bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent Applications of Chitin- and Chitosan-Based Polymers in Plants
Authors: Malerba Massimo , Cerana Raffaella
Published: May 2019

In recent years, the use of complex molecules based on the natural biopolymer chitin and/or on its deacetylated derivative chitosan has resulted in great advantages for many users. In particular, industries involved in the production of drugs, cosmetics, biotechnological items, and food have achieved better results using these particular molecules. In plants, chitin- and chitosan-based molecules are largely used as safe and environmental-friendly tools to ameliorate crop productivity and conservation of agronomic commodities. This review summarizes the results of the last two years on the application of chitin- and chitosan-based molecules on plant productivity. The open questions and future perspectives to overcome the present gaps and limitations are also discussed.
Photophysical and Electroluminescence Characteristics of Polyfluorene Derivatives with Triphenylamine
Authors: Zhang Qiang , Wang Po-I. , Ong Guang Liang , Tan Shen Hoong , Tan Zhong Wei , Hii Yew Han , Wong Yee Lin , Cheah Khee Sang , Yap Seong Ling , Ong Teng Sian , Tou Teck Yong , Nee Chen Hon , Liaw Der Jang , Yap Seong Shan
Published: May 2019

In this work, polymers of poly[(9,9-dioctylfluorenyl-2,7-diyl)-co-triphenylamine] with side chains containing: pyrene (C1), diphenyl (C2), naphthalene (C3), and isopropyl (C6) structures were synthesized via a Suzuki coupling reaction. The structures were verified using NMR and cyclic voltammetry measurements provide the HOMO and LUMO of the polymers. The polymer with pyrene (C1) and naphthalene (C3) produced photoluminescence in the green while the polymer with the side chain containing diphenyl (C2) and isopropyl (C6) produce dual emission peaks of blue-green photoluminescence (PL). In order to examine the electroluminescence properties of the polymers, the solutions were spin-coated onto patterned ITO anode, dried, and subsequently coated with an Al cathode layer to form pristine single layer polymer LEDs. The results are compared to a standard PFO sample. The electroluminescence spectra resemble the PL spectra for C1 and C3. The devices of C2, C3, and C6 exhibit voltage-dependent EL. An additional red emission peak was detected for C2 and C6, resulting in spectra with peaks at 435 nm, 490 nm, and 625 nm. The effects of the side chains on the spectral characteristics of the polymer are discussed.
Erosive Response of Non-Glaciated Pyrenean Headwater Catchments to the Last Major Climate Transition and Establishing Interglacial Conditions
Authors: Stange Kurt , Midtkandal Ivar , Nystuen Johan , Murray Andrew , Sohbati Reza , Thompson Warren , Spiegel Cornelia , Kuss Hans-Joachim
Published: May 2019

Non-glaciated mountain headwater catchments feature high-resolution geomorphic archives, which provide important insight into erosive processes and sediment dynamics in mountain ranges. As such the Valle de la Fueva catchments in the southern Pyrenees present high-lying talus remnants, extensive denudation surfaces (pediments), deeply incised tributary ravines, and low-lying fluvial-cut terraces. Based on geomorphic analyses and absolute dating using terrestrial cosmogenic nuclides and optically stimulated luminescence, a (late stage) catchment erosion model for the Valle de la Fueva was elaborated and indicates successive development stages of (i) lasting pedimentation under cold-climate conditions during Marine Isotope Stages 4–2, (ii) rapid fluvial dissection, sediment remobilization and downcutting of ravines in response to the last major climate transition and establishing interglacial conditions, and (iii) late stage fluvial incision after 3–4 ka due to regionally increased flood magnitudes, and/or intensification of agriculture and forest management. Valle de la Fueva headwater catchment analysis indicated that the styles and magnitudes of basin surface processes were directly correlated with the amplitude and nature of paleoclimatic changes, modified by the interplay of environmental parameters. In contrast to large-scale fluvial systems, mountain headwater catchments seemed to be less afflicted with temporal and spatial averaging biases. They are thus useful targets for investigating direct climate change effects, surface process coupling, and non-linear response mechanisms in Quaternary fluvial systems.
Implementation of Circular Economy Principles in Regional Solid Municipal Waste Management: The Case of Sverdlovskaya Oblast (Russian Federation)
Authors: Plastinina Iuliia , Teslyuk Lyudmila , Dukmasova Nataliya , Pikalova Elena
Published: May 2019

