Airborne Platforms Research Articles

Parallel and Distributed Computing for Anomaly Detection From Hyperspectral Remote Sensing Imagery

Anomaly detection from remote sensing images is to detect pixels whose spectral signatures are different from their background. Anomalies are often man-made targets. With such target signatures being unknown, anomaly detection has many important applications, such as water quality monitoring, crop stress surveying, and law enforcement-related uses, where prior information of targets is often unavailable. The key to success is accurate background modeling. Anomaly detection from remote sensing images is challenging because spatial coverage is very large and the background is highly heterogeneous. For pixel-based anomaly detection, computing cost in background modeling and a spatial-convolution-type detection process is very expensive. Thus, parallel and distributed computing is critical in reducing execution time, which can fit the need for real-time or near real-time detection from airborne and spaceborne platforms in support of immediate decision-making. This article reviews the recent advances in anomaly detection from hyperspectral remote sensing images and their implementation using parallel and distributed systems. The classical methods, i.e., the Reed-Xiaoli (RX) algorithm and its variants, including its real-time processing version, are illustrated in commodity graphic processing units (GPUs), cloud, and field-programmable gate array (FPGA) implementations. Practical issues and future development trends are also discussed.

Validation of Permafrost Active Layer Estimates from Airborne SAR Observations

In permafrost regions, active layer thickness (ALT) observations measure the effects of climate change and predict hydrologic and elemental cycling. Often, ALT is measured through direct ground-based measurements. Recently, synthetic aperture radar (SAR) measurements from airborne platforms have emerged as a method for observing seasonal thaw subsidence, soil moisture, and ALT in permafrost regions. This study validates airborne SAR-derived ALT estimates in three regions of Alaska, USA using calibrated ground penetrating radar (GPR) geophysical data. The remotely sensed ALT estimates matched the field observations within uncertainty for 79% of locations. The average uncertainty for the GPR-derived ALT validation dataset was 0.14 m while the average uncertainty for the SAR-derived ALT in pixels coincident with GPR data was 0.19 m. In the region near Utqiaġvik, the remotely sensed ALT appeared slightly larger than field observations while in the Yukon-Kuskokwim Delta region, the remotely sensed ALT appeared slightly smaller than field observations. In the northern foothills of the Brooks Range, near Toolik Lake, there was minimal bias between the field data and remotely sensed estimates. These findings suggest that airborne SAR-derived ALT estimates compare well with in situ probing and GPR, making SAR an effective tool to monitor permafrost measurements.

Permafrost Dynamics Observatory-Part I: Postprocessing and Calibration Methods of UAVSAR L-Band InSAR Data for Seasonal Subsidence Estimation.

Interferometric synthetic aperture radar (InSAR) has been used to quantify a range of surface and near surface physical properties in permafrost landscapes. Most previous InSAR studies have utilized spaceborne InSAR platforms, but InSAR datasets over permafrost landscapes collected from airborne platforms have been steadily growing in recent years. Most existing algorithms dedicated toward retrieval of permafrost physical properties were originally developed for spaceborne InSAR platforms. In this study, which is the first in a two part series, we introduce a series of calibration techniques developed to apply a novel joint retrieval algorithm for permafrost active layer thickness retrieval to an airborne InSAR dataset acquired in 2017 by NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and Western Canada. We demonstrate how InSAR measurement uncertainties are mitigated by these calibration methods and quantify remaining measurement uncertainties with a novel method of modeling interferometric phase uncertainty using a Gaussian mixture model. Finally, we discuss the impact of native SAR resolution on InSAR measurements, the limitation of using few interferograms per retrieval, and the implications of our findings for cross‐comparison of airborne and spaceborne InSAR datasets acquired over Arctic regions underlain by permafrost.

Aerodynamic and Structural Optimisation of Maritime Patrol Radar System Radome using Evolutionary Algorithms

Airborne early warning systems are deployed for collecting surveillance information on airborne enemy targets in real-time. The Maritime Patrol Radar system is used for surveillance of sea surface for various types of ships and low flying aircraft. Radio Detection And Ranging system, or RADAR, in short, is an Electromagnetic sensor integrated on such airborne platforms. An antenna of this radar system is generally mounted under the belly of the aircraft and protected by a cover called a radome. This radome is installed to protect the radar antenna from environmental disturbances. Due to the installation of the radome, increased drag is experienced by aircraft during its flight due to resistance to the flow of the oncoming air. Radome design is a multidisciplinary effort involving structural, aerodynamics, and electromagnetic disciplines. In this study, the multi-disciplinary design of the maritime patrol aircraft radome for optimality in terms of structural strength and aerodynamic performance is carried out by integrating both disciplinary analyses on an optimisation software platform. The utopia point in terms of these two disciplines is found.

