear coefficient at the location of the wind turbine has a constant value between −0.05 and 0.4: in these cases, the rotor equivalent wind speed and the wind speed at hub height are within 1%. For complex terrains with high wind shear deviations are larger. The effect of non‐constant wind shear exponent, ie, different wind shear coefficients for lower and upper half of the rotor swept area especially at offshore conditions is limited to also about 1%. [ABSTRACT FROM AUTHOR]

Recent advances in low-cost microprocessors and sensors have allowed for the development of low-cost spectrometers for public experimentation and education outreach. In the context of the present paper and other recent research, low-cost spectrometers are important remote sensing instruments for numerous applications in citizen science and education. We built five spectrometer designs then tested them for absorption using concentrations of fluids and reflectance using colour swatches and minerals found on Mars as well as other materials possessing a high near infra-red (NIR) reflectance. These results were then compared with a laboratory grade spectrometer. We found that while all spectrometers produced meaningful results during the fluid concentration tests, there were considerable differences in accuracies between home-made instruments during the reflectance testing. Two spectrometer designs were identified that produced useful results for a range of low-cost scientific applications. [ABSTRACT FROM AUTHOR]

Highlights • We modelled spatial and temporal patterns of fire incidents in the Himalayan foothills. • Temperature affected fire incidents positively in March, but had no effect in April. • Low altitude areas with Sal forest and grasslands are particularly vulnerable to fire. • Fire incidences showed non-linear (uni-modal) trends with distance to village and village area. Abstract The tropical forests situated in the Himalayan foothills (terai) experience frequent wildfires which can alter the vegetation structure and composition, challenging tiger conservation efforts in this region. Hence, there is a need for better understanding of the drivers of forest fire to aid efficient management, but these efforts are hampered by the deficiency of spatial and temporal data on fire incidences. Advancement in remote sensing technology provides an opportunity to understand the spatial and temporal patterns of wildfires in relation to anthropogenic, ecological, and environmental drivers. We used MODIS fire data from 2001 to 2015 to understand fire incidences in Valmiki Tiger Reserve (VTR), an important tiger habitat area in the Himalayan terai region. We analyzed fire incidences to understand monthly and inter-annual variation of fire incidences at two spatial scales: first, using only climatic variables considering VTR as a single spatial unit and the second, to understand the fire dynamics at 1 km2 spatial resolution using climatic, ecological, and anthropogenic variables. The results show that fire incidences occurred from January to May, 88% of which occurred in March and April. Overall, different variables affected fire incidences in March and April for both the temporal models. Precipitation had a significant negative effect on fire incidences in both March and April, but temperature had a positive effect only in March. Similarly, the fine scale temporal model showed that while ecological (litter load, NPP) and anthropogenic (distance to villages and roads) variables influenced fire incidences in March, altitude and village area surrounding the forest affected fires in April. Litter input, distance to nearest villages, and village area had a non-linear relationship with fire incidences indicating a few inconsistencies with the global patterns of fire with human activity. We show that the Sal dominated forests and terai grasslands at low altitudes (200 m), falling within a zone of 2.5–3 km from villages and with good road connectivity are more prone to fire. The fine-scale fire prediction map of VTR will be helpful to the Tiger Reserve management in developing appropriate strategies for the fire prone areas. [ABSTRACT FROM AUTHOR]

Abstract: The experience of constructing a geoinformational system (GIS) of the Svetlyi Yar irrigation system (Volgograd oblast) for monitoring the status of irrigated lands is considered. The GIS was based on comprehensive information that included remote sensing materials. Analyzing and processing these materials showed that the economic pattern in the area had changed in recent years, the area of irrigated land had decreased significantly, and the irrigation volume had decreased, bringing about a drop in the groundwater level. The structure of crops had also changed, and irrigated soils had entered the desalinization and calcareous invasion stage after the salinization stage of the 1990s. [ABSTRACT FROM AUTHOR]

Smoke alarms have been shown to develop sonically-deposited regions of acoustically agglomerated soot particles when they sound in smoke-filled air. These sonic depositions can be examined forensically post-fire to determine if the smoke alarm sounded during the incident. However, it is not clear how these sonic depositions are affected by common firefighting and post-fire actions. To determine the effects of post-fire forensic smoke alarm testing and environmental conditions on the persistence of the existing sonic deposition of soot on the horns of a smoke alarm, sixty (60) smoke alarms were subjected to smoke from fires of several different fuel types and common post-fire conditions or actions. Initially, each alarm was exposed to smoke in a small-scale experimental fire to develop sonic deposition around the horn. The fuel types for the fires were smoldering wood, flaming toluene-heptane, smoldering polyurethane foam, flaming polyurethane foam, and a combination of smoldering and flaming polyurethane foam. The alarms were then subjected to four common post-fire actions: pressing the test button, exposure to synthetic canned smoke, exposure to standing water, and exposure to running water. Each detector was visually inspected before and after the post-fire action. Results varied from no soot removed to almost all soot removed depending on the fuel type and post-fire test. An objective evaluation system was used to rank the degree to which soot was removed from the alarm horns: 0 (no soot removed), 1 (some soot removed), and 2 (all soot removed) based on visual inspection. The smoldering wood and smoldering polyurethane foam fires left behind a sticky resin that was essentially unaffected by any of the post-tests. The flaming foam and flaming toluene-heptane fires left powdery soot on the horn which could be easily wiped off. This soot was almost completely washed off by running water (1.067 average degree of removal) while the canned smoke and standing water post-tests removed a significant portion of the soot (0.533 and 1.000 average degrees of removal, respectively), which could lead an investigator to an errant sounding determination. Pressing the test-button appeared to make minimal impact on the amount of soot around the alarms horns regardless of the fuel type (0.067 average degree of removal). [ABSTRACT FROM AUTHOR]