The circular economy entails the principles of rational waste management to the best advantage. Waste management in the Russian Federation is currently in a reform process, thus it is crucial to choose now the most efficient way for its development. Besides, in addition to general government strategic aims in such a vast country such as Russia, it is very important to take into consideration specific aspects typical of its territories, which will allow determining regional areas of activities. The article analyzes current municipal solid waste management in the Russian Federation. The case study is of one of the regions (Sverdlovskaya Oblast) characterized by some typical and specific problems which illustrate the results of the first stage of reforming. The authors analyzed the existing and planned mechanisms of state regulation in the waste management sector at regional and federal levels, and scrutinized changes in legislation. The development level of the circular economy in the region was determined on the basis of the index method. The life cycle analysis (LCA)-based evaluation approach made it possible to evaluate the economic efficiency of the production and economic activities at different stages of municipal solid waste (waste paper) recycling with due regard to externalities. The research showed that despite the current substantial economic potential of the secondary resources’ (waste paper) reuse, there was a low level of development of the circular economy index. Regional factors hindering and fostering the circular economy development were identified. Recommendations for improving the quality of governance of the municipal solid waste management were formulated.
Development of Supraglacial Ponds in the Everest Region, Nepal, between 1989 and 2018
Authors: Chand Mohan Bahadur , Watanabe Teiji
Published: May 2019

Several supraglacial ponds are developing and increasing in size and number in the Himalayan region. They are the precursors of large glacial lakes and may become potential for glacial lake outburst floods (GLOFs). Recently, GLOF events originating from supraglacial ponds were recorded; however, the spatial, temporal, and seasonal distributions of these ponds are not well documented. We chose 23 debris-covered glaciers in the Everest region, Nepal, to study the development of supraglacial ponds. We used historical Landsat images (30-m resolution) from 1989 to 2017, and Sentinel-2 (10-m resolution) images from 2016 to 2018 to understand the long-term development and seasonal variations of these ponds. We also used fine-resolution (0.5–2 m) WorldView and GeoEye imageries to reveal the high-resolution inventory of these features and these images were also used as references for accuracy assessments. We observed a continuous increase in the area and number of ponds from 1989–2017, with minor fluctuations. Similarly, seasonal variations were observed at the highest ponded area in the pre- and postmonsoon seasons, and lowest ponded area in the winter season. Substantial variations of the ponds were also observed among glaciers corresponding to their size, slope, width, moraine height, and elevation. The persistency and densities of the ponds with sizes >0.005 km2 were found near the glacier terminuses. Furthermore, spillway lakes on the Ngozompa, Bhote Koshi, Khumbu, and Lumsamba glaciers were expanding at a faster rate, indicating a trajectory towards large lake development. Our analysis also found that Sentinel-2 (10-m resolution) has good potential to study the seasonal changes of supraglacial ponds, while fine-resolution (<2 m) imagery is able to map the supraglacial ponds with high accuracy and can help in understanding the surrounding morphology of the glacier.
Effective Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion and Improved Nonmaximal Suppression Algorithm
Authors: Zhu Mingming , Xu Yuelei , Ma Shiping , Li Shuai , Ma Hongqiang , Han Yongsai
Published: May 2019

Aiming at the problem of insufficient representation ability of weak and small objects and overlapping detection boxes in airplane object detection, an effective airplane detection method in remote sensing images based on multilayer feature fusion and an improved nonmaximal suppression algorithm is proposed. Firstly, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airplane images using a limited amount of data. Then, the L2 norm normalization, feature connection, scale scaling, and feature dimension reduction are introduced to achieve effective fusion of low- and high-level features. Finally, a nonmaximal suppression method based on a soft decision function is proposed to solve the overlap problem of detection boxes. The experimental results show that the proposed method can effectively improve the representation ability of weak and small objects, as well as quickly and accurately detect airplane objects in the airport area.
Cropland Product Fusion Method Based on the Overall Consistency Difference: A Case Study of China
Authors: Zhong Yanfei , Luo Chang , Hu Xin , Wei Lifei , Wang Xinyu , Jin Shuying
Published: May 2019