Discrete anisotropic radiative transfer modelling of solar-induced chlorophyll fluorescence: Structural impacts in geometrically explicit vegetation canopies

Solar-induced fluorescence (SIF) is a subtle but informative optical signal of vegetation photosynthesis. Remotely sensed SIF integrates environmental, physiological and structural changes that alter photosynthesis at leaf, plant and canopy scales. Radiative transfer models are ideally suited to investigate the complex sources of variability in the SIF signal to guide the interpretation of SIF retrievals from airborne and space-borne platforms. Here, we coupled the Fluspect-Cx model of leaf optical properties and chlorophyll-a fluorescence with the Discrete Anisotropic Radiative Transfer (DART) model to upscale SIF from individual leaves to three-dimensional (3D) structurally explicit canopies. For one-dimensional homogeneous (turbid-like) canopies, DART-SIF was nearly identical to SIF simulated in two existing models, SCOPE and mSCOPE (RMSE <0.221 W.m−2.μm−1.sr−1). DART simulations in geometrically explicit 3D canopies offered four important insights regarding the influence of vegetation structure on the multi-angular top-of-canopy SIF signal. First, changes in the 3D canopy architecture of maize crops, represented by leaf density (leaf area index), and plant clumping (canopy closure) had a larger impact on SIF than the modelled photosynthetic efficiency distinction between sun-adapted and shade-adapted foliage. Second, clumping of leaves at the crop and stand levels was identified as one of the key driving factors of multi-angular anisotropy of red and far-red SIF (686 and 740 nm) for both maize and eucalyptus canopies. Third, non-photosynthetic woody material had a significant impact on top-of-canopy SIF in modelled 3D forest stands. Wood shadowing decreased the photosynthetically active radiation absorbed by green leaves, and consequently the SIF emissions, by 10% in sparse and 17% in dense eucalyptus stands. The wood obstruction (blocking) effect, quantified as a relative difference of SIF escape probabilities from canopies with and without wood in the nadir viewing direction, decreased far-red SIF by 4–6% but it had a smaller and sometimes positive influence (by less than 2%) on red SIF. Fourth, DART 3D radiative budget profiles revealed that the majority of the SIF signal from a dense eucalyptus stand originated from the top 25% of the simulated canopy. Interestingly, the introduction of bark-covered woody elements did not alter the simulated balance and omnidirectional escape factor of red SIF in this upper canopy part but did raise significantly both of them in case of far-red SIF. These results demonstrate the importance of 3D radiative transfer and radiative budget simulations for investigating SIF interactions in structurally complex plant canopies and for a better understanding of spatiotemporal and multi-angular remote sensing SIF observations.

Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook

Intensifying pressure on global aquatic resources and services due to population growth and climate change is inspiring new surveying technologies to provide science-based information in support of management and policy strategies. One area of rapid development is hyperspectral remote sensing: imaging across the full spectrum of visible and infrared light. Hyperspectral imagery contains more environmentally meaningful information than panchromatic or multispectral imagery and is poised to provide new applications relevant to society, including assessments of aquatic biodiversity, habitats, water quality, and natural and anthropogenic hazards. To aid in these advances, we provide resources relevant to hyperspectral remote sensing in terms of providing the latest reviews, databases, and software available for practitioners in the field. We highlight recent advances in sensor design, modes of deployment, and image analysis techniques that are becoming more widely available to environmental researchers and resource managers alike. Systems recently deployed on space- and airborne platforms are presented, as well as future missions and advances in unoccupied aerial systems (UAS) and autonomous in-water survey methods. These systems will greatly enhance the ability to collect interdisciplinary observations on-demand and in previously inaccessible environments. Looking forward, advances in sensor miniaturization are discussed alongside the incorporation of citizen science, moving toward open and FAIR (findable, accessible, interoperable, and reusable) data. Advances in machine learning and cloud computing allow for exploitation of the full electromagnetic spectrum, and better bridging across the larger scientific community that also includes biogeochemical modelers and climate scientists. These advances will place sophisticated remote sensing capabilities into the hands of individual users and provide on-demand imagery tailored to research and management requirements, as well as provide critical input to marine and climate forecasting systems. The next decade of hyperspectral aquatic remote sensing is on the cusp of revolutionizing the way we assess and monitor aquatic environments and detect changes relevant to global communities.