Detecting fires at an early stage is crucial for mitigating and extinguishing fires. The increased use of biofuels in Northern Europe has led to an increased number of fires in storage facilities. These fires are often caused by self-heating in the interior of the stored materials and slowly develop to smouldering fires. Consequently, these smouldering fires are usually detected several days or weeks after the initial smouldering fire had started. At this point, measures to extinguish the smouldering fire inside the material are difficult as the fire has grown for a long time and is located inside the material. This makes it difficult to gain a successful effect of any extinguishing agents. This paper presents a pilot study in lab scale suggesting levoglucosan as an early tracer for smouldering fires using 600 g cotton as the biomass source. The advantage of detecting levoglucosan as a fire signature is that it serves as a tracer compound for biomass burning and is produced at temperatures of 200-400°C reducing the risk of false alarms from emissions produced at lower temperatures. In this paper, levoglucosan was detected in aerosols emitted in an early stage from smouldering fires and was analyzed by ultrasonic assisted extraction followed by gas chromatographic separation and mass spectrometric detection. First detection of levoglucosan was made in the first sample, collected after 30 min when the smouldering fire was only a few cubic centimeters of the cotton package. In addition, levoglucosan was found in the solid residues of carbonized cotton after the initial smouldering process had moved through the material. The findings should be regarded as screening results to be used for the development of sensors and technology for smouldering fire detection. [ABSTRACT FROM AUTHOR]

A small device with WiFi multi-sensing element is essential under a digital social century. This study aims to implement the hardware and the power of the algorithm with WiFi technologies. Notably, the multi-sensors have to reinforce around a home area and support to any requirement in the term of digital society. This work focus to care at home security- on going to the fire detection with applying several technologies based on social networks. Firstly, the multisensor calibration used the calibration time, and the self-calibration used the Finite Impulse Response (FIR) filter. Next, the Full-Scale Kalman Filter (FSKF) helps to fill data and estimate the accuracy data. After that, a fire detection mechanism used Fuzzy logic to detect and sent alert messages over the term of IF This Then That (IFTTT) process. The home event changes the WiFi multi-sensor data. They took the data range effect to the fire proportion inside the home. Furthermore, The OFF-Mode is to reduce the power consumption suddenly the WiFi module sends the sensor data to the Cloud. If had a critical point, sent an alert message to social networks. Finally, the WiFi multi-sensor node has to use more than one same detector that it will be high stability and high accuracy. [ABSTRACT FROM AUTHOR]

The remote health monitoring system (RHMS) has become more real-time because the internet plus technology develops quickly. To support real-time health monitoring server, three main aspects, such as collecting, transferring and analyzing the health data, should be integrated into a system. So far, many challenges have been solved and many RHMSs have been applied in our daily lives. It is significant to use RHMS to take good care of clients who live alone, especially monitors the health data of clients in nursing home. This paper introduces a health gateway which automatically collects information of the clients. The information includes the users’ identity, vital signs, body temperature, clients’ real-time location and falling action detection. Moreover, the health gateway transmits information in WIFI relaying mode until the information is received by the background server. If the information is abnormal, the health gateway would send an alarm to the server. The server should call a doctor or caretaker and send accurate navigation based on real-time location in 3D architectural modeling to their mobile devices for finding clients quickly. [ABSTRACT FROM AUTHOR]

Highlights • A moving objects detection algorithm called ALI-TM is proposed. • An adaptive threshold segmentation method is proposed. • A modified Template Matching method is presented. • The proposed ALI-TM obtained better detection ability. Abstract Moving objects detection is an important precursor of stable tracking and recognition, especially for the infrared scenes with dynamic background. In this paper, we proposed an Algorithmic Lateral Inhibition-Template Matching (ALI-TM) algorithm. This algorithm simulates the Lateral Inhibition (LI) mechanism, and achieves a good detection ability in the infrared scenes with dynamic background. First, the input infrared images are segmented into several binary images using an adaptive threshold segmentation. Then, for each binary image, the moving part can be detected by Algorithmic Lateral Inhibition (ALI) method, and the moving part includes moving objects and dynamic background. Finally, the moving objects can be separated from the dynamic background with a modified Template Matching method. Experimental validation of the ALI-TM algorithm is demonstrated on a diverse set of infrared images with dynamic background, the results showed that the proposed ALI-TM algorithm has higher detection precision, higher recognition rate, and better detection ability. Therefore the ALI-TM algorithm is capable to cope with the infrared scenes with dynamic background. [ABSTRACT FROM AUTHOR]

Submerged remote sensor systems (UWSNs) have been appeared as an encouraging innovation to screen and investigate the seas in lieu of customary undersea wireline instruments. In any case, the information get-together of UWSNs is still seriously restricted on the grounds that of the acoustic channel correspondence qualities. One approach to enhance the information gathering in UWSNs is through the plan of steering conventions thinking about the special attributes of the submerged acoustic correspondence and the profoundly unique system topology. In this paper, we propose the GEDAR directing convention for UWSNs. GEDAR is an anycast, geographic and entrepreneurial directing convention that courses information bundles from sensor hubs to different sinks at the ocean's surface. At the point when the hub is in a correspondence void locale, GEDAR changes to the recuperation mode strategy which depends on topology control through the profundity alteration of the void hubs, rather than the conventional methodologies utilizing control messages to find and keep up steering ways along void districts. Recreation comes about demonstrate that GEDAR fundamentally enhances the system execution when contrasted and the gauge arrangements, even in hard and troublesome versatile situations of extremely meagre and exceptionally thick systems and for high system movement loads. [ABSTRACT FROM AUTHOR]

