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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 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]

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]

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]

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]

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]

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 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]

The main goal of this study was to evaluate four major remote sensing soil moisture (SM) products over the state of Texas. These remote sensing products are: (i) the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) (2002-September 2011); (ii) the Soil Moisture Ocean Salinity system (SMOS, 2010-present); (iii) AMSR2 (2012-present); and (iv) the Soil Moisture Active Passive system (SMAP, 2015-present). The quality of the generated SM data is influenced by the accuracy and precision of the sensors and the retrieval algorithms used in processing raw data. Therefore, it is important to evaluate the quality of these satellite SM products using in situ measurements and/or by inter-comparing their data during overlapping periods. In this study, these two approaches were used where we compared each satellite SM product to in situ soil moisture measurements and we also conducted an inter-comparison of the four satellite SM products at 15 different locations in Texas over six major land cover types (cropland, shrub, grassland, forest, pasture and developed) and eight climate zones along with in situ SM data from 15 Mesonet, USCRN and USDA-NRCS Scan stations. Results show that SM data from SMAP had the best correlation coefficients range from 0.37 to 0.92 with in situ measurements among the four tested satellite surface SM products. On the other hand, SM data from SMOS, AMSR2 and AMSR-E had moderate to low correlation coefficients ranges with in situ data, respectively, from 0.24-0.78, 0.07-0.62 and 0.05-0.52. During the overlapping periods, average root mean square errors (RMSEs) of the correlations between in situ and each satellite data were 0.13 (AMSR-E) and 0.13 (SMOS) cm³/cm³ (2010-2011), 0.16 (AMSR2) and 0.14 (SMOS) cm³/cm³ (2012-2016) and 0.13, 0.16, 0.14 (SMAP, AMSR2, SMOS) cm³/cm³ (2015-2016), respectively. Despite the coarser spatial resolution of all four satellite products (25-36 km), their SM measurements are considered reasonable and can be effectively used for different applications, e.g., flood forecasting, and drought prediction; however, further evaluation of each satellite product is recommended prior to its use in practical applications. [ABSTRACT FROM AUTHOR]

The focus of this paper is on participatory sensing in which every participant carrying smartphone senses its environment and shares it with server. Most of the applications require location information to perform sensing activity. But, GPS drains considerable amount of energy if used for localization. There exists a centralized localization approach to detect a set of devices for the role of broadcaster which must turn on GPS. Its neighbouring devices rely on them to calculate their position. In this paper, we provide a novel distributed solution for finding roles for the participating mobile devices. It is important in real scenario to alleviate server from the burden of assigning roles. Our proposed approach does not make any assumption about the presence of infrastructure or device capabilities, other than the availability of WiFi and the ability to know devices within the WiFi range. To the best of our knowledge, this is the first protocol for distributed localization in participatory sensing system. Simulation results using real dataset demonstrate that our algorithm is capable of assigning role in 70-85% less time compared to the centralized algorithm but consumes 12-15% more energy as it does not find the optimal set of broadcasters which requires global information. [ABSTRACT FROM AUTHOR]

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1–2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions. [ABSTRACT FROM AUTHOR]