Aircraft to Ground Profiling: Turbulence Measurements and Optical System Performance Modeling

A series of flight experiments have demonstrated a novel approach to measuring path-resolved optical turbulence quantities, such as , along an air-to-ground slant path. This paper describes the data acquisition experiments that involved two laser beams propagating between an orbiting airborne platform and a stationary ground terminal over an 8 km slant path. Ground-based and in-flight measurements were collected simultaneously, and profiles were computed using the difference of differential-tilt variance (DDTV) technique. This paper describes the DDTV technique that enables the path-resolved measurement of turbulence strength resulting in profiles. The resulting turbulence profiles reveal what is believed to be the aero-optical contamination from the aircraft boundary layer within the statistics closest to the aircraft. Therefore, the contamination of the aero-optical environment can be quantified with respect to the rest of the atmospheric propagation path. Lastly, this paper presents analyses that compare the measured atmospheric turbulence profiles to state-of-the-art atmospheric models. The analyses extend beyond comparisons and show the measurement-versus-modeling comparison in terms of key directed energy system propagation parameters such as Greenwood frequency, coherence diameter, Rytov number, isoplanatic angle, Tyler frequency, open-loop jitter, and open-loop Strehl ratio. The slant path turbulence is analyzed in the context of air-to-ground and ground-to-air directed energy systems.

The Scientific Legacy of NASA’s Operation IceBridge

AbstractThe National Aeronautics and Space Administration (NASA)’s Operation IceBridge (OIB) was a 13‐year (2009–2021) airborne mission to survey land and sea ice across the Arctic, Antarctic, and Alaska. Here, we review OIB’s goals, instruments, campaigns, key scientific results, and implications for future investigations of the cryosphere. OIB’s primary goal was to use airborne laser altimetry to bridge the gap in fine‐resolution elevation measurements of ice from space between the conclusion of NASA’s Ice, Cloud, and land Elevation Satellite (ICESat; 2003–2009) and its follow‐on, ICESat‐2 (launched 2018). Additional scientific requirements were intended to contextualize observed elevation changes using a multisensor suite of radar sounders, gravimeters, magnetometers, and cameras. Using 15 different aircraft, OIB conducted 968 science flights, of which 42% were repeat surveys of land ice, 42% were surveys of previously unmapped terrain across the Greenland and Antarctic ice sheets, Arctic ice caps, and Alaskan glaciers, and 16% were surveys of sea ice. The combination of an expansive instrument suite and breadth of surveys enabled numerous fundamental advances in our understanding of the Earth’s cryosphere. For land ice, OIB dramatically improved knowledge of interannual outlet‐glacier variability, ice‐sheet, and outlet‐glacier thicknesses, snowfall rates on ice sheets, fjord and sub‐ice‐shelf bathymetry, and ice‐sheet hydrology. Unanticipated discoveries included a reliable method for constraining the thickness within difficult‐to‐sound incised troughs beneath ice sheets, the extent of the firn aquifer within the Greenland Ice Sheet, the vulnerability of many Greenland and Antarctic outlet glaciers to ocean‐driven melting at their grounding zones, and the dominance of surface‐melt‐driven mass loss of Alaskan glaciers. For sea ice, OIB significantly advanced our understanding of spatiotemporal variability in sea ice freeboard and its snow cover, especially through combined analysis of fine‐resolution altimetry, visible imagery, and snow radar measurements of the overlying snow thickness. Such analyses led to the unanticipated discovery of an interdecadal decrease in snow thickness on Arctic sea ice and numerous opportunities to validate sea ice freeboards from satellite radar altimetry. While many of its data sets have yet to be fully explored, OIB’s scientific legacy has already demonstrated the value of sustained investment in reliable airborne platforms, airborne instrument development, interagency and international collaboration, and open and rapid data access to advance our understanding of Earth’s remote polar regions and their role in the Earth system.

Jet Propulsion Laboratory Small Satellite Dynamics Testbed Planar Air-Bearing Propulsion System Characterization

The Small Satellite Dynamics Testbed (SSDT) at the Jet Propulsion Laboratory offers infrastructure that supports small satellite guidance and control testing in a relevant dynamics environment while constrained by the gravity of Earth. One of the testing environments of the SSDT is its planar air-bearing platform, which has two lateral and one rotational degrees of freedom. This paper details the new position-control system and its compressed-gas thrusters of the planar air-bearing test platform, and characterizes the pertinent parameters related to the platform and propulsion system. A detailed description of the hardware and software systems of the platform is provided. Testing shows a maximum float time of 13.5 min, an average maximum thrust per thruster of 0.51 N, a tank pressure drop of 6.85 psi/s, and minimum impulse bit of 40 ms. The paper further presents results necessary for designing position controllers for use on this system, such as the relationship between number of thruster firing and output force, and the timing latency in the thruster firing control system. The paper shows that the embedded computer used in this platform exhibits timing delays of approximately 34 ms. Implications of these results on control system design are discussed in the conclusions.

