Backscatter Information Research Articles

The distributed CERES-Maize model with crop parameters determined through data assimilation assists in regional irrigation schedule optimization

Crop model–based irrigation scheduling is essential for improving crop production and water resource allocation. However, the parametrization of crop growth processes, for which relevant genetic coefficients must be determined through field scale experiments, is usually hampered by poor spatial representation; this limitation compromises the robustness of irrigation scheduling at the regional scale. We propose a framework for optimizing regional irrigation schedules; this framework involves using a distributed crop model with crop parameters determined through data assimilation. We first conducted a sensitivity analysis to identify the key genetic coefficients (P2: photoperiod sensitivity coefficient; P5: thermal time from silking to physiological maturity; G3: kernel filling rate during the linear grain filling stage and under optimal conditions) affecting the leaf area index (LAI) in the CERES-Maize model. Subsequently, the field-scale CERES-Maize model was calibrated and validated using data collected from field experiments conducted in 2016 and 2017, respectively. Subsequently, the LAI estimated from 2020 microwave backscattering data was used to assimilate remote sensing information and crop model in order to obtain the key genetic coefficients of maize in the distributed crop model. Finally, on the basis of the distributed crop model, a multiobjective genetic algorithm was executed to optimize irrigation schedules under various meteorological scenarios (e.g., precipitation and reference evapotranspiration). The LAI of maize at a regional scale was accurately estimated using backscattering information extracted from microwave remote sensing images (R2 = 0.94, RMSE = 0.27). Compared with the crop model parameters calibrated at the field scale, the crop model parameters determined through data assimilation resulted in more accurate simulations of LAI, soil water content, and yield at the regional scale. The irrigation schedule based on the distributed model increased the maize yield corresponding to the local current irrigation schedule by 125–851 kg/ha, increased the water use efficiency by 0.02–0.17 kg/m3, and reduced the irrigation amount by 2–60 mm. The proposed framework for estimating genetic coefficients based on data assimilation methods helps in regional irrigation scheduling for other crops.

Stochastic Geometry Based Performance Study for Wireless-Powered Backscatter Communications

Wirelessly powered backscatter communication (WP-BackCom) enables battery-free Internet of Things (IoT) nodes to passively modulate and backscatter information on the energy signals transmitted by power beacons (PBs) and to harvest energy for realizing energy self-sustainability. It has been deemed a promising solution for the large-scale deployment of IoT nodes. In this paper, we exploit stochastic geometry (SG) to develop a tractable framework to evaluate the successful transmission probability in WP-BackCom while considering the imperfect successive interference cancellation (SIC) at each gateway (GW) and the non-linear energy harvesting (EH) model and energy-causality constraint at each backscatter user (BU). The successful transmission probability is defined as the probability for the event that not only the BU can harvest sufficient power from PBs for sustaining its operation but also the backscattered information can be successfully decoded by the GW. We derive the active probability into a closed form and then obtain the successful transmission probability based on the random spectrum access (RSA) policy. The closed-form expression for the successful transmission probability is derived under three special cases. We also devise a closed-form expression for the sub-optimal reflection coefficient to obtain a sub-optimal successful transmission probability. The analysis reveals that the sub-optimal reflection coefficient increases with the SIC coefficient. Numerical simulations validate the correctness of the above theoretical derivations and examine the impacts of various parameters on the successful transmission probability.

Seasonal change of multifrequency backscatter in three Baltic Sea habitats

This study investigated the seasonality of acoustic backscatter intensities, exploring three habitats in the southwestern Baltic Sea: 1) a mussel-covered reef, 2) coarse sand and gravel, and 3) seagrass meadows. Backscatter information of different, partly calibrated frequencies (200, 400, 550, and 700 kHz) was collected in three seasons (May, August, and October). The acoustic data were supported by point samples and video profiles for grain size and benthic community analysis. Angular response curves helped to quantify the seasonal backscatter response of the different frequencies. The multifrequency and multiseasonal backscatter maps distinguish the three habitats and reveal variable seasonal differences in acoustic backscatter, but not all changes in the benthic community can be recognized in the acoustic data. 1) The high-backscatter response of the mussel-covered reef shows little seasonal differences and was frequency independent. 2) The ecologically valuable coarse sand and gravel areas show small-scale seasonal alterations in the sediment composition and morphology, mainly caused by changes in local hydrodynamics. Higher frequencies were found best suited to identify coarse sand and gravel. 3) Seagrass meadows seasonality is dominated by growth of seagrass blades, increasing the backscatter response compared to bare sand. The use of multiple frequencies is beneficial as the low frequency is sensitive to changes in the shallow subsurface and benthic features such as seagrass rhizomes, while the higher frequency highlights changes related to coarser sediment.