Spectral radiance measurements collected in nadir and sideward viewing directions by two airborne passive solar remote sensing instruments, the Spectral Modular Airborne Radiation measurement sysTem (SMART) and the Differential Optical Absorption Spectrometer (mini-DOAS), are used to compare the remote sensing results of cirrus optical thickness τ. The comparison is based on a sensitivity study using radiative transfer simulations (RTS) and on data obtained during three airborne field campaigns: the North Atlantic Rainfall VALidation (NARVAL) mission, the Mid-Latitude Cirrus Experiment (ML-CIRRUS) and the Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems (ACRIDICON) campaign. Radiative transfer simulations are used to quantify the sensitivity of measured upward radiance I with respect to τ, ice crystal effective radius reff, viewing angle of the sensor θV, spectral surface albedo α, and ice crystal shape. From the calculations it is concluded that sideward viewing measurements are generally better suited than radiance data from the nadir direction to retrieve τ of optically thin cirrus, especially at wavelengths larger than λ = 900 nm. Using sideward instead of nadir-directed spectral radiance measurements significantly improves the sensitivity and accuracy in retrieving τ, in particular for optically thin cirrus of τ ≤ 2. The comparison of retrievals of τ based on nadir and sideward viewing radiance measurements from SMART, mini-DOAS and independent estimates of τ from an additional active remote sensing instrument, the Water Vapor Lidar Experiment in Space (WALES), shows general agreement within the range of measurement uncertainties. For the selected example a mean τ of 0.54 ± 0.2 is derived from SMART, and 0.49 ± 0.2 by mini-DOAS nadir channels, while WALES obtained a mean value of τ = 0.32 ± 0.02 at 532 nm wavelength, respectively. The mean of τ derived from the sideward viewing mini-DOAS channels is 0.26 ± 0.2. For the few simultaneous measurements, the mini-DOAS sideward channel measurements systematically underestimate (-17.6%) the nadir observations from SMART and mini-DOAS. The agreement between mini-DOAS sideward viewing channels and WALES is better, showing the advantage of using sideward viewing measurements for cloud remote sensing for τ ≤ 1. Therefore, we suggest sideward viewing measurements for retrievals of τ of thin cirrus because of the significantly enhanced capability of sideward viewing compared to nadir measurements. [ABSTRACT FROM AUTHOR]

Illumination of nocturnal environments is increasing steadily worldwide. While there are some benefits for mankind, light at night affects animals, plants, and human health by blurring the natural distinction between day and night. International regulations exist to protect the environment for the maintenance of human health but nocturnal darkness is not considered. In Germany, cities and communities labeled as Climatic Health Resorts provide for high standards in air quality. However, their degree of nocturnal darkness is unexplored so far. In our study, we examined the degree of nocturnal darkness in German Climatic Health Resorts by two datasets based on georeferenced remote sensing data. The majority of Climatic Health Resorts (93.1 %) are able to offer a relative respite (≥ 20 mag/arcsec) from a degraded nocturnal environment, while only 3.4 % are able to offer a dark, if by no means pristine, night environment (≥ 21 mag/arcsec). Climatic Health Resorts emit less light as well as are less affected by night sky brightness compared to the average of non-classified communities. In combination with daytime requirements, the resorts provide conditions for a more distinct day-and-night-cycle than non-classified communities. [ABSTRACT FROM AUTHOR]

Snow contributes to regional and global water budgets, and is of critical importance to water resources management and our society. Along with advancement in remote sensing tools and techniques to retrieve snowfall, verification and refinement of these estimates need to be performed using ground-validation datasets. A comprehensive evaluation of the Multi-Radar/Multi-Sensor (MRMS) snowfall products and Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) precipitation products is conducted using the Snow Telemetry (SNOTEL) daily precipitation and Snow Water Equivalent (SWE) datasets. Severe underestimations are found in both radar and satellite products. Comparisons are conducted as functions of air temperature, snowfall intensity, and radar beam height, in hopes of resolving the discrepancies between measurements by remote sensing and gauge, and finally developing better snowfall retrieval algorithms in the future. [ABSTRACT FROM AUTHOR]