There is inconsistency between the existing remote sensing cropland products, whose accuracy of estimated cropland area and spatial positioning needs to be improved. The existing generalized methods of generating synergy cropland products for improving the accuracy of existing products do not consider the overall consistency difference between the different products in each grid cell in the fusion process. To reduce the impact of the abnormal estimated cropland areas of the individual cropland products on the results, this paper proposes a method of generating a synergy cropland product by fusing the multiple existing cropland products, based on the overall consistency difference. In the proposed method, the process of fusing the multiple existing cropland products is based on the overall consistency difference of the estimated cropland area of all the cropland products in each grid cell. The synergy cropland product is then generated after determining the best combination level with the cropland statistics. In this study, we set 2010 as the base year, and used the proposed method to conduct experiments with four remote sensing cropland products: GlobCover 2009, MODIS Cropland, MCD12Q1, and FROM-GLC within China, and national cropland statistics. The results show that the synergy cropland product generated by the proposed method has a higher accuracy of cropland area estimation and spatial positioning than the results obtained by the generalized model, as well as the original products.
A Survey of Deep Learning-Based Human Activity Recognition in Radar
Authors: Li Xinyu , He Yuan , Jing Xiaojun
Published: May 2019

Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely on heuristic hand-crafted feature extraction, and their generalization capability is limited. Additionally, extracting features manually is time–consuming and inefficient. Deep learning acts as a hierarchical approach to learn high-level features automatically and has achieved superior performance for HAR. This paper surveys deep learning based HAR in radar from three aspects: deep learning techniques, radar systems, and deep learning for radar-based HAR. Especially, we elaborate deep learning approaches designed for activity recognition in radar according to the dimension of radar returns (i.e., 1D, 2D and 3D echoes). Due to the difference of echo forms, corresponding deep learning approaches are different to fully exploit motion information. Experimental results have demonstrated the feasibility of applying deep learning for radar-based HAR in 1D, 2D and 3D echoes. Finally, we address some current research considerations and future opportunities.
Artificial Neural Network for the Short-Term Prediction of Arctic Sea Ice Concentration
Authors: Choi Minjoo , De Silva Liyanarachchi Waruna Arampath , Yamaguchi Hajime
Published: May 2019

In this paper, we applied an artificial neural network (ANN) to the short-term prediction of the Arctic sea ice concentration (SIC). The prediction was performed using encoding and decoding processes, in which a gated recurrent unit encodes sequential sea ice data, and a feed-forward neural network model decodes the encoded input data. Because of the large volume of Arctic sea ice data, the ANN predicts the future SIC of each cell individually. The limitation of these singular predictions is that they do not use information from other cells. This results in low accuracy, particularly when there are drastic changes during melting and freezing seasons. To address this issue, we present a new data scheme including global and local SIC information, where the global information is represented by sea ice statistics. We trained ANNs using different data schemes and network architectures, and then compared their performances quantitatively and visually. The results show that, compared with a data scheme that uses only local sea ice information, the newly proposed scheme leads to a significant improvement in prediction accuracy.
Coastal Ecosystem Investigations with LiDAR (Light Detection and Ranging) and Bottom Reflectance: Lake Superior Reef Threatened by Migrating Tailings
Authors: Kerfoot W. , Hobmeier Martin , Green Sarah , Yousef Foad , Brooks Colin , Shuchman Robert , Sayers Mike , Lin Lihwa , Luong Phu , Hayter Earl , Reif Molly
Published: May 2019

Where light penetration is excellent, the combination of LiDAR (Light Detection And Ranging) and passive bottom reflectance (multispectral, hyperspectral) greatly aids environmental studies. Over a century ago, two stamp mills (Mohawk and Wolverine) released 22.7 million metric tons of copper-rich tailings into Grand Traverse Bay (Lake Superior). The tailings are crushed basalt, with low albedo and spectral signatures different from natural bedrock (Jacobsville Sandstone) and bedrock-derived quartz sands. Multiple Lidar (CHARTS and CZMIL) over-flights between 2008–2016—complemented by ground-truth (Ponar sediment sampling, ROV photography) and passive bottom reflectance studies (3-band NAIP; 13-band Sentinal-2 orbital satellite; 48 and 288-band CASI)—clarified shoreline and underwater details of tailings migrations. Underwater, the tailings are moving onto Buffalo Reef, a major breeding site important for commercial and recreational lake trout and lake whitefish production (32% of the commercial catch in Keweenaw Bay, 22% in southern Lake Superior). If nothing is done, LiDAR-assisted hydrodynamic modeling predicts 60% tailings cover of Buffalo Reef within 10 years. Bottom reflectance studies confirmed stamp sand encroachment into cobble beds in shallow (0-5m) water but had difficulties in deeper waters (>8 m). Two substrate end-members (sand particles) showed extensive mixing but were handled by CASI hyperspectral imaging. Bottom reflectance studies suggested 25-35% tailings cover of Buffalo Reef, comparable to estimates from independent counts of mixed sand particles (ca. 35% cover of Buffalo Reef by >20% stamp sand mixtures).
Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
Authors: Pelich Ramona , Chini Marco , Hostache Renaud , Matgen Patrick , Lopez-Martinez Carlos , Nuevo Miguel , Ries Philippe , Eiden Gerd
Published: May 2019