The western margin of the Tibetan Plateau is defined by the NE-striking, sinistral Longmu Co fault system and the NW-striking, dextral Karakoram fault system. The region of convergence of these two systems is remote and politically sensitive, precluding systematic geologic mapping in the field. As a consequence, there is considerable controversy regarding the relationship between these regionally important structural features. Analysis of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data and validation with field mapping provide new insights via the production of a lithologic map. Detailed analysis of the ASTER data shows that the two fault systems do not intersect but instead become parallel. Additionally, the geometry and sense of motion of the two fault systems imply that they are acting as a conjugate fault pair, in conjunction with the normal Angmong fault system, allowing for the extrusion of Tibetan lithosphere toward the east. [ABSTRACT FROM AUTHOR]

Fast tracking algorithm discussed in this paper is applied to star trackers for improving the performance of star identification. Space Robotics Laboratory (SRL) in Tohoku University has developed star trackers for micro-satellites so far. Since it has only a lost-in-space algorithm for star identification, the attitude update rate is limited up to 1 Hz. It was implemented to the Philippines' 50 kg-class micro-satellite "DIWATA-1" released from the International Space Station on April 2016. Although on-orbit evaluation showed good results enough to output attitude autonomously, the performance of continuous attitude determination was worse than expected. Since quite a high access frequency of star catalog is required, timeout of the process for attitude calculation occurs frequently even if update rate is 1 Hz. Insufficient ground evaluation before launch is also one of the causes of operation failure. Tracking algorithm helps to calculate latest attitude faster than conventional methods by feeding back the previous attitude information. This algorithm includes two additional processes. First, future star positions on the image frame can be predicted according to the previous attitude and pre-identified star information. The sensor can find corresponding latest centroids compared to predicted star positions. Second, un-identified stars on the camera field of view (FOV) can be detected by referring to the star neighborhood catalog, which includes the list of some adjacent star IDs against each reference star. PC simulation shows that continuous attitude determination works effectively by keeping low catalog access frequency. The proposed algorithm is implemented to the real hardware. Then, ground evaluation is conducted using star simulator environment and satellite dynamics simulator. The result demonstrates that the processing speed in real situation becomes about 70 times faster compared to the previous method and it is successful to obtain much more stable 1 Hz attitude output. [ABSTRACT FROM AUTHOR]

In this study, the segmented particle swarm optimization (SPSO) algorithm and the concepts of the gradient boosting decision tree algorithm (GBDT) were combined to propose the SPSO adaptive neural network (SANN) method. The purpose of this method is to address the inadequacies of the traditional basis function (BP) and radial basis function (RBF) neural networks when solving problems that involve local optima and overfitting. Experimental results indicated that, overall, the SANN method is accurate in remote-sensing estimations of aquatic vegetation biomass. However, accuracies of estimations were unsatisfactory for certain indicators and sessions when data was taken. The estimations were analyzed using three sets of indicators: (i) root mean square error, average relative error, and total relative error; (ii) correlation coefficient and coefficient of determination, and their scatter plots; and (iii) relative error values and their distributions. The results clearly showed that the SANN method was superior to the BP neural network as well as the stepwise multiple linear regression analysis (SR). However, when the relative errors in biomass estimations by the other two methods were low, the advantages of the SANN method were less pronounced. This was particularly true when the relative errors were <30%, in which case SANN was only marginally better than the other two methods. [ABSTRACT FROM AUTHOR]

In-field estimation of alfalfa ( Medicago sativa L.) yield and nutritive value can inform management decisions to optimize forage quality and production. However, acquisition of timely information at the field scale is limited using traditional measurements such as destructive sampling and assessment of plant maturity. Remote sensing technologies (e.g., measurement of canopy reflectance) have the potential to enable rapid measurements at the field scale. Canopy reflectance (350–2500 nm) and Light Detection and Ranging (LiDAR)-estimated canopy height were measured in conjunction with destructive sampling of alfalfa across a range of maturities at Rosemount, MN in 2014 and 2015. Sets of specific spectral wavebands were determined via stepwise regression to predict alfalfa yield and nutritive value and models were reduced by spectral range to improve utility. Cumulative growing degree units (GDUs) and canopy height were tested as model covariates. An alternative GDU calculation (GDU ALT ) using a temporally graduating base temperature was also tested against the traditional static base temperature. The inclusion of GDU ALT increased prediction accuracy for all response variables by 9–17%. Models using a common set of seven wavebands, combined with GDU ALT , explained 81–90% of the variability in yield, crude protein (CP), neutral detergent fiber (NDF), and NDF digestibility (NDFd; 48-h in-vitro), respectively. This research establishes potential for remote sensing measurements to be integrated with air temperature information to achieve rapid and accurate predictions of alfalfa yield and nutritive value at the field scale for optimized harvest management. [ABSTRACT FROM AUTHOR]

A photonic crystal fiber (PCF)-based surface plasmon resonance (SPR) probe with gold nanowires as the plasmonic material is proposed in this work. The coupling characteristics and sensing properties of the probe are numerically investigated by the finite element method. The probe is designed to detect low refractive indices between 1.27 and 1.36. The maximum spectral sensitivity and amplitude sensitivity are 6 × 103 nm/RIU and 600 RIU−1, respectively, corresponding to a resolution of 2.8 × 10−5 RIU for the overall refractive index range. Our analysis shows that the PCF-SPR probe can be used for lower refractive index detection. [ABSTRACT FROM AUTHOR]