The vocalization source level distributions and pulse compression gains are estimated for four distinct baleen whale species in the Gulf of Maine: fin, sei, minke and an unidentified baleen whale species. The vocalizations were received on a large-aperture densely-sampled coherent hydrophone array system useful for monitoring marine mammals over instantaneous wide areas via the passive ocean acoustic waveguide remote sensing technique. For each baleen whale species, between 125 and over 1400 measured vocalizations with significantly high Signal-to-Noise Ratios (SNR > 10 dB) after coherent beamforming and localized with high accuracies (<10% localization errors) over ranges spanning roughly 1 km-30 km are included in the analysis. The whale vocalization received pressure levels are corrected for broadband transmission losses modeled using a calibrated parabolic equation-based acoustic propagation model for a random range-dependent ocean waveguide. The whale vocalization source level distributions are characterized by the following means and standard deviations, in units of dB re 1 μ Pa at 1 m: 181.9 ± 5.2 for fin whale 20-Hz pulses, 173.5 ± 3.2 for sei whale downsweep chirps, 177.7 ± 5.4 for minke whale pulse trains and 169.6 ± 3.5 for the unidentified baleen whale species downsweep calls. The broadband vocalization equivalent pulse-compression gains are found to be 2.5 ± 1.1 for fin whale 20-Hz pulses, 24 ± 10 for the unidentified baleen whale species downsweep calls and 69 ± 23 for sei whale downsweep chirps. These pulse compression gains are found to be roughly proportional to the inter-pulse intervals of the vocalizations, which are 11 ± 5 s for fin whale 20-Hz pulses, 29 ± 18 for the unidentified baleen whale species downsweep calls and 52 ± 33 for sei whale downsweep chirps. The source level distributions and pulse compression gains are essential for determining signal-to-noise ratios and hence detection regions for baleen whale vocalizations received passively on underwater acoustic sensing systems, as well as for assessing communication ranges in baleen whales. [ABSTRACT FROM AUTHOR]

Nowadays, vision-based techniques for automatic early smoke detection in the outdoor scenes are in a hot topic of computer vision. The basic set of features includes the traditional features describing the spatial ones, such as color, shape, transparency, energy, and fractal property, and the temporal ones, such as frame difference estimator, motion estimator, and flicker on boundaries. The main problem of the early smoke detection is to obtain the low values of the clustering errors. Our contribution deals with a reasonable clustering of the smoke/non-smoke regions based on the Boosted Random Forests (BRFs). The BRFs provide better clustering results in comparison with the traditional clustering techniques, as well as the ordinary random forests. Forty test video sequences with and without smoke were analyzed during experiments. The true recognition results of a smoke detection achieved 97.8% that is better on 3–4% of the results obtaining by the Support Vector Machine (SVM) application. False reject rate and false acceptance rate values were significantly decreased till 3.68% and 3.24% in average, respectively. [ABSTRACT FROM AUTHOR]

The dynamic provisioning of hybrid sensing services that integrates both WSN and MPS is a promising, yet challenging concept. It does not only widen the spatial sensing coverage, but it also enables different types of sensing nodes to collaboratively perform sensing tasks and complement each other. Furthermore, it allows for the provisioning of a new category of services that was not possible to implement in pure WSN or MPS networks. Offering a hybrid sensing platform as a service results in several benefits including, but no limited to, efficient sharing and dynamic management of sensing nodes, diversification and reuse of sensing services, as well as combination of many sensing paradigms to enable data to be collected from different sources. However, many challenges need to be resolved before such architecture can be feasible. Currently, the deployment of sensing applications and services is a costly and complex process, which also lacks automation. This paper motivates the need for hybrid sensing, sketches an early architecture, and identifies the research issues with few hints on how to solve them. We argue that a sensing platform that reuses the virtualization and cloud computing concepts will help in addressing many of these challenges, and overcome the limitations of today's deployment practices. [ABSTRACT FROM AUTHOR]

Altitude-resolved aerosol detection in the upper troposphere and lower stratosphere (UTLS) is a challenging task for remote sensing instruments. Infrared limb emission measurements provide vertically resolved global measurements at day- and nighttime in the UTLS. For high-spectralresolution infrared limb instruments we present here a new method to detect aerosol and separate between ice and nonice particles. The method is based on an improved aerosol-cloud index that identifies infrared limb emission spectra affected by non-ice aerosol or ice clouds. For the discrimination between non-ice aerosol and ice clouds we employed brightness temperature difference correlations. The discrimination thresholds for this method were derived from radiative transfer simulations (including scattering) and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS)/Envisat measurements obtained in 2011. We demonstrate the value of this approach for observations of volcanic ash and sulfate aerosol originating from the Grímsvötn (Iceland, 64° N), Puyehue-Cordón Caulle (Chile, 40° S), and Nabro (Eritrea, 13° N) eruptions in May and June 2011 by comparing the MIPAS volcanic aerosol detections with Atmospheric Infrared Sounder (AIRS) volcanic ash and SO2 measurements. [ABSTRACT FROM AUTHOR]