Cloud Removal with SAR-Optical Data Fusion and Graph-Based Feature Aggregation Network

In observations of Earth, the existence of clouds affects the quality and usability of optical remote sensing images in practical applications. Many cloud removal methods have been proposed to solve this issue. Among these methods, synthetic aperture radar (SAR)-based methods have more potential than others because SAR imaging is hardly affected by clouds, and can reflect ground information differences and changes. While SAR images used as auxiliary information for cloud removal may be blurred and noisy, the similar non-local information of spectral and electromagnetic features cannot be effectively utilized by traditional cloud removal methods. To overcome these weaknesses, we propose a novel cloud removal method using SAR-optical data fusion and a graph-based feature aggregation network (G-FAN). First, cloudy optical images and contemporary SAR images are concatenated and transformed into hyper-feature maps by pre-convolution. Second, the hyper-feature maps are inputted into the G-FAN to reconstruct the missing data of the cloud-covered area by aggregating the electromagnetic backscattering information of the SAR image, and the spectral information of neighborhood and non-neighborhood pixels in the optical image. Finally, post-convolution and a long skip connection are adopted to reconstruct the final predicted cloud-free images. Both the qualitative and quantitative experimental results from the simulated data and real data experiments show that our proposed method outperforms traditional deep learning methods for cloud removal.

An Adaptive PolSAR Trilateral Filter Based on the Mechanism of Scattering Consistency

Polarimetric Synthetic Aperture Radar (PolSAR) can record the backscattering information of ground objects by capturing the phase difference of echo signal in different polarization combinations. Due to the characteristics of coherent imaging, there is a lot of coherent speckle noise in PolSAR images. However, most of the current PolSAR image despeckling algorithms are difficult to balance the ability in aspects of speckle suppression, polarization information retention and edge structure preservation. This paper proposes an adaptive PolSAR trilateral filter (PTF) based on the mechanism of scattering consistency, which consists of following three steps. (1) The marker map of ground object scattering category is firstly obtained based on the dominant scattering mechanism determined by Freeman decomposition of the polarization covariance matrix. (2) Next, the neighborhood pixels with the same scattering mechanism as the center pixel are screened out within an adaptive filtering window. (3) A PolSAR trilateral filter is established through integration of the spatial proximity, polarization similarity and local structural characteristics of the center pixel and the screened pixels to effectively suppress the speckle noises. Experimental results upon the data of Radarsat-2 and AIRSAR demonstrate that PTF can not only suppress coherent speckle noise effectively without sacrificing the edge structure information, but also retain the unique polarization information of PolSAR image. In particular, compared with the traditional six widely used PolSAR despeckling algorithms, PTF have been significantly improved in terms of Equivalent Number of Looks (ENL) and Degree of difference (Dod) indexes by 2.49 times and 6 orders of magnitude, respectively.