Low frequency passive microwave remote sensing is a proven technology for providing soil moisture estimates, but the coarse resolution of its data restricts the range of applications. Downscaling, otherwise known as disaggregation, has been proposed as the solution to spatially enhance these coarse resolution soil moisture observations, through association with complementary observations, or ancillary information about land surface features at higher spatial resolution. Such information includes solar reflectance, thermal emission, passive microwave emissions at a higher frequency, radar backscatter, soil or surface attributes such as topography and soil properties, and land surface modelling. Each of these ancillary data sources has its own strengths and limitations in terms of, for example, sensitivity to surface soil moisture dynamics and availability. This paper provides an extensive review of the capabilities and opportunities of current soil moisture downscaling approaches which provide a deterministic pattern of soil moisture, together with their strengths and limitations. [ABSTRACT FROM AUTHOR]

Long-term ground LAI measurements from the global networks of sites (e.g. FLUXNET) have emerged as a promising data source to validate remotely sensed global LAI product time-series. However, the spatial scale-mismatch issue between site and satellite observations hampers the use of such invaluable ground measurements in validation practice. Here, we propose an approach (Grading and Upscaling of Ground Measurements, GUGM) that integrates a spatial representativeness grading criterion and a spatial upscaling strategy to resolve this scale-mismatch issue and maximize the utility of time-series of site-based LAI measurements. The performance of GUGM was carefully evaluated by comparing this method to both benchmark LAI and other widely used conventional approaches. The uncertainty of three global LAI products (i.e. MODIS, GLASS and GEOV1) was also assessed based on the LAI time-series validation dataset derived from GUGM. Considering all the evaluation results together, this study suggests that the proposed GUGM approach can significantly reduce the uncertainty from spatial scale mismatch and increase the size of the available validation dataset. In particular, the proposed approach outperformed other widely used approaches in these two respects. Furthermore, GUGM was successfully implemented to validate global LAI products in various ways with advantaging frequent time-series validation dataset. The validation results of the global LAI products show that GLASS has the lowest uncertainty, followed by GEOV1 and MODIS for the overall biome types. However, MODIS provides more consistent uncertainties across different years than GLASS and GEOV1. We believe that GUGM enables us to better understand the structure of LAI product uncertainties and their evolution across seasonal or annual contexts. In turn, this method can provide fundamental information for further LAI algorithm improvements and the broad application of LAI product time-series. [ABSTRACT FROM AUTHOR]

Typical mobile agent networks, such as multi-unmanned aerial vehicle (UAV) systems, are constrained by limited resources: energy, computing power, memory and communication bandwidth. In particular, limited energy affects system performance directly, such as system lifetime. Moreover, it has been demonstrated experimentally in the wireless sensor network literature that the total energy consumption is often dominated by the communication cost, i.e., the computational and the sensing energy are small compared to the communication energy consumption. For this reason, the lifetime of the network can be extended significantly by minimizing the communication distance as well as the amount of communication data, at the expense of increasing computational cost. In this paper, we aim at attaining an optimal tradeoff between the communication and the computational energy. Specifically, we propose a mixed-integer optimization formulation for a multihop hierarchical clustering-based self-organizing UAV network incorporating data aggregation, to obtain an energy-efficient information routing scheme. The proposed framework is tested on two applications, namely target tracking and area mapping. Based on simulation results, our method can significantly save energy compared to a baseline strategy, where there is no data aggregation and clustering scheme. [ABSTRACT FROM AUTHOR]

Shifting cultivation in North Eastern Region of India is practised along the hill slopes by tribes of the region for subsistence living. The present study was carried out to examine the dynamics of shifting cultivation in relation to slope and elevation in Mokokchung, Teunsang and Wokha districts of Nagaland. Temporal Advanced Widefield Sensor data of Resources I were used to derive information on the changes and spatial extent of shifting cultivation areas in these districts. Slope and elevation parameters were derived from Shuttle Radar Thematic Mapper data. The study found a change in current shifting cultivation lands to regenerating shifting cultivation. All three districts showed an increasing trend in regenerating shifting cultivation and decrease in current shifting cultivation lands. A shift of cultivation plots towards higher elevations was noted. Southern slopes are mostly occupied for shifting cultivation by tribal communities in these areas. [ABSTRACT FROM AUTHOR]

In present time due to increase in number of crimes, Parents always worry about their children while sending them out for schools, which no one can't avoid. In a world where dangerous crime in school is a legitimate concern, arming teachers is becoming a realistic strategy for some. At the same time, what else can be done to ensure schools are more secure, visitors can be trusted, and students and teachers can efficiently stop or quickly combat an emergency situation? This paper proposes a solution for their worry using the latest enhancement in technology. Radio Frequency Identification (RFID) is one of key technologies to implement IoT applications to keep an eye on child problems. [ABSTRACT FROM AUTHOR]

Reservoir and lake levels are susceptible to interference from inter-basin water transfer projects that are intended to alleviate increasing serious water resource crises. However, the impact of these transfers on riparian buffers as well as to water security is not clear. This paper considers the world’s largest inter-basin water transfer, the South-to-North Water Diversion (SNWD), as an example for assessing changes in riparian buffer width and biomass in order to indicate the influence of the project on the ecological system of the receiving reservoir. The riparian buffer width is determined by Landsat NDWI. Basing on in situ survey, the method that combing unmanned aircraft vehicle (UAV) and remote sensing (Landsat) was employed to calculate riparian biomass. Results show that water arriving from the SNWD directly decrease the existing buffer width due to the rising water level, and riparian biomass shows a negative response to elevated water levels. Therefore, the transferred water will impair the ecological functions of that buffer and increase risk to the security of the water resource. The vegetation cover in extant riparian buffer zones should be restored if the buffers are to be effective as usual. This study provides information useful to assess riparian impact from artificial inter-basin water projects prior to implementation. [ABSTRACT FROM AUTHOR]