Unpiloted Aerial Vehicle Acquired Lidar for Mapping Monumental Architecture

ABSTRACTAs lidar becomes a regular part of surveying, ground-based platforms (handheld, mobile, and terrestrial lidar) and airborne platforms (piloted aircraft) are being joined by unpiloted aerial vehicle (UAV)–acquired lidar. We present a method for leveraging UAV-acquired lidar data with data collected using different lidar platforms (terrestrial and piloted aircraft), at a range of resolutions (1 to +1,000 points per m2) and geographic scales. We use these instruments to document a dry-masonry stone wall enclosing a religious precinct within the royal center at Kealakekua, Kona District, Hawai'i Island. Prior to European contact in AD 1779, Kealakekua was the center of the island-wide polity during the annual Makahiki festival. Results of this study suggest that when the wall was constructed around AD 1640, it was the largest structure ever built on the island of Hawai'i as well as a strong material expression of the power of state religion and the Makahiki rituals.

Evaluating the Performance of the state-of-the-art HybridSN Deep Learning Algorithm for Airborne Hyperspectral Image Classification

This study aims to evaluate the performance of state-of-the-art HybridSN deep learning algorithm versus standard machine learning (ML) and deep learning (DL) techniques using open-source Python libraries for producing hyperspectral land use and land cover (LULC) classification maps. Japanese Chikusei hyperspectral datasets captured by the airborne platform using Hyperspec-VNIR-C sensor were used in this study. Standard ML methods used in this study were support vector machine linear kernel (SVM-linear), support vector machine radial basis function kernel (SVM-RBF) and random forests (RFs) that were provided in Python’s Scikit-learn library. DL techniques used in this study were multilayer perceptron (MLP), two-dimensional convolutional neural network (2-D CNN) and hybrid spectral convolutional neural network (HybridSN), which integrates the 2-D and 3-D feature learning. These DL models were built based on the sequential model using Keras API. The results show that all the proposed methods obtained overall accuracies (OAs) above 95%. The HybridSN and 2-D CNN models gave the best score with 99.97% OAs for hyperspectral image classification using the Chikusei dataset.

Modelling lidar-derived estimates of forest attributes over space and time: A review of approaches and future trends

Light detection and ranging (lidar) data acquired from airborne or spaceborne platforms have revolutionized measurement and mapping of forest attributes. Airborne data are often either acquired using multiple overlapped flight lines to provide complete coverage of an area of interest, or using transects to sample a given population. Spaceborne lidar datasets are unique to each sensor and are sample- or profile-based with characteristics driven by acquisition mode and orbital parameters. To leverage the wealth of accurate vegetation structural data from these lidar systems, a number of approaches have been developed to extend these observations over broader areas, from local landscapes to the globe. In this review we examine studies that have utilised modelling approaches to extend air- or space-based lidar data with the aim of communicating methods, outcomes, and accuracies, and offering guidance on linking lidar metrics and lidar-derived forest attributes with broad-area predictors. Modelling approaches are developed for a variety of applications. In some cases, generation of spatially-exhaustive layers may be useful for forest management purposes, driving management and inventory decisions over smaller focus areas or regions. In other cases, outputs are designed for monitoring at regional or global scales, and may be – due to the spatial grain of the structural estimates – insufficiently accurate or reliable for management. From the reviewed studies, we found height, aboveground biomass and volume, derived from either upper proportions of a large-footprint full-waveform lidar profiles, or statistically modelled from discrete return small-footprint lidar point clouds, to be the most commonly extended forest attributes, followed by canopy cover, basal area and stand complexity. Assessment of the accuracy and bias of the extrapolated forest attributes varied with both independent and model-derived estimates. The coefficient of determination (R2) was the most often reported, followed by absolute and relative (i.e., as a proportion of the mean) root mean square error (RMSE and RMSE% respectively). Compilation of the stated accuracies suggested that the variance explained in predictions of forest height ranged from R2 = 0.38 to 0.90 (mean = 0.64), RMSE from 2 to 6m and RMSE% from 12 to 34%. For volume, R2 ranged from 0.25 to 0.72 (mean = 0.53) and RMSE from 60 to 87 m3/ha and for aboveground biomass (AGB) R2 ranged from 0.35 to 0.78 (mean = 0.55) and RMSE from 28 to 44 Mg/ha. There was no consensus on the level of accuracy required to support successful extension over larger areas. Ultimately, the review suggests that the information need motivating the spatial extension over larger areas drives the choice of the type of lidar data, spatial datasets and related grain. We conclude by discussing future directions and the outlook for new approaches including new lidar-derived response variables, advances in modelling approaches, and assessment of change.