Active remote-sensing instruments, such as ceilometers, have been shown to be potentially useful for the investigation of the behavior of the atmospheric mixing layer height (MLH). For the first time ever, high-resolution measurements of backscatter intensity, taken from two CL31 ceilometers situated inland and onshore of Israel, have enabled evaluation of the mean diurnal cycle of the MLH in the eastern Mediterranean region. Although the Israeli summer synoptic conditions are considered to be quite stable, results for the summer season (July-August 2014) showed the inland MLH to be about 200m higher than the MLH at the onshore site, situated only 7.5 km away. The prevailing influence of the sea breeze front (SBF), as it progresses inland, is presented by the ceilometer plots. Complementing results were found between the radiosonde profiles and the adjacent ceilometer at the inland site of Beit Dagan. In contrast to the expected regularity of clear skies during the Israeli summer, the ceilometers revealed significant cloud cover throughout the day, with higher presence onshore. Assessment of cloud thickness in further research would serve to improve the evaluation of the MLH evolution. [ABSTRACT FROM AUTHOR]

The abundance of NO2 in the boundary layer relates to air quality and pollution sources monitoring. Observing the spatio-temporal distribution of NO2 above well-delimited (flue gas stacks, volcanoes, ships) or more extended sources (cities) allows for several applications: monitoring emission fluxes or studying the plume dynamic chemistry and its transport. So far, most attempts to map the NO2 field from the ground have been made with visible-light scanning spectrometers. Benefiting from a high retrieval accuracy, they only achieve a relatively low temporal resolution that hampers the detection of dynamic features. We present a new type of passive remote sensing instrument aiming at the measurement of the 2-D distributions of NO2 slant column densities (SCD) with a high spatio-temporal resolution. The measurement principle has strong similarities with the popular filter-based SO2 camera as it relies on spectral images taken at wavelengths where the molecule absorption cross-section is different. Contrary to the SO2 camera, the spectral selection is performed by an acousto-optical tunable filter (AOTF) capable of resolving the target molecule's spectral features. The NO2 camera capabilities are demonstrated by imaging the NO2 abundance in the plume of a coal-fired power plant. During this experiment, the 2-D distribution of the NO2 SCD was retrieved with a temporal resolution of 3 minutes and a spatial sampling of 50 cm (over a 250 x 250 m² area). The detection limit was close to 5 x 1016 molecules cm-2, with a maximum detected SCD of 4 x 1017 molecules cm-2. Illustrating the added-value of the NO2 camera measurements, the data reveal the dynamics of the NO to NO2 conversion in the early plume with an unprecedent resolution: from its release in the air, and for 100 m upwards, the observed NO2 plume concentration increased at a rate of 0.75-1.25 g s-1. In joint campaigns with SO2 cameras, the NO2 camera could also help in removing the bias introduced by the NO2 interference in the SO2 measurements. [ABSTRACT FROM AUTHOR]

Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field. It is built on agricultural mechanization and state-of-the-art technologies of geographical information systems (GIS), global positioning systems (GPS) and remote sensing, and is used to monitor soil, crop growth, weed infestation, insects, diseases, and water status in farm fields to provide data and information to guide agricultural management practices. Precision agriculture began with mapping of crop fields at different scales to support agricultural planning and decision making. With the development of variable-rate technology, precision agriculture focuses more on tactical actions in controlling variable-rate seeding, fertilizer and pesticide application, and irrigation in real-time or within the crop season instead of mapping a field in one crop season to make decisions for the next crop season. With the development of aerial variable-rate systems, low-altitude airborne systems can provide high-resolution data for prescription variable-rate operations. Airborne systems for multispectral imaging using a number of imaging sensors (cameras) were developed. Unmanned aerial vehicles (UAVs) provide a unique platform for remote sensing of crop fields at slow speeds and low-altitudes, and they are efficient and more flexible than manned agricultural airplanes, which often cannot provide images at both low altitude and low speed for capture of high-quality images. UAVs are also more universal in their applicability than agricultural aircraft since the latter are used only in specific regions. This study presents the low-altitude remote sensing systems developed for detection of crop stress caused by multiple factors. UAVs, as a special platform, were discussed for crop sensing based on the researchers' studies. [ABSTRACT FROM AUTHOR]