Ultrasonic detection of white etching defect based on convolution neural network

<sec>Unlike classical defects formed by rolling contact fatigue, white etching defect (WED) including white etching area and white etching crack will cause surface to spall in the early stage and the service life to shorten seriously. Located in the subsurface of bearings, the tiny size WED is difficult to detect by conventional ultrasonic methods. The root cause of WED generation remains unclear. It is time consuming and expensive to prepare samples during the evolution of such defects. For characterizing the WED at early stage, five evolving states concerning the existing microscopic information are established in this paper. The immersion ultrasonic inspection process is simulated based on <i>k</i>-space pseudo spectrum method.</sec><sec>For the later evolutionary stage with crack, the bearing can be simplified into a homogeneous three-layer model by ignoring the internal grain structure. The crack depth is obtained by using the ultrasonic reflection coefficient amplitude spectrum (URCAS), with an error of 1.5%. For other states without crack, the spectrum characteristic is no longer evident with slight acoustic impedance difference between layers. The polycrystalline structure on a microscale is thus realized based on Voronoi diagram, from which the grain induced backscattering can be used to amplify the microstructure variations at different stages. The backscattering signal is influenced by the grain size and detection frequency from the simulation. Since a direct comparison of backscattering information among evolutionary stages is difficult, the five different evolutionary stages of WED are recognized with the help of deep learning. The received waveform is transformed into a time-frequency map by short-time Fourier transform. Based on RESNET network structure, the results show that the train accuracy and validation accuracy reach 92% and 97% respectively. This study provides a sound way to characterize WED, which is conducive to early failure prediction and residual life evaluation.</sec>

Spatio-temporal distribution of sea-ice thickness using a machine learning approach with Google Earth Engine and Sentinel-1 GRD data

Knowledge of ice thickness and its distribution is of great interest to plan ship and offshore operations in ice areas. It is also a major direct factor in climate change. However, it is currently the most inaccurate sea-ice parameter according to a large majority of the scientific community. Satellite-borne synthetic aperture radar (SAR) data are highly valuable for monitoring daily ice-covered oceans. In this study, we develop a new technique based on a machine learning Random Forest (RF) regression approach using the combination of the in-situ thickness measurements and the backscattering information from Sentinel-1 to retrieve the level first-year ice (FYI) thickness. By applying the technique over the Sentinel-1 ground range detected (GRD) data set available in Google Earth Engine (GEE) over the Beaufort Sea spanning a time period from Apr 2015 to Sep 2018, a thickness of up to 1.5 m with a root mean square error (RMSE) of 22 cm is retrieved.

Application of an unsupervised clustering algorithm on in situ broadband acoustic data to identify different mesopelagic target types

Abstract The mesopelagic zone (200–1000 m depth) contains high fish species diversity but biomass and abundances are uncertain yet essential to understand ecosystem functioning. Hull-mounted acoustic systems (usually 38 kHz) often make assumptions on average target strength (TS) of mesopelagic fish assemblages when estimating biomass/abundance. Here, an unsupervised clustering algorithm was applied on broadband acoustic data (54–78 kHz), collected by a towed instrumented platform in the central Northeast Atlantic, to identify different mesopelagic target types based on similarity of individual TS spectra. Numerical density estimates from echo-counting showed spatial differences in vertical distribution patterns of the different target types and TS spectra data suggested that >30% of the gas-bearing targets had high resonance frequencies (>60 kHz) with low scattering strength at 38 kHz. This conceptual study highlights the importance of separating targets into different target groups to obtain correct backscatter information and to account for all relevant scatterers when estimating average TS at 38 kHz, in order to achieve more accurate biomass/abundance estimates. It furthermore demonstrates the use of a towed broadband acoustic platform for fine-scale numerical density estimates as a complementary method to hull-mounted acoustic data to increase knowledge on mesopelagic ecosystem structure.

Performance evaluation of machine learning algorithms using optical and microwave data for LULC classification

LULC mapping is essential for any satellite data to visualize the land information. This study aims to focus on the performance evaluation of Machine Learning (ML) Algorithms for LULC mapping of satellite datasets. Optical (Sentinel-2), Microwave (Sentinel-1), and fused datasets have been generated using Ehlers, Brovey and Principal Component fusion. For each dataset, two ML Algorithms i.e. Random Forest (RF) and Support Vector Machine (SVM) have been applied. Results suggested that the optical and fused dataset achieved the more promising results than the microwave dataset as it contains only the backscatter information. Moreover, fusion of microwave with optical achieved more realistic LULC classification results. The overall accuracy derived for optical, microwave, and fused data are 92 %, 93.43 %, 37.71 %, 37.14 %, 85.71 % and 89.14 % using RF and SVM classifiers, respectively.