This research addresses the use of dual-polarimetric descriptors for automatic large-scale ship detection and characterization from synthetic aperture radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the detection results are merged subsequently. In this study, we propose to make use of the complex coherence between the two polarization channels of Sentinel-1 and to perform vessel detection in this domain. Therefore, an automatic algorithm, based on the dual-polarization coherence, and applicable to entire large scale SAR scenes in a timely manner, is developed. Automatic identification system (AIS) data are used for an extensive and also large scale cross-comparison with the SAR-based detections. The comparative assessment allows us to evaluate the added-value of the dual-polarization complex coherence, with respect to SAR intensity images in ship detection, as well as the SAR detection performances depending on a vessel’s size. The proposed methodology is justified statistically and tested on Sentinel-1 data acquired over two different and contrasting, in terms of traffic conditions, areas: the English Channel the and Pacific coastline of Mexico. The results indicate a very high SAR detection rate, i.e., >80%, for vessels larger than 60 m and a decrease of detection rate up to 40 % for smaller size vessels. In addition, the analysis highlights many SAR detections without corresponding AIS positions, indicating the complementarity of SAR with respect to cooperative sources for detecting dark vessels.
Scene Classification from Synthetic Aperture Radar Images Using Generalized Compact Channel-Boosted High-Order Orderless Pooling Network
Authors: Ni Kang , Wu Yiquan , Wang Peng
Published: May 2019

The convolutional neural network (CNN) has achieved great success in the field of scene classification. Nevertheless, strong spatial information in CNN and irregular repetitive patterns in synthetic aperture radar (SAR) images make the feature descriptors less discriminative for scene classification. Aiming at providing more discriminative feature representations for SAR scene classification, a generalized compact channel-boosted high-order orderless pooling network (GCCH) is proposed. The GCCH network includes four parts, namely the standard convolution layer, second-order generalized layer, squeeze and excitation block, and the compact high-order generalized orderless pooling layer. Here, all of the layers are trained by back-propagation, and the parameters enable end-to-end optimization. First of all, the second-order orderless feature representation is acquired by the parameterized locality constrained affine subspace coding (LASC) in the second-order generalized layer, which cascades the first and second-order orderless feature descriptors of the output of the standard convolution layer. Subsequently, the squeeze and excitation block is employed to learn the channel information of parameterized LASC statistic representation by explicitly modelling interdependencies between channels. Lastly, the compact high-order orderless feature descriptors can be learned by the kernelled outer product automatically, which enables low-dimensional but highly discriminative feature descriptors. For validation and comparison, we conducted extensive experiments into the SAR scene classification dataset from TerraSAR-X images. Experimental results illustrate that the GCCH network achieves more competitive performance than the state-of-art network in the SAR image scene classification task.
A Comparative Assessment of the Performance of Individual Tree Crowns Delineation Algorithms from ALS Data in Tropical Forests
Authors: Aubry-Kientz Mélaine , Dutrieux Raphaël , Ferraz Antonio , Saatchi Sassan , Hamraz Hamid , Williams Jonathan , Coomes David , Piboule Alexandre , Vincent Grégoire
Published: May 2019

Tropical forest canopies are comprised of tree crowns of multiple species varying in shape and height, and ground inventories do not usually reliably describe their structure. Airborne laser scanning data can be used to characterize these individual crowns, but analytical tools developed for boreal or temperate forests may require to be adjusted before they can be applied to tropical environments. Therefore, we compared results from six different segmentation methods applied to six plots (39 ha) from a study site in French Guiana. We measured the overlap of automatically segmented crowns projection with selected crowns manually delineated on high-resolution photography. We also evaluated the goodness of fit following automatic matching with field inventory data using a model linking tree diameter to tree crown width. The different methods tested in this benchmark segmented highly different numbers of crowns having different characteristics. Segmentation methods based on the point cloud (AMS3D and Graph-Cut) globally outperformed methods based on the Canopy Height Models, especially for small crowns; the AMS3D method outperformed the other methods tested for the overlap analysis, and AMS3D and Graph-Cut performed the best for the automatic matching validation. Nevertheless, other methods based on the Canopy Height Model performed better for very large emergent crowns. The dense foliage of tropical moist forests prevents sufficient point densities in the understory to segment subcanopy trees accurately, regardless of the segmentation method.
Automatic Data Selection and Boresight Adjustment of LiDAR Systems
Authors: Keyetieu Rabine , Seube Nicolas
Published: May 2019