Remote sensors such as Doppler radars are providing novel insights into the migrations of diverse animal taxa, but limits in scope and sensitivity can hamper the utility of these tools. For example, studies investigating whether songbirds compensate effectively for wind displacement during nocturnal migration have been challenged by the need to assess behavior on a large scale. In addition, these studies typically overlook the potential role low-altitude diurnal flights play in dealing with unfavorable winds. In such cases, a combination of approaches—new and traditional—may be necessary to understand behavior more completely. Here, we unite ground-based visual observations with a new radar analysis method to investigate how songbirds deal with crosswinds over the northeast United States. We find that nocturnally migrating birds experienced significant wind drift, even though they often flew at 90° or more to the wind direction. Significantly, more birds undertook reoriented diurnal flights after nocturnal wind drift, and wind influence, nocturnal migration intensity, and time of season together explained the majority of variation in counts of these "morning flights." This study shows that bird behavior during migration can be strongly shaped by the danger of wind drift and that some songbird species respond to drift with reoriented diurnal migratory flights. Knowledge of birds' interactions with wind is essential for successfully modeling migratory behavior and assessing the risks associated with changing habitats and meteorological patterns. Furthermore, an understanding of the degree to which drift defines migratory behaviors may have value across animal taxa. [ABSTRACT FROM AUTHOR]

The launch of the MinION Access Program has caused much activity within the scientific community. MinION represents a keenly anticipated, novel addition to the current melange of commercial sequencers. Driven by the nanopore sequencing mechanism that requires minimal sample manipulation, the device is capable of generating long sequence reads in sizes (up to or exceeding 50 kb) that surpass those of all other platforms. One notable advantage of this feature is that long-range haplotypes can be more accurately resolved; such advantage is particularly pertinent to the genotyping of complex loci such as genes encoding the human leukocyte antigens, which are pivotal determinants of drug hypersensitivity. With this timely, albeit brief, review, we set out to examine the applications on which MinION has been tested thus far, the bioinformatics workflow tailored to the unique characteristics of its extended sequence reads, the device's potential utility in the detection of genetic markers for drug hypersensitivity, and how it may eventually evolve to become fit for diagnostic purposes in the clinical setting. [ABSTRACT FROM AUTHOR]

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003-2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW's) of 3, 5, 7, 9, . . . , 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach. [ABSTRACT FROM AUTHOR]

An all-spherical catadioptic telescope with the angular field of view of several tens of degrees in diameter and spherical focal surface is proposed for the monitoring of large sky areas. We provide a few examples of such a system with the apertures up to 800 mm and the field of view 30° and 40° in diameter. The curvature of the focal surface is repaid by high performance of the telescope. In particular, the diameter of a circle that includes 80% of energy in the polychromatic image of a star is in the range 1.″4-1.″9 across the field of 30° size and 2.″2-2.″9 for the field of 40° size. Some ways of working with curved focal surfaces are discussed. (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