CP-IRGS: A Region-Based Segmentation of Multilook Complex Compact Polarimetric SAR Data

The Canadian RADARSAT constellation mission (RCM) is represented by three synthetic aperture radar (SAR) satellites, each of which includes a compact polarimetry (CP) mode. CP is advantageous because it provides increased backscatter information relative to single and conventional dual-polarized modes and has larger swath widths relative to a quad polarization mode. CP captures single-look complex data which can be used to derive the multilook complex (MLC) coherence matrix, or, equivalently, the Stokes vector data of the backscattered field. The challenge is to develop computer vision algorithms that can be used to effectively segment the scene using this new data source. An unsupervised region-based segmentation approach has been designed and implemented that utilizes the complex Wishart distribution characteristic of the MLC CP data. The segmentation method is based on the iterative region growing with semantics algorithm originally designed for single and dual pol intensity SAR data. The algorithm has been tested using both simulated CP SAR images and a pair of available quad polarization SAR images. The results demonstrate that the CP-IRGS algorithm provides more accurate segmentation images than those using only the RH and RV channel intensity images.

Feature-Level Fusion between Gaofen-5 and Sentinel-1A Data for Tea Plantation Mapping

The accurate mapping of tea plantations is significant for government decision-making and environmental protection of tea-producing regions. Hyperspectral and Synthetic Aperture Radar (SAR) data have recently been widely used in land cover classification, but effective integration of these data for tea plantation mapping requires further study. This study developed a new feature-level image fusion method called LPPSubFus that combines locality preserving projection and subspace fusion (SubFus) to map tea plantations. Based on hyperspectral and SAR data, we first extracted spectral indexes, textures, and backscattering information. Second, this study applied LPPSubFus to tea plantation mapping with different classification algorithms. Finally, we compared the performance of LPPSubFus, SubFus, and pixel-level image fusion in tea plantation mapping. Feature-level image fusion performed better than pixel-level image fusion. An improvement of about 3% was achieved using feature-level image fusion compared to hyperspectral data alone. Regarding feature-level image fusion, LPPSubFus improved the overall accuracy by more than 3% compared to SubFus. In particular, LPPSubFus using neural network algorithms achieved the highest overall accuracy (95%) and over 90% producer and user accuracy for tea plantations and forests. In addition, LPPSubFus was more compatible with different classification algorithms than SubFus. Based on these findings, it is concluded that LPPSubFus has better and more stable performance in tea plantation mapping than pixel-level image fusion and SubFus. This study demonstrates the potential of integrating hyperspectral and SAR data via LPPSubFus for mapping tea plantations. Our work offers a promising tea plantation mapping method and contributes to the understanding of hyperspectral and SAR data fusion.

Sentinel-1 Sensitivity to Soil Moisture at High Incidence Angle and the Impact on Retrieval Over Seasonal Crops

Approximately, 30% of the Sentinel-1 (S-1) swath over land is imaged with incidence angles higher than 40°. Still, the interplay among the scattering mechanisms taking place at such a high incidence and their implications on the backscatter information content is often disregarded. This article investigates, through an experimental and numerical study, the S-1 sensitivity to the surface soil moisture (SSM) over agricultural fields observed at low (~33°) and high (~43°) incidence angles and quantifies the impact of the incidence angle on the SSM retrieval accuracy. The study sites are the Apulian Tavoliere (Italy) and REd de MEDición de la HUmedad del Suelo (REMEDHUS) (Spain), which are both instrumented with a hydrologic network continuously measuring SSM. At low incidence angles, results confirm that for crops such as wheat and barley, dominated in C-band by surface scattering, there exists a good sensitivity of S-1 VV to SSM. At high incidence angles, the sensitivity to SSM holds through the combination of the soil attenuated and double bounce scattering. Conversely, over crops dominated by volume scattering, such as sugar beet, the S-1 VV signal is not correlated with the in situ SSM observations, neither at low nor at high incidence. For all the crops, the sensitivity of S-1 to SSM in VH is found significantly lower than in VV. The impact of the incidence angle on the SSM retrieval has been studied with a recursive algorithm based on a short-term change detection approach. An upper and lower bounds for the worsening of the S-1 VV retrieval performance at far versus near range observations have been estimated. In the worst-case scenario, the root mean square error (RMSE) increases from ~0.056 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , at low incidence, to ~0.071 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , at high incidence. The mechanism that lowers the retrieval accuracy at high incidence angles is further investigated in the synthetic experiment and its impact on the RMSE is estimated in terms of the volume scattering contribution.