This paper details a new automatic calibration method for the boresight angles between a LiDAR (Light Detection and Ranging) and an inertial measurement unit (IMU), based on a data selection algorithm, followed by the adjustment of boresight angles. This method, called LiDAR-IMU boresight automatic calibration (LIBAC), takes in input overlapping survey strips following simple line patterns over regular slopes. We first construct a boresight error observability criterion, used to select automatically the most sensitive points to boresight errors. From these points, we adjust the boresight angles. From a statistical analysis of the adjustment results, we derive the boresight angle precision. Results obtained with LIBAC on several LiDAR system integrated within drones are presented. We also give results about the reproducibility of the method.
An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
Authors: Wang Yulong , Xu Xingang , Huang Linsheng , Yang Guijun , Fan Lingling , Wei Pengfei , Chen Guo
Published: May 2019

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.
Crop Sensor-Based In-Season Nitrogen Management of Wheat with Manure Application
Authors: Aranguren Marta , Castellón Ander , Aizpurua Ana
Published: May 2019

It is difficult to predict the crop-available nitrogen (N) from farmyard manures applied to soil. The aim of this study was to assess the usefulness of the proximal sensors, Yara N-TesterTM and RapidScan CS-45, for diagnosing the N nutritional status of wheat after the application of manures at sowing. Three annual field trials were established (2014–2015, 2015–2016 and 2016–2017) with three types of fertilizer treatments: dairy slurry (40 t ha−1 before sowing), sheep manure (40 t ha−1 before sowing) and conventional treatment (40 kg N ha−1 at tillering). For each treatment, five different mineral N fertilization doses were applied at stem elongation: 0, 40, 80, 120, and 160 kg N ha−1. The proximal sensing tools were used at stem elongation before the application of mineral N. Normalized values of the proximal sensing look promising for adjusting mineral N application rates at stem elongation. For dairy slurry, when either proximal sensor readings were 60–65% of the reference plants with non-limiting N, the optimum N rate for maximizing yield was 118–128 kg N ha−1. When the readings were 85–90%, the optimum N rate dropped to 100–110 kg N ha−1 for both dairy slurry and conventional treatments. It was difficult to find a clear relationship between sensor readings and yield for sheep manure treatments. Measurements taken with RapidScan C-45 were less time consuming and better represent the spatial variation, as they are taken on the plant canopy. Routine measurements throughout the growing season are particularly needed in climates with variable rainfall. The application of 40 kg N ha−1 at the end of winter is necessary to ensure an optimal N status from the beginning of wheat crop development. These research findings could be used in applicator-mounted sensors to make variable-rate N applications.
Relative Azimuthal-Angle Matching (RAM): A Screening Method for GEO-LEO Reflectance Comparison in Middle Latitude Forests
Authors: Adachi Yusuke , Kikuchi Ryota , Obata Kenta , Yoshioka Hiroki
Published: May 2019

This study introduced a data screening method for comparing the reflectances in middle latitude forest regions collected by a Geostationary Earth Observing (GEO) satellite and a Low Earth Orbit (LEO) satellite. This method attempts to reduce the differences between the relative azimuth angles of the GEO and LEO observations. The method, called relative azimuthal-angle matching (RAM), takes advantage of the high temporal resolution of the GEO satellites, which enables collection of a wide range of relative azimuth angles within a day. The performance of the RAM method was evaluated using data in the visible and near-infrared bands collected by the Himawari-8/Advanced Himawari Imager (AHI) and the Terra/Moderate Resolution Imaging Spectroradiometer (MODIS). The consistency of the reflectance pairs of MODIS and AHI selected by the RAM method was compared with the consistency of the reflectance pairs acquired simultaneously by the two sensors. The data were matched pixel-by-pixel after applying atmospheric corrections and cloud screening. The results show that RAM improved the reflectance ratio by approximately 10% for the red and NIR bands on average relative to the simultaneous observations. Significant improvements in the two bands were observed (20%), especially among data collected in the fall and winter. Performance of RAM depends largely on season. Especially in summer, the reflectance pair chosen by RAM showed less consistency than solar zenith-angle matching (SZM). The results also indicated the relatively large influence of the spectral response functions on the green and red bands of the two sensors.
Fusion Analysis of Optical Satellite Images and Digital Elevation Model for Quantifying Volume in Debris Flow Disaster
Authors: Miura Hiroyuki
Published: May 2019