Efficiency increase of resources through automation of agriculture requires more information about the production process, as well as process and machinery status. Sensors are necessary for monitoring the status and condition of production by recognizing the surrounding structures such as objects, field structures, natural or artificial markers, and obstacles. Currently, three dimensional (3-D) sensors are economically affordable and technologically advanced to a great extent, so a breakthrough is already possible if enough research projects are commercialized. The aim of this review paper is to investigate the state-of-the-art of 3-D vision systems in agriculture, and the role and value that only 3-D data can have to provide information about environmental structures based on the recent progress in optical 3-D sensors. The structure of this research consists of an overview of the different optical 3-D vision techniques, based on the basic principles. Afterwards, their application in agriculture are reviewed. The main focus lays on vehicle navigation, and crop and animal husbandry. The depth dimension brought by 3-D sensors provides key information that greatly facilitates the implementation of automation and robotics in agriculture. [ABSTRACT FROM AUTHOR]

The paper addresses problems related to classification of images obtained by various types of remote sensing devices. Development and use of Bayes type land cover classifiers based on multidimensional Gaussian, Dirichlet and gamma distributions is analysed and compared on the basis of sample data from RGB and hyperspectral thermal sensing devices with unequal spatial resolution. Approaches to data fusion for design of the combined classifiers are presented including the cases where different families of multidimensional distributions are used to model the sensor data and classifiers are designed using combinations of their probability density functions. The best classification results are obtained when the fusion of data from both images is used together with classification based on all three considered distributions combined together. [ABSTRACT FROM AUTHOR]

This work demonstrates the use of genetic algorithms in optimized view planning for 3D reconstruction applications using small unmanned aerial vehicles (UAVs). The quality of UAV site models is currently highly dependent on manual pilot operations or grid-based automation solutions. When applied to 3D structures, these approaches can result in gaps in the total coverage or inconsistency in final model resolution. Genetic algorithms can effectively explore the search space to locate image positions that produce high quality models in terms of coverage and accuracy. A fitness function is defined, and optimization parameters are selected through semi-exhaustive search. A novel simulation environment for evaluating view plans is demonstrated using terrain generation software. The view planning algorithm is tested in two separate simulation cases: a water drainage structure and a reservoir levee, as representative samples of infrastructure monitoring. The optimized flight plan is compared against three alternate flight plans in each case. The optimized view plan is found to yield terrain models with up to 43% greater accuracy than a standard grid flight pattern, while maintaining comparable coverage and completeness. [ABSTRACT FROM AUTHOR]

Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi- and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges. [ABSTRACT FROM AUTHOR]

The ground-based microwave sounding radiometers installed at nine weather stations of Korea Meteorological Administration alongside with the wind profilers have been operating for more than 4 years. Here we apply a process to assess the characteristics of the observation data by comparing the measured brightness temperature (Tb) with reference data. For the current study, the reference data are prepared by the radiative transfer simulation with the temperature and humidity profiles from the numerical weather prediction model instead of the conventional radiosonde data. Based on the 3 years of data, from 2010 to 2012, we were able to characterize the effects of the absolute calibration on the quality of the measured Tb. We also showed that when clouds are present the comparison with the model has a high variability due to presence of cloud liquid water therefore making cloudy data not suitable for assessment of the radiometer's performance. Finally we showed that differences between modeled and measured brightness temperatures are unlikely due to a shift in the selection of the center frequency but more likely due to spectroscopy issues in the wings of the 60GHz absorption band. With a proper consideration of data affected by these two effects, it is shown that there is an excellent agreement between the measured and simulated Tb. The regression coefficients are better than 0.97 along with the bias value of better than 1.0K except for the 52.28 GHz channel which shows a rather large bias and variability of -2.6 and 1.8K, respectively. [ABSTRACT FROM AUTHOR]

A wearable sensor system enables continuous and remote health monitoring and is widely considered as the next generation of healthcare technology. The performance, the packet error rate (PER) in particular, of a wearable sensor system may deteriorate due to a number of factors, particularly the interference from the other wearable sensor systems in the vicinity. We systematically evaluate the performance of the wearable sensor system in terms of PER in the presence of such interference in this paper. The factors that affect the performance of the wearable sensor system, such as density, traffic load, and transmission power in a realistic moderate-scale deployment case in hospital are all considered. Simulation results show that with 20% duty cycle, only 68.5% of data transmission can achieve the targeted reliability requirement (PER is less than 0.05) even in the off-peak period in hospital. We then suggest some interference mitigation schemes based on the performance evaluation results in the case study. [ABSTRACT FROM AUTHOR]