Development of a 355-nm high-spectral-resolution lidar using a scanning Michelson interferometer for aerosol profile measurement.

A simple 355-nm high-spectral-resolution lidar (HSRL) is developed for continuous observation of aerosol profiles. A scanning Michelson interferometer is used to separate the Rayleigh and Mie scattering components. The interferometer is periodically scanned in the range of one fringe. Interference contrast, which contains aerosol backscatter information, is estimated at each height through fitting analysis of the scan data. The interference contrast and fringe position are calibrated with the reference signals taken from the transmitted laser. Furthermore, the 1-day continuous measurement of aerosol backscatter and extinction coefficients is demonstrated. Comparison with a nighttime Raman lidar indicates a good performance of the scanning method.

Use of Sentinel-1 GRD SAR Images to Delineate Flood Extent in Pakistan

Floods are some of the most serious and devastating natural hazards on earth, bringing huge threats to lives, properties, and living environments. Rapid delineation of the spatial extent of flooding is of great importance for the dynamic monitoring of flood evolution and corresponding emergency strategies. Some of the current flood mapping methods mainly process single date images characterized by simple flood situations and homogenous backgrounds. Although other methods show good performance for images with harsh conditions for floods, they must be trained—many times based on pre-classified samples—or undergo complicated parameter tuning processes, which require computation efforts. The widely used change detection methods utilize multi-temporal Synthetic Aperture Radar (SAR) images for the detection of flood area, but the results are largely influenced by the quality of defined reference images. Furthermore, these methods were mostly applied for some river basin floods, which are not effective for the large scale, semi-arid regions with complex flood conditions, and various land cover types. All of these extremely limited the use of these methods for the timely and accurate extraction of the spatial distribution pattern of floods in other typical and broad areas. Based on the above considerations, this paper presents a new method for rapidly determining the extent of flooding in large, semi-arid areas with challenging environmental conditions, based on multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data. First, a preprocessing scheme is applied to perform geometric correction and to estimate the intensity of the imagery. Second, an automatic thresholding procedure is used to generate an initial land and water classification through the integration of the probability density distribution. A fuzzy logic-based approach, combining SAR backscattering information and other auxiliary data, is then used to refine the initial classified image. The fuzzy logic-based refinement removes areas that look similar to water in the SAR images, significantly enhancing the flood mapping accuracy. Finally, a post-processing step consisting of morphological operations and extraction improves the homogeneity of the extracted flood segments, discards isolated pixels, and gives the final flood map. This method can automatically detect the extent of floods at little computational cost. As Sentinel-1 data are publicly available and have a fast repeat cycle, the procedure can provide near real time results for rapid emergency response following flash floods. The accuracy of the proposed method is assessed at three test sites in Pakistan, which covered diverse landscapes and suffered large scale serious flooding after a long and severe drought in 2015. In comparison with the more recent studies from Ohki et al., 2020, and Shahabi et al., 2020, our results indicate the best spatial agreement with GF-2 panchromatic multi-spectral (PMS) water classification, with an encouraging overall accuracy ranging from 91.1% to 96.6%, and Kappa coefficients ranging from 0.893 to 0.954. Especially for the areas with fragmented floods, heterogeneous backgrounds, and the areas where samples are highly unbalanced in the SAR images, our method combines the global statistics and local relationships of backscattering properties, terrain, and other auxiliary information, enabling to effectively preserve the detailed structures and also remove the noise.

Semi-automated impervious feature extraction using built-up indices developed from space-borne optical and SAR remotely sensed sensors