NASA's Earth Polychromatic Imaging Camera (EPIC) onboard NOAA's Deep Space Climate Observatory (DSCOVR) satellite observes the entire sunlit Earth every 65 to 110 min from the Sun-Earth Lagrangian L1 point. This paper presents initial EPIC shortwave spectral observations of the sunlit Earth reflectance and analyses of its diurnal and seasonal variations. The results show that the reflectance depends mostly on (1) the ratio between land and ocean areas exposed to the Sun and (2) cloud spatial and temporal distributions over the sunlit side of Earth. In particular, the paper shows that (a) diurnal variations of the Earth's reflectance are determined mostly by periodic changes in the land-ocean fraction of its the sunlit side; (b) the daily reflectance displays clear seasonal variations that are significant even without including the contributions from snow and ice in the polar regions (which can enhance daily mean reflectances by up to 2 to 6% in winter and up to 1 to 4% in summer); (c) the seasonal variations of the sunlit Earth reflectance are mostly determined by the latitudinal distribution of oceanic clouds. [ABSTRACT FROM AUTHOR]

The accurate mapping of impervious surfaces is of key significance for various urban applications. Usually, traditional methods extract the proportion image of impervious surfaces from remote sensing images; however, the proportion image cannot specify where the impervious surfaces spatially distribute within a pixel. Meanwhile, impervious surfaces often locate urban areas and have a strong correlation with the relatively new big (geo)data points of interest (POIs). This study, therefore, proposed a novel impervious surfaces mapping method (super-resolution mapping of impervious surfaces, SRMIS) by combining a super-resolution mapping technique and POIs to increase the spatial resolution of impervious surfaces in proportion images and determine the accurate spatial location of impervious surfaces within each pixel. SRMIS was evaluated using a 10-m Sentinel-2 image and a 30-m Landsat 8 Operational Land Imager (OLI) image of Nanjing city, China. The experimental results show that SRMIS generated satisfactory impervious surface maps with better-classified image quality and greater accuracy than a traditional hard classifier, the two existing super-resolution mapping (SRM) methods of the subpixel-swapping algorithm, or the method using both pixel-level and subpixel-level spatial dependence. The experimental results show that the overall accuracy increase of SRMIS was from 2.34% to 5.59% compared with the hard classification method and the two SRM methods in the first experiment, while the overall accuracy of SRMIS was 1.34-3.09% greater than that of the compared methods in the second experiment. Hence, this study provides a useful solution to combining SRM techniques and the relatively new big (geo)data (i.e., POIs) to extract impervious surface maps with a higher spatial resolution than that of the input remote sensing images, and thereby supports urban research. [ABSTRACT FROM AUTHOR]

The correlation coefficient (CC) was substantiated as a simple, yet robust statistical tool in the quality assessment of hyperspectral imaging (HSI) data. The sensitivity of the metric was also characterized with respect to artificially-induced errors. The CC was found to be sensitive to spectral shifts and single feature modifications in hyperspectral ground data despite the high, artificially-induced, signal-to-noise ratio (SNR) of 100:1. The study evaluated eight airborne hyperspectral images that varied in acquisition spectrometer, acquisition date and processing methodology. For each image, we identified a uniform ground target region of interest (ROI) that was comprised of a single asphalt road pixel from each column within the sensor field-of-view (FOV). A CC was calculated between the spectra from each of the pixels in the ROI and the data from the center pixel. Potential errors were located by reductions in the CCs below a designated threshold, which was derived from the results of the sensitivity tests. The spectral range associated with each error was established using a windowing technique where the CCs were recalculated after removing the spectral data within various windows. Errors were isolated in the spectral window that removed the previously-identified reductions in the CCs. Finer errors were detected by calculating the CCs across the ROI in the spectral range surrounding various atmospheric absorption features. Despite only observing deviations in the CCs from the 3rd-6th decimal places, non-trivial errors were detected in the imagery. An error was detected within a single band of the shortwave infrared imagery. Errors were also observed throughout the visible-near-infrared imagery, especially in the blue end. With this methodology, it was possible to immediately gauge the spectral consistency of the HSI data across the FOV. Consequently, the effectiveness of various processing methodologies and the spectral consistency of the imaging spectrometers themselves could be studied. Overall, the research highlights the utility of the CC as a simple, low monetary cost, analytical tool for the localization of errors in spectroscopic imaging data. [ABSTRACT FROM AUTHOR]

We report on the recent reactivation of a large rift in the Brunt Ice Shelf, East Antarctica, in December 2012 and the formation of a 50 km long new rift in October 2016. Observations from a suite of ground-based and remote sensing instruments between January 2000 and July 2017 were used to track progress of both rifts in unprecedented detail. Results reveal a steady accelerating trend in their width, in combination with alternating episodes of fast (> 600mday-1) and slow propagation of the rift tip, controlled by the heterogeneous structure of the ice shelf. A numerical ice flow model and a simple propagation algorithm based on the stress distribution in the ice shelf were successfully used to hindcast the observed trajectories and to simulate future rift progression under different assumptions. Results show a high likelihood of ice loss at the McDonald Ice Rumples, the only pinning point of the ice shelf. The nascent iceberg calving and associated reduction in pinning of the Brunt Ice Shelf may provide a uniquely monitored natural experiment of ice shelf variability and provoke a deeper understanding of similar processes elsewhere in Antarctica. [ABSTRACT FROM AUTHOR]