The need for better 3D documentation of the built environment has come to the fore in recent years, led primarily by city modelling at the large scale and Building Information Modelling (BIM) at the smaller scale. Automation is seen as desirable as it removes the time-consuming and therefore costly amount of human intervention in the process of model generation. BIM is the focus of this paper as not only is there a commercial need, as will be shown by the number of commercial solutions, but also wide research interest due to the aspiration of automated 3D models from both Geomatics and Computer Science communities. The aim is to go beyond the current labour-intensive tracing of the point cloud to an automated process that produces geometry that is both open and more verifiable. This work investigates what can be achieved today with automation through both literature review and by proposing a novel point cloud processing process. We present an automated workflow for the generation of BIM data from 3D point clouds. We also present quality indicators for reconstructed geometry elements and a framework in which to assess the quality of the reconstructed geometry against a reference. [ABSTRACT FROM AUTHOR]

Due to the spatiotemporal variations of complex optical characteristics, accurately estimating chlorophyll-a (Chl-a) concentrations in inland waters using remote sensing techniques remains challenging. In this study, a weighted algorithm was developed to estimate the Chl-a concentrations based on spectral classification and weighted matching using normalized mutual information (NMI). Based on the NMI algorithm, three water types (Class 1 to Class 3) were identified using the in situ normalized spectral reflectance data collected from Taihu Lake. Class-specific semi-analytic algorithms for the Chl-a concentrations were established based on the GOCI data. Next, weighted factors, which were used to determine the matching probabilities of different water types, were calculated between the GOCI data and each water type using the NMI algorithm. Finally, Chl-a concentrations were estimated using the weighted factors and the class-specific inversion algorithms for the GOCI data. Compared to the non-classification and hard-classification algorithms, the accuracies of the weighted algorithms were higher. The mean absolute error and root mean square error of the NMI weighted algorithm decreased to 22.63% and 9.41 mg/m3, respectively. The results also indicated that the proposed algorithm could reduce discontinuous or jumping effects associated with the hard-classification algorithm. [ABSTRACT FROM AUTHOR]

La Salle and Ebro Observatory have been involved in remote sensing projects in Antarctica for the last 11 years (approximately one solar cycle). The Ebro Observatory has been monitoring and analyzing the geomagnetic and the ionospheric activity in the Antarctic Spanish station Juan Carlos I (ASJI) (62.7°S, 299.6°E) for more than eighteen and ten years, respectively. La Salle has two main goals in the project. The first one is the data transmission and reception from Antarctica to Spain to obtain a historical series of measurements of channel sounding of this 12,760-km ionospheric HF (high frequency) radio link. The second one is the establishment of a stable data low power communication system between the ASJI and Cambrils, Spain (41.0°N, 1.0°E), to transmit the data from the remote sensors located on the island. In this paper, both narrowband and wideband soundings have been carried out to figure out the channel availability performed using a frequency range from 2 to 30 MHz with 0.5 MHz step during the 24 h of the day, encompassing wider channel measurements than previously done, in terms of hours and frequency. This paper presents the results obtained for the austral summer in 2014, using a monopole antenna at the transmitter and an inverted V on the receiver side. These results led us to the final physical layer design for the long haul link, dividing the day into two parts: daytime, with low data throughput design, and nighttime, reaching high data throughput. [ABSTRACT FROM AUTHOR]