In order to cope up with the emerging demand of interpreting and detecting urban features from remotely sensed imagery, this study proposes an innovative unique yet practically easy approach to extract impervious surfaces and features using certain built-up indices (BUIs) that are developed from space-borne optical and SAR remotely sensed sensors. Resourcesat-2 LISS III and HH/HV dual polarized L-band ALOS PALSAR data are respectively the space-borne optical and synthetic aperture radar (SAR) data used for designing the BUIs used for impervious feature detection. BUI generated from optical sensor uses green (G), red (R), near infrared (NIR) and short wave infrared (SWIR) channels simultaneously. BUI designed from SAR sensor uses horizontal-horizontal (HH) co-polarized and horizontal-vertical (HV) cross-polarized L-band backscatter coefficient/sigma nought (σ°) data. The feature extraction performed by two new indices, one derived from existing indices using spectral-based information from optical sensors– an Impervious Built-up Index (IBUIopt); and the other from SAR HH/HV σ° backscatter information– an Impervious Built-up Index (IBUIsar); are cross-checked and validated with Ground Truth (GT) data that revealed the performance of the indices designed and proposed in the study for three sample sites bearing completely different biophysical environments; for easy and rapid extraction of impervious land features in satellite imageries. Overall accuracies of impervious feature extraction using IBUIopt are 92.3%, 95% and 75.6% in comparison to 90%, 98% and 87.6% using IBUIsar for the three study areas respectively. The approach adopted in this study is henceforth acceptable due to its high accuracy, simplicity and reliability; and hence easy to replicate.

The SVD Beamformer: Physical Principles and Application to Ultrafast Adaptive Ultrasound.

A shift of paradigm is currently underway in biomedical ultrasound thanks to plane or diverging waves coherent compounding for faster imaging. One remaining challenge consists in handling phase and amplitude aberrations induced during the ultrasonic propagation through complex layers. Unlike conventional line-per-line imaging, ultrafast ultrasound provides backscattering information from the whole imaged area for each transmission. Here, we take benefit from this feature and propose an efficient approach to perform fast aberration correction. Our method is based on the Singular Value Decomposition (SVD) of an ultrafast compound matrix containing backscattered data for several plane wave transmissions. First, we explain the physical signification of SVD and associated singular vectors within the ultrafast matrix formalism. We theoretically demonstrate that the separation of spatial and angular variables, rendered by SVD on ultrafast data, provides an elegant and straightforward way to optimize the angular coherence of backscattered data. In heterogeneous media, we demonstrate that the first spatial and angular singular vectors retrieve respectively the non-aberrated image of a region of interest, and the phase and amplitude of its aberration law. Numerical, in vitro and in vivo results prove the efficiency of the image correction, but also the accuracy of the aberrator determination. Based on spatial and angular coherence, we introduce a complete methodology for adaptive beamforming of ultrafast data, performed on successive isoplanatism patches undergoing SVD beamforming. The simplicity of this method paves the way to real-time adaptive ultrafast ultrasound imaging and provides a theoretical framework for future quantitative ultrasound applications.

Building Damage Assessment Based on the Fusion of Multiple Texture Features Using a Single Post-Earthquake PolSAR Image

After a destructive earthquake, most of the casualties are brought about by building collapse. Our work is focused on using a single postevent PolSAR (full-polarimetric synthetic aperture radar) imagery to extract the building damage information for effective emergency decision-making. PolSAR data is subject to sunlight and contains richer backscatter information. The undamaged buildings whose orientation is not parallel to the SAR flight pass and the collapsed buildings share similar dominated scattering mechanisms, i.e., volume scattering, so they are easily confused. However, the two kinds of buildings have different textures. For a more accurate classification of damaged buildings and undamaged buildings, the OPCE (optimization of polarimetric contrast enhancement) algorithm is employed to enhance the contrast ratio of the textures for the two kinds of buildings and the precision-weighted multifeature fusion (PWMF) method is proposed to merge the multiple texture features. The experiment results show that the accuracy of the proposed novel method is improved by 8.34% compared to the traditional method. In general, the proposed PWMF method can effectively merge the multiple features and the overestimation of the building collapse rate can be reduced using the proposed method in this study.

Optimal Operating Mode Selection of Ambient Backscatter Communication Systems

Ambient backscatter (AB) communication has emerged as an energy-preserving technology in Internet of Things (IoT). Specifically, an AB transmitter communicates with its receiver by backscattering ambient radio frequency (RF) signals instead of active information transmissions. When not operating in the communication mode, the AB transmitter can instead harvest the RF energy to replenish its battery, e.g., for supplying sensing energy consumptions. In this paper, we consider the optimal operating mode selection problem of the AB system that the transmitter decides either backscatter information or harvest energy based on the time-varying ambient RF signal strength. In particular, we aim to maximize the long-term average communication data rate given that an average energy-harvesting budget constraint is met. We formulate the optimization as a 0-1 knapsack problem and propose a dynamic programming (DP) based solution algorithm and a reduced-complexity greedy algorithm. Furthermore, we simulate under various setups, and show that the two proposed algorithms can achieve significant performance improvement over other representative benchmark methods.