To establish an estimation procedure for reliable catch amount of illegal, unreported and unregulated (IUU) fishing, light-gathering fishing operations in the northwestern Pacific were analyzed based on the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) data provided by the Suomi National Polar Partnership (SNPP) satellite. The estimated fishing activities were compared with the navigation tracks of vessels obtained from the automatic identification system (AIS). As a model case, the fishing activities of Chinese fishing boats using fish aggregation lights outside the Japanese EEZ in the northwestern Pacific were analyzed from mid-June to early-September 2016. Integration analyses of VIIRS DNB data and AIS information provided reliable data for estimating the fishing activities of Chinese fishing boats and suggested the importance of estimating fish carrier ship movements. The total amount of the chub mackerel ( Scomber japonicus ) catch during this period was independently estimated from three angles: 1) the fishing capacity of the fishing boats, 2) the freezing capacity of refrigeration factory ships and 3) the fish hold capacity of the fish carrier ships, based on information obtained from interviews with Chinese fisheries companies. These estimates indicated that the total amount of mackerel catch by Chinese fisheries was more than 80% of the allowable biological catch (ABC) of Japan in this area in 2016. This suggests that Pacific high seas fishing has a significant impact on the future of fish abundance. Our proposed procedure raises the possibility of evaluating the fishing impact of some forms of IUU fisheries independently from conventional statistical reports. [ABSTRACT FROM AUTHOR]

over the last decade, Amman's local authorities have faced challenges in meeting the needs of local populations due to unexpected rapid urbanization growth and the impact this has had on welfare and outdated urban infrastructure. Therefore, it is essential to model spatial dynamic growth to identify the land-use class of urbanized regions expansions to prepare for future urban demands. The present study addresses this need by exploring the application of the remotely- sensed data and cellular automation (CA) based SLEUTH model and its effectiveness in modelling of urban growth in highly Vibrant Metropolitan of Amman. the SLETUH model for Amman was successfully calibrated using the Optimum SLEUTH Metric (OSM) method based on archived remote sensing data from 1985 to 2015. The urban growth characteristics were determined using the urban rules derived from SLEUTH via calibration. SLEUTH's probabilistic image of future urbanized lands of historical trend-based urban growth scenario was employed as dynamic factors for urbanization suitability mapping and landscape pattern analysis of the year 2040. The SLEUTH model predicts that Amman's urban space will expand by 57km2 by 2040 (compared to 2015), resulting in a total area of 473.845 km2. Importantly, this research demonstrates that the issues experienced in Amman's urban locations today will continue into the next two decades unless steps are taken to improve urban growth strategies and increase both environmental and urban action. Findings of the present study cooperate the government's plan to demonstrate Jordan's commitment to modernisation through the creation a new administrative city for national-level civil servants and government ministries. [ABSTRACT FROM AUTHOR]

In object-based image analysis of high-resolution images, the number of features can reach hundreds, so it is necessary to perform feature reduction prior to classification. In this paper, a feature selection method based on the combination of a genetic algorithm (GA) and tabu search (TS) is presented. The proposed GATS method aims to reduce the premature convergence of the GA by the use of TS. A prematurity index is first defined to judge the convergence situation during the search. When premature convergence does take place, an improved mutation operator is executed, in which TS is performed on individuals with higher fitness values. As for the other individuals with lower fitness values, mutation with a higher probability is carried out. Experiments using the proposed GATS feature selection method and three other methods, a standard GA, the multistart TS method, and ReliefF, were conducted on WorldView-2 and QuickBird images. The experimental results showed that the proposed method outperforms the other methods in terms of the final classification accuracy. [ABSTRACT FROM AUTHOR]

In-situ measurements using Unmanned Aerial Vehicles (UAVs) and remote sensing observations can independently provide dense vertically-resolved measurements of atmospheric aerosols; information which is highly required in climate models. In both cases, inverting the recorded signals to useful information requires assumptions and constraints, and this can make the comparison of the results difficult. Here we compare, for the first time, vertical profiles of the aerosol mass concentration derived from Light Detection And Ranging (lidar) observations and in-situ measurements using an Optical Particle Counter (OPC) onboard a UAV during moderate and weak Saharan dust episodes. Agreement between the two measurement methods was within experimental uncertainty for the coarse-mode (i.e., particles having radii > 0.5 µm) where the properties of dust particles can be assumed with good accuracy. This result proves that the two techniques can be used interchangeably for determining the vertical profiles of the aerosol concentrations, bringing them a step closer towards their systematic exploitation in climate models. [ABSTRACT FROM AUTHOR]

Place and local milieu have always been important considerations in the study of human behavior. However, place is typically measured with secondary data in aggregate form, obfuscating crucial, hyper-local information on neighborhood ecological conditions contributing to larger social, criminological, and public health processes. Hyper-local information, which is rarely available via traditional neighborhood audits or secondary data, should include information on neighborhood aesthetics (e.g., architecture, trees, public art), physical disorder (e.g., litter, unkempt lots, building decay), pedestrian safety (e.g., lighting, curb cuts), and related street characteristics. When this information is absent, the ability to connect and interpret the underlying effects of place on social problems is severely compromised. Using two neighborhoods in Phoenix, Arizona as case studies, we employ a novel strategy to collect hyper-local ecological information on physical disorder using unmanned aerial systems (UAS). We compare the collected data to more widely available sources and methods, including systematic social observation, as well as the use of satellite and street imagery. Finally, we discuss the operational challenges, constraints and data quality issues that emerge from implementing a UAS-based approach. [ABSTRACT FROM AUTHOR]