Detailed information about coastal inundation is vital to understanding dynamic and populated areas that are impacted by storm surge and flooding. To understand these natural hazard risks, lidar elevation surfaces are frequently used to model inundation in coastal areas. A single-value surface method is sometimes used to inundate areas in lidar elevation surfaces that are below a specified elevation value. However, such an approach does not take into consideration hydrologic connectivity between elevation grids cells resulting in inland areas that should be hydrologically connected to the ocean, but are not. Because inland areas that should drain to the ocean are hydrologically disconnected by raised features in a lidar elevation surface, simply raising the water level to propagate coastal inundation will lead to inundation uncertainties. We took advantage of this problem to identify hydrologically disconnected inland areas to point out that they should be considered for coastal inundation, and that a lidar-based hydrologic surface should be developed with hydrologic connectivity prior to inundation analysis. The process of achieving hydrologic connectivity with hydrologic-enforcement is not new, however, the application of hydrologically-enforced lidar elevation surfaces for improved coastal inundation mapping as approached in this research is innovative. In this article, we propagated a high-resolution lidar elevation surface in coastal Staten Island, New York to demonstrate that inland areas lacking hydrologic connectivity to the ocean could potentially be included in inundation delineations. For inland areas that were hydrologically disconnected, we evaluated if drainage to the ocean was evident, and calculated an area exceeding 11 ha (~0.11 km2) that could be considered in inundation delineations. We also assessed land cover for each inland area to determine the type of physical surfaces that would be potentially impacted if the inland areas were considered as part of a coastal inundation. A visual analysis indicated that developed, medium intensity and palustrine forested wetland land cover types would be impacted for those locations. This article demonstrates that hydrologic connectivity is an important factor to consider when inundating a lidar elevation surface. This information is needed for inundation monitoring and management in sensitive coastal regions. [ABSTRACT FROM AUTHOR]

Accurate estimation of rainfall in mountainous areas is necessary for various water resource-related applications. Though rain gauges accurately measure rainfall, they are rarely found in mountainous regions and satellite rainfall data can be used as an alternative source over these regions. This study evaluated the performance of three high-resolution satellite rainfall products, the Tropical Rainfall Measuring Mission (TRMM 3B42), the Global Satellite Mapping of Precipitation (GSMaP_MVK+), and the Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Networks (PERSIANN) at daily, monthly, and seasonal time scales against rain gauge records over data-scarce parts of Eastern Ethiopia. TRMM 3B42 rain products show relatively better performance at the three time scales, while PERSIANN did much better than GSMaP. At the daily time scale, TRMM correctly detected 88% of the rainfall from the rain gauge. The correlation at the monthly time scale also revealed that the TRMM has captured the observed rainfall better than the other two. For Belg (short rain) and Kiremt (long rain) seasons, the TRMM did better than the others by far. However, during Bega (dry) season, PERSIANN showed a relatively good estimate. At all-time scales, noticing the bias, TRMM tends to overestimate, while PERSIANN and GSMaP tend to underestimate the rainfall. The overall result suggests that monthly and seasonal TRMM rainfall performed better than daily rainfall. It has also been found that both GSMaP and PERSIANN performed better in relatively flat areas than mountainous areas. Before the practical use of TRMM, the RMSE value needs to be improved by considering the topography of the study area or adjusting the bias. [ABSTRACT FROM AUTHOR]

An index-based insurance solution was developed to estimate and monitor near real-time forage production using the indicator Forage Production Index (FPI) as a surrogate of the grassland production. The FPI corresponds to the integral of the fraction of green vegetation cover derived from moderate spatial resolution time series images and was calculated at the 6 km × 6 km scale. An upscaled approach based on direct validation was used that compared FPI with field-collected biomass data and high spatial resolution (HR) time series images. The experimental site was located in the Lot and Aveyron departments of southwestern France. Data collected included biomass ground measurements from grassland plots at 28 farms for the years 2012, 2013 and 2014 and HR images covering the Lot department in 2013 (n = 26) and 2014 (n = 22). Direct comparison with ground-measured yield led to good accuracy (R2 = 0.71 and RMSE = 14.5%). With indirect comparison, the relationship was still strong (R2 ranging from 0.78 to 0.93) and informative. These results highlight the effect of disaggregation, the grassland sampling rate, and irregularity of image acquisition in the HR time series. In advance of Sentinel-2, this study provides valuable information on the strengths and weaknesses of a potential index-based insurance product from HR time series images. [ABSTRACT FROM AUTHOR]