Rapid identification of affected areas and volumes in a large-scale debris flow disaster is important for early-stage recovery and debris management planning. This study introduces a methodology for fusion analysis of optical satellite images and digital elevation model (DEM) for simplified quantification of volumes in a debris flow event. The LiDAR data, the pre- and post-event Sentinel-2 images and the pre-event DEM in Hiroshima, Japan affected by the debris flow disaster on July 2018 are analyzed in this study. Erosion depth by the debris flows is empirically modeled from the pre- and post-event LiDAR-derived DEMs. Erosion areas are detected from the change detection of the satellite images and the DEM-based debris flow propagation analysis by providing predefined sources. The volumes and their pattern are estimated from the detected erosion areas by multiplying the empirical erosion depth. The result of the volume estimations show good agreement with the LiDAR-derived volumes.
Water Storage Variations in Tibet from GRACE, ICESat, and Hydrological Data
Authors: Zou Fang , Tenzer Robert , Jin Shuanggen
Published: May 2019

The monitoring of water storage variations is essential not only for the management of water resources, but also for a better understanding of the impact of climate change on hydrological cycle, particularly in Tibet. In this study, we estimated and analyzed changes of the total water budget on the Tibetan Plateau from the Gravity Recovery And Climate Experiment (GRACE) satellite mission over 15 years prior to 2017. To suppress overall leakage effect of GRACE monthly solutions in Tibet, we applied a forward modeling technique to reconstruct hydrological signals from GRACE data. The results reveal a considerable decrease in the total water budget at an average annual rate of −6.22 ± 1.74 Gt during the period from August 2002 to December 2016. In addition to the secular trend, seasonal variations controlled mainly by annual changes in precipitation were detected, with maxima in September and minima in December. A rising temperature on the plateau is likely a principal factor causing a continuous decline of the total water budget attributed to increase melting of mountain glaciers, permafrost, and snow cover. We also demonstrate that a substantial decrease in the total water budget due to melting of mountain glaciers was partially moderated by the increasing water storage of lakes. This is evident from results of ICESat data for selected major lakes and glaciers. The ICESat results confirm a substantial retreat of mountain glaciers and an increasing trend of major lakes. An increasing volume of lakes is mainly due to an inflow of the meltwater from glaciers and precipitation. Our estimates of the total water budget on the Tibetan Plateau are affected by a hydrological signal from neighboring regions. Probably the most significant are aliasing signals due to ground water depletion in Northwest India and decreasing precipitation in the Eastern Himalayas. Nevertheless, an integral downtrend in the total water budget on the Tibetan Plateau caused by melting of glaciers prevails over the investigated period.
Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion
Authors: Alexakis Dimitrios D. , Tapoglou Evdokia , Vozinaki Anthi-Eirini K. , Tsanis Ioannis K.
Published: May 2019

Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.
High Spatial Resolution Remote Sensing for Salt Marsh Mapping and Change Analysis at Fire Island National Seashore
Authors: Campbell Anthony , Wang Yeqiao
Published: May 2019

Salt marshes are changing due to natural and anthropogenic stressors such as sea level rise, nutrient enrichment, herbivory, storm surge, and coastal development. This study analyzes salt marsh change at Fire Island National Seashore (FIIS), a nationally protected area, using object-based image analysis (OBIA) to classify a combination of data from Worldview-2 and Worldview-3 satellites, topobathymetric Light Detection and Ranging (LiDAR), and National Agricultural Imagery Program (NAIP) aerial imageries acquired from 1994 to 2017. The salt marsh classification was trained and tested with vegetation plot data. In October 2012, Hurricane Sandy caused extensive overwash and breached a section of the island. This study quantified the continuing effects of the breach on the surrounding salt marsh. The tidal inundation at the time of image acquisition was analyzed using a topobathymetric LiDAR-derived Digital Elevation Model (DEM) to create a bathtub model at the target tidal stage. The study revealed geospatial distribution and rates of change within the salt marsh interior and the salt marsh edge. The Worldview-2/Worldview-3 imagery classification was able to classify the salt marsh environments accurately and achieved an overall accuracy of 92.75%. Following the breach caused by Hurricane Sandy, bayside salt marsh edge was found to be eroding more rapidly (F1, 1597 = 206.06, p < 0.001). However, the interior panne/pool expansion rates were not affected by the breach. The salt marsh pannes and pools were more likely to revegetate if they had a hydrological connection to a mosquito ditch (χ2 = 28.049, p < 0.001). The study confirmed that the NAIP data were adequate for determining rates of salt marsh change with high accuracy. The cost and revisit time of NAIP imagery creates an ideal open data source for high spatial resolution monitoring and change analysis of salt marsh environments.
Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land
Authors: Zhang Wenhao , Xu Hui , Zhang Lili
Published: May 2019