Tree diversity assessment and above ground forests biomass estimation using SAR remote sensing: A case study of higher altitude vegetation of North-East Himalayas, India

Forest biomass is one of the significant components for assessing the productivity and sustainability of the forest's ecosystem. In the present study, the mapping and estimation of above ground biomass (AGB) was performed using field observations employing allometric equations and C band Sentinel 1A and L band Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) remote sensing satellite images for the parts of north eastern Himalayan forests, Arunachal Pradesh. Total 57 transects of 500 m × 10 m size was laid in West Kameng and Tawang districts (altitude range: 1047–4161 m) of Arunachal Pradesh, in which a total of 12203 individuals of 67 tree species (Diameter at Breast Height (DBH) range: 3.18–235.45 cm) was recorded. The study exhibited higher tree density (428 individuals' ha−1) and basal cover (7658.63 m2ha-1) in the studied forests. The highest above ground biomass was contributed by Castanopsis indica (103.82 t ha−1) followed by Pinus roxburghii (46.24 t ha−1) and Quercus semicarpifolia (23.14 t ha−1). Further, AGB was estimated using the Horizontal-Horizontal (HH), Horizontal-Vertical (HV) polarizations acquired by the ALOS PALSAR 50 m mosaic (2008) dataset and Vertical-Vertical (VV), Vertical-Horizontal (VH) polarizations acquired by the Sentinel 1A dataset. The relationship between field based AGB and radar backscatter for selected sample plots was established using pair wise correlation. While comparing the two satellite-based SAR images, it is observed that high correlation between radar backscatter and field based AGB was observed in ALOS PALSAR as compared to Sentinel 1A SAR. This is due to higher sensitive to backscatter values and higher penetration level of L band ALOS PALSAR as compared to C band Sentinel 1A SAR. The correlation study indicates high correlation of HV polarizations in ALOS PALSAR (HV polarization R2 = 0.63; HH polarization R2 = 0.58) as compared to Sentinel 1A (VV polarization R2 = 0.054 and VH polarization R2 = 0.044). The most accurate and reliable method to estimate AGB is the field based method, but it is time consuming and tedious. Whereas, AGB estimation by L band SAR remote sensing is time effective and can be used for larger area in comparatively less time. The present study exhibited poor species diversity in higher altitudes northeast Himalayan forests as species diversity decreases with increase in altitudes. On the other hand, the higher AGB in the studied forests may be due to favorable environmental conditions including less temperature and more nutrient availability as well as less biotic interferences in terms of anthropogenic disturbances, herbivory and grazing. The study signifies the potential use of L and C band backscatter information in the reliable estimation of spatial AGB in sub-tropical to alpine forests of Arunachal Pradesh.

Better together—Combining acoustics and environmental DNA to understand ecosystems

Acoustic signals have historically been and presently are the state-of-the-art for sensing the ocean at small to large spatial scales. Passive acoustics non-invasively assess sound levels, surface conditions, human activity, and the distribution of vocalizing marine life. Echosounders provide acoustic backscatter information that contribute not only critical information on biology but also physical components of the water column which has supplied invaluable knowledge on the community structure, organism size and distribution, and oceanic microstructure. Advances in DNA methods present an opportunity to harness a new technology and fundamentally improve our capacity to monitor habitats, communities, and individual species. Environmental DNA (eDNA) includes whole microorganisms, tissue fragments, reproductive and waste products, and other cellular material in a sample. eDNA methods, such as acoustics, allow for the identification of species without having to physically capture animals. While eDNA and acoustics are both powerful methods for identifying and describing organisms present in the environment, no single survey method can fully represent the “real” condition. We envision a future where remote platforms can gather and relay data in near-real time and can elicit a targeted response to information, such as launching a drone to survey additional locations or increasing the sampling frequency when key species are present.