Field experiment in South China was undertaken to improve understanding of cloud and precipitation properties. Measurements of the vertical structures of non-precipitating and precipitating clouds were obtained using passive and active remote sensing equipment: a Ka-band cloud radar (CR) system, a C-band frequency modulated continuous wave vertical pointing radar (CVPR), a microwave radiometer and a laser ceilometer (CEIL). CR plays a key role in high-level cloud observation, whereas CVPR is important for observing low- and mid-level clouds and heavy precipitation. CEIL helps us diminish the effects of "clear-sky" in the planetary boundary layer. The experiment took place in Longmen, Guangdong Province, China from May to September of 2016. This study focuses on evaluating the ability of the two radars to deliver consistent observation data and develops an algorithm to merge the CR, CVPR and CEIL data. Cloud echo base, thickness, frequency of observed cloud types and reflectivity vertical distributions are analyzed in the radar data. Comparisons between the collocated data sets show that reflectivity biases between the CR three operating modes are less than 2 dB. The averaged difference between CR and CVPR reflectivity can be reduced with attenuation correction to 3.57 dB from the original 4.82 dB. No systemic biases were observed between velocity data collected in the three CR modes and CVPR. The corrected CR reflectivity and velocity data were then merged with the CVPR data and CEIL data to fill in the gaps during the heavy precipitation periods and reduce the effects of Bragg scattering and fog on cloud observations in the boundary layer. Meanwhile, the merging of velocity data with different Nyquist velocities and resolutions diminishes velocity folding to provide fine-grain information about cloud and precipitation dynamics. The three daily periods in which low-level clouds tended to occur were at sunrise, noon and sunset and large differences in the average reflectivity values were observed. Mid- and high-level clouds tended to occur at 1400 and 1800 BT. Few clouds were found between a height of 3 and 5 km. [ABSTRACT FROM AUTHOR]

In this study, we showed that the multi-angle satellite remote sensing product, MISR L2 Land Surface (MIL2ASLS), which has a scale of 1.1 km, could be suitable for improving land-cover studies. Using seven images from this product, captured by the multi-angle imaging spectroradiometer sensor (MISR), we explored the values reached by the three parameters (ρ0, Θ, and k) of the Rahman-Pinty-Verstraete model, which was modified by Martonchick (MRPV). Thereafter, we compared the values and behaviors shown in seven Co-ordination of Information on the Environment (CORINE) land cover categories, in the red and near infrared (NIR) bands, over the seven MISR orbits captured in 2006 for Mainland Spain. Furthermore, we used Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) ancillary data and the illumination angles from the same pixels, which made up the images. These ancillary data were also provided by the MISR products. An inferential statistic test was performed to evaluate the relationship between each parameter-band combination, and the land cover in every MISR orbit used. The results suggested that the ρ0 parameters of this product seemed to be the most related to photosynthetic activity, and it should be comparable with the widely-used NDVI. On the other hand, the k and Θ parameter values were not related, or at least not entirely related, to the phenology of land coverage. These seemed to be more influenced by the anisotropy behavior of the studied land cover pixels. Additionally, we observed, by constructing analysis of variance, how the mean of each MRPV parameter-band differed statistically (p < 0.01) by land covers and orbits. This study suggested that the MISR MRPV model parameter data product has great potential to be used to improve land cover applications. [ABSTRACT FROM AUTHOR]

The Colour and Stereo Surface Imaging System (CaSSIS) is the main imaging system onboard the European Space Agency's ExoMars Trace Gas Orbiter (TGO) which was launched on 14 March 2016. CaSSIS is intended to acquire moderately high resolution (4.6 m/pixel) targeted images of Mars at a rate of 10-20 images per day from a roughly circular orbit 400 km above the surface. Each image can be acquired in up to four colours and stereo capability is foreseen by the use of a novel rotation mechanism. A typical product from one image acquisition will be a $9.5~\mbox{km} \times {\sim}45~\mbox{km}$ swath in full colour and stereo in one over-flight of the target thereby reducing atmospheric influences inherent in stereo and colour products from previous high resolution imagers. This paper describes the instrument including several novel technical solutions required to achieve the scientific requirements. [ABSTRACT FROM AUTHOR]

Networked observational devices have proliferated in recent years, contributing to voluminous data streams from a variety of sources and problem domains. These streams often have a spatiotemporal component and include multidimensional features of interest. Processing such data in an offline fashion using batch systems or data warehouses is costly from both a storage and computational standpoint, and in many situations the insights derived from the data streams are useful only if they are timely. In this study, we propose Synopsis, an online, distributed sketch that is constructed from voluminous spatiotemporal data streams. The sketch summarizes feature values and inter-feature relationships in memory to facilitate real-time query evaluations and to serve as input to computations expressed using analytical engines. As the data streams evolve, Synopsis performs targeted dynamic scaling to ensure high accuracy and effective resource utilization. We evaluate our system in the context of two real-world spatiotemporal datasets and demonstrate its efficacy in both scalability and query evaluations. [ABSTRACT FROM PUBLISHER]