This study conducted the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The AHI Level 3 AOD (Version 3.0) was evaluated using the collocated Aerosol Robotic Network (AERONET) level 2.0 direct sun AOD measurements over the last three years (May 2016–December 2018) at 58 selected AERONET sites. A comprehensive comparison between AHI and AERONET AOD was carried out, which yielded a correlation coefficient (R) of 0.82, a slope of 0.69, and a root mean square error (RMSE) of 0.16. The results indicate a good agreement between AHI and AERONET AOD, while revealing that the AHI aerosol retrieval algorithm tends to underestimate the atmospheric aerosol load. In addition, the expected uncertainty of AHI Level 3 AOD (Version 3.0) is ± (0.1 + 0.3 × AOD). Furthermore, the performance of the AHI aerosol retrieval algorithm exhibits regional variation. The best performance is reported over East Asia (R 0.86), followed by Southeast Asia (R 0.79) and Australia (R 0.35). The monthly and seasonal comparisons between AHI and AERONET show that the best performance is found in summer (R 0.93), followed by autumn (R 0.84), winter (R 0.82), and spring (R 0.76). The worst performance was observed in March (R 0.75), while the best performance appeared in June (R 0.94). The variation in the annual mean AHI AOD on the scale of hours demonstrates that AHI can perform continuous (no less than ten hours) aerosol monitoring.
Sugarcane Productivity Mapping through C-Band and L-Band SAR and Optical Satellite Imagery
Authors: Molijn Ramses , Iannini Lorenzo , Vieira Rocha Jansle , Hanssen Ramon
Published: May 2019

Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of São Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity.
Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil
Authors: Paredes-Trejo Franklin , Barbosa Humberto , dos Santos Carlos A. C.
Published: May 2019

Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI) (from the Advanced SCATterometer (ASCAT) soil moisture data) were evaluated against in situ rainfall observations under different bioclimatic conditions in Brazil. The research V7 version of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TRMM TMPA) was also used as a state-of-the-art rainfall product with an up-bottom approach. Comparisons were made at daily and 0.25° scales, during the time-span of 2007–2015. The SM2RAIN-CCI, SM2RAIN-ASCAT, and TRMM TMPA products showed relatively good Pearson correlation values (R) with the gauge-based observations, mainly in the Caatinga (CAAT) and Cerrado (CER) biomes (R median > 0.55). SM2RAIN-ASCAT largely underestimated rainfall across the country, particularly over the CAAT and CER biomes (bias median < −16.05%), while SM2RAIN-CCI is characterized by providing rainfall estimates with only a slight bias (bias median: −0.20%), and TRMM TMPA tended to overestimate the amount of rainfall (bias median: 7.82%). All products exhibited the highest values of unbiased root mean square error (ubRMSE) in winter (DJF) when heavy rainfall events tend to occur more frequently, whereas the lowest values are observed in summer (JJA) with light rainfall events. The SM2RAIN-based products showed larger contribution of systematic error components than random error components, while the opposite was observed for TRMM TMPA. In general, both SM2RAIN-based rainfall products can be effectively used for some operational purposes on a daily scale, such as water resources management and agriculture, whether the bias is previously adjusted.
A Novel Internet of Things-Enabled Accident Detection and Reporting System for Smart City Environments
Authors: Bhatti Fizzah , Shah Munam Ali , Maple Carsten , Islam Saif Ul
Published: May 2019