This study focused on the patterns of urban sprawl in China's major cities since1978. Information about the major urban built-up areas in China was extracted from the remote sensing images from multiple sources, such as the QuickBird, SPOT and TM, using the soil-vegetation-adjusted building index (SVBI). The trend of urban sprawl was analyzed by measuring the rate of urban expansion and growth rate of urban area. Then qualitative methods were employed to classify the spatial patterns of these cities and discuss the transformation mechanisms of these patterns. The results show that the rates of expansion and area growth in China's major cities were stable between 1984 and 1994 and then sharply increased between 1994 and 2004, followed by a slow-down during 2004-2014. Natural setting was found to be the most essential and limiting factor in a city's morphology. The qualitative analysis suggests that the sprawl patterns of China's major cities were categorized into four groups: circular, leapfrog, interactive and belt-like patterns. Besides, traffic can guide the direction of urban sprawl. It was considered an important factor in urban sprawl and a determining factor in the transformation of urban morphology. Technological, socio-economic development was proved to have played a key role in the development of urban morphology and have acted as the greatest power for cities to withstand the impact of natural setting on their morphology. Moreover, city-industry integration and administrative divisions have also affected the development of spatial patterns of these cities. They have altered the internal links between different parts of a city and the driving forces behind urban development, facilitating formation of new spatial patterns. The findings about urban morphology and its transformation mechanisms can provide a scientific basis for cities to efficiently raise their management levels and formulate more rational plans for urban-space use. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop a novel algorithm for a single-axis maximum power generation sun tracker in order to identify the optimal stopping angle for generating the maximum amount of daily electric energy. First, the photovoltaic modules of the single-axis maximum power generation sun tracker are automatically rotated from 50° east to 50° west. During the rotation, the instantaneous power generated at different angles is recorded and compared, meaning that the optimal angle for generating the maximum power can be determined. Once the rotation (detection) is completed, the photovoltaic modules are then rotated to the resulting angle for generating the maximum power. The photovoltaic module is rotated once per hour in an attempt to detect the maximum irradiation and overcome the impact of environmental effects such as shading from cloud cover, other photovoltaic modules and surrounding buildings. Furthermore, the detection range is halved so as to reduce the energy consumption from the rotation operations and to improve the reliability of the sun tracker. The results indicate that electric energy production is increased by 3.4% in spring and autumn, 5.4% in summer, and 8.3% in winter, compared with that of the same sun tracker with three fixed angles of 50° east in the morning, 0° at noon and 50° west in the afternoon. [ABSTRACT FROM AUTHOR]

Remote sensing has been pivotal to our understanding of freshwater fisheries, and we review this rapidly changing field with a focus on satellite and airborne applications. Historical applications emphasized spatial variation in the environment (e.g., watershed land use and in situ primary productivity), but improved access to imagery archives facilitates better change detection over time. New sensor platforms and technology now yield imagery with higher spatial, temporal, and spectral resolutions than ever before, which has accelerated development of remote sensing products that more accurately characterize aquatic environments. Free access to imagery archives, cloud computing, and availability of derived products linked to national hydrography databases are all removing historical barriers to its use by fisheries professionals. These advances in remote sensing have allowed new questions to be answered at finer spatial resolutions across broader landscapes and longer time frames, providing a new big-picture perspective to freshwater fisheries conservation and management. [ABSTRACT FROM AUTHOR]

Prediction of location and amount of slip for distributed faulting associated to strong earthquakes is a recently explored issue with major implications in terms of hazard assessment. Currently, the best practice involves the applications of Probabilistic Fault Displacement Hazard Analysis (PFDHA) whose results fit sufficiently well real data in the near-range of the primary fault but show considerable inaccuracies in the far-range. We believe that this inaccuracy descends from the biased earthquake databases used for regression analysis, whose data, relative to old earthquakes, were largely collected only by field surveys focused close to the primary fault (i.e., near-range). Remotely-sensed data (i.e., Interferometric Radar Imaging – InSAR) offer the opportunity to precisely measure the surface deformation induced by strong earthquakes and thus to explore its possible relation with distributed faulting. We analyze the L'Aquila earthquake case study (29th April, 2009, Mw 6.3) and explore the correlation between location and slip on distributed faulting and InSAR-derived deformation field. We find a significant correlation between occurrence of distributed faulting and profile curvature of the dislocation field, in spite of the distance from the primary fault. Moreover, distributed faults tend to occur within the area deformed by the earthquake, as imaged by InSAR data and whose extent is directly proportional to the earthquake magnitude (Mw), according to a dataset of 30 recent earthquakes. We then propose that these observations have to be incorporated into the present PFDHA practice as limit boundaries to possible scenarios of probabilistic analysis and that an integrated use of field-based and remotely data collection have to be implemented, following strong earthquakes. [ABSTRACT FROM AUTHOR]

The recent advancements in the fields of electronics, information and communication technologies have paved a pathway towards a world-wide future network of connected smart devices. Researchers from industry and academia are taking even more interests in the realization of such an infrastructure where a seamless interaction of sensing and actuating devices will take place in order to provide valuable services to the human kind and other systems. So far the major focus of research is towards the connectivity, management and control of sensing devices and no major attention has been given to the control of actuating devices in such an environment. This paper presents a generic process model for actuating device control service in future networks. A prototype implementation of the proposed model based on the presented platform has been described along with the performance analysis of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper, we describe the development of linear optical fiber array based remote position sensor that uses the oblique laser triangulation technique. The developed sensor is compliant for non-contact and precise measurement of the position of the desired target. The configuration of sensor head comprises of a diode laser producing a laser beam striking the target at an oblique angle to the surface, an aspheric lens for capturing the scattered light and transmitting it to the linear optical fiber array and finally to the CCD camera. Contrary to conventional methods, the proposed technique has the potential to be applied in hazardous environments containing radioactivity or electromagnetic noise since the camera is placed remotely from the sensor head. The position change information is inferred from the center of laser spot at CCD camera using the Fourier series method. A position resolution of 1 mm (or ∼1%) at a standoff distance of ∼100 mm between target and light collecting lens was achieved for our selected linear fiber array. Effect of roughness on the accuracy of the measurement was tested by using standard targets of known roughness values. It is seen that the uncertainty in the measured position due to variation in roughness was less than the resolution of our setup for all targets. The error in measurement was high when the roughness of target is comparable to the laser wavelength used due to the diffraction effects at the target surface that ultimately alters the intensity pattern on the optical fiber array. [ABSTRACT FROM AUTHOR]