Internet of Things-enabled Intelligent Transportation Systems (ITS) are gaining significant attention in academic literature and industry, and are seen as a solution to enhancing road safety in smart cities. Due to the ever increasing number of vehicles, a significant rise in the number of road accidents has been observed. Vehicles embedded with a plethora of sensors enable us to not only monitor the current situation of the vehicle and its surroundings but also facilitates the detection of incidents. Significant research, for example, has been conducted on accident rescue, particularly on the use of Information and Communication Technologies (ICT) for efficient and prompt rescue operations. The majority of such works provide sophisticated solutions that focus on reducing response times. However, such solutions can be expensive and are not available in all types of vehicles. Given this, we present a novel Internet of Things-based accident detection and reporting system for a smart city environment. The proposed approach aims to take advantage of advanced specifications of smartphones to design and develop a low-cost solution for enhanced transportation systems that is deployable in legacy vehicles. In this context, a customized Android application is developed to gather information regarding speed, gravitational force, pressure, sound, and location. The speed is a factor that is used to help improve the identification of accidents. It arises because of clear differences in environmental conditions (e.g., noise, deceleration rate) that arise in low speed collisions, versus higher speed collisions). The information acquired is further processed to detect road incidents. Furthermore, a navigation system is also developed to report the incident to the nearest hospital. The proposed approach is validated through simulations and comparison with a real data set of road accidents acquired from Road Safety Open Repository, and shows promising results in terms of accuracy.
SPS and DPS: Two New Grid-Based Source Location Privacy Protection Schemes in Wireless Sensor Networks
Authors: Wang Qiuhua , Zhan Jiacheng , Ouyang Xiaoqin , Ren Yizhi
Published: May 2019

Wireless Sensor Networks (WSNs) have been widely deployed to monitor valuable objects. In these applications, the sensor node senses the existence of objects and transmitting data packets to the sink node (SN) in a multi hop fashion. The SN is a powerful node with high performance and is used to collect all the information sensed by the sensor nodes. Due to the open nature of the wireless medium, it is easy for an adversary to trace back along the routing path of the packets and get the location of the source node. Once adversaries have got the source node location, they can capture the monitored targets. Thus, it is important to protect the source node location privacy in WSNs. Many methods have been proposed to deal with this source location privacy protection problem, and most of them provide routing path diversity by using phantom node (PN) which is a fake source node used to entice the adversaries away from the actual source node. But in the existing schemes, the PN is determined by the source node via flooding, which not only consumes a lot of communication overhead, but also shortens the safety period of the source node. In view of the above problems, we propose two new grid-based source location privacy protection schemes in WSNs called grid-based single phantom node source location privacy protection scheme (SPS) and grid-based dual phantom node source location privacy protection scheme (DPS) in this paper. Different from the idea of determining the phantom node by the source node in the existing schemes, we propose to use powerful sink node to help the source node to determine the phantom node candidate set (PNCS), from which the source node randomly selects a phantom node acting as a fake source node. We evaluate our schemes through theoretical analysis and experiments. Experimental results show that compared with other schemes, our proposed schemes are more efficient and achieves higher security, as well as keeping lower total energy consumption. Our proposed schemes can protect the location privacy of the source node even in resource-constrained wireless network environments.
Incoherent Optical Frequency-Domain Reflectometry Based on Homodyne Electro-Optic Downconversion for Fiber-Optic Sensor Interrogation
Authors: Clement Juan , Maestre Haroldo , Torregrosa Germán , Fernández-Pousa Carlos R.
Published: May 2019

Fiber-optics sensors using interrogation based on incoherent optical frequency-domain reflectometry (I-OFDR) offer benefits such as the high stability of interference in the radio-frequency (RF) domain and the high SNR due to narrowband RF detection. One of the main impairments of the technique, however, is the necessity of high-frequency detectors and vector network analyzers (VNA) in systems requiring high resolution. In this paper, we report on two C-band implementations of an I-OFDR architecture based on homodyne electro-optic downconversion enabling detection without VNA and using only low-bandwidth, high-sensitivity receivers, therefore alleviating the requirements of conventional I-OFDR approaches. The systems are based on a pair of modulators that are synchronized to perform modulation and homodyne downconversion at a reference frequency of 25.5 kHz. In the first system, we attain centimeter resolution with a sensitivity down to −90 dB using the modulation frequency range comprised between 3.2 and 14.2 GHz. In the second, we measured, for the first time using this approach, Rayleigh backscattering traces in standard single mode fiber with resolution of 6 m and a sensitivity of −83 dB by use of the 10.1–30.1 MHz range. These results show the feasibility of these simple, homodyne downconversion I-OFDR systems as compact interrogators for distributed or quasi-distributed optical fiber sensors.