Approach on Recognizing Uncovered Earth Region from Aerial Images Based on Multi-feature Fusion

It is one of the main hidden dangers of power transmission lines accidents if there is the uncovered earth under or near power transmission lines. It can give the important early warning message to prevent the accidents through recognizing the uncovered earth region from aerial images of Unmanned Aerial Vehicle (UAV) power transmission lines inspection. Due to the low recognizing precision of Mask RCNN CNN (Mask Convolution Neural Network), this paper proposed an approach to recognize the uncovered earth region from aerial images of UAV by image feature fusion. The HOG and LBP features of aerial images were extracted and their dimension were reduced. Then these two features were fused by different weights. The experiments show that, (1) the average precision of recognizing the uncovered earth region can be above 80%, which is the lowest requirement to use; (2) the weights of two features should make the orders of magnitude of the two features as equal as possible. The approach is application for the first image filtering by the UAV airborne platform because it not only has enough good recognizing precision but also is very rapid, which provides a novel way for objective recognition from UAV aerial images.

Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU.

The increase in annual wildfires in many areas of the world has triggered international efforts to deploy sensors on airborne and space platforms to map these events and understand their behaviour. During the summer of 2017, an airborne flight campaign acquired mid-wave infrared imagery over active wildfires in Northern Ontario, Canada. However, it suffered multiple position-based equipment issues, thus requiring a non-standard geocorrection methodology. This study presents the approach, which utilizes a two-step semi-automatic geocorrection process that outputs image mosaics from airborne infrared video input. The first step extracts individual video frames that are combined into orthoimages using an automatic image registration method. The second step involves the georeferencing of the imagery using pseudo-ground control points to a fixed coordinate systems. The output geocorrected datasets in units of radiance can then be used to derive fire products such as fire radiative power density (FRPD). Prior to the georeferencing process, the Root Mean Square Error (RMSE) associated with the imagery was greater than 200 m. After the georeferencing process was applied, an RMSE below 30 m was reported, and the computed FRPD estimations are within expected values across the literature. As such, this alternative geocorrection methodology successfully salvages an otherwise unusable dataset and can be adapted by other researchers that do not have access to accurate positional information for airborne infrared flight campaigns over wildfires.

Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data

The aim of this study is to evaluate the effectiveness of the identification of Natura 2000 wetland habitats (Alkaline fens—code 7230, and Transition mires and quaking bogs—code 7140) depending on various remotely sensed (RS) data acquired from an airborne platform. Both remote sensing data and botanical reference data were gathered for mentioned habitats in the Lower (LB) and Upper Biebrza (UB) River Valley and the Janowskie Forest (JF) in different seasonal stages. Several different classification scenarios were tested, and the ones that gave the best results for analyzed habitats were indicated in each campaign. In the final stage, a recommended term of data acquisition, as well as a list of remote sensing products, which allowed us to achieve the highest accuracy mapping for these two types of wetland habitats, were presented. Designed classification scenarios integrated different hyperspectral products such as Minimum Noise Fraction (MNF) bands, spectral indices and products derived from Airborne Laser Scanning (ALS) data representing topography (developed in SAGA), or statistical products (developed in OPALS—Orientation and Processing of Airborne Laser Scanning). The image classifications were performed using a Random Forest (RF) algorithm and a multi-classification approach. As part of the research, the correlation analysis of the developed remote sensing products was carried out, and the Recursive Feature Elimination with Cross-Validation (RFE-CV) analysis was performed to select the most important RS sub-products and thus increase the efficiency and accuracy of developing the final habitat distribution maps. The classification results showed that alkaline fens are better identified in summer (mean F1-SCORE equals 0.950 in the UB area, and 0.935 in the LB area), transition mires and quaking bogs that evolved on/or in the vicinity of alkaline fens in summer and autumn (mean F1-SCORE equals 0.931 in summer, and 0.923 in autumn in the UB area), and transition mires and quaking bogs that evolved on dystrophic lakes in spring and summer (mean F1-SCORE equals 0.953 in spring, and 0.948 in summer in the JF area). The study also points out that the classification accuracy of both wetland habitats is highly improved when combining selected hyperspectral products (MNF bands, spectral indices) with ALS topographical and statistical products. This article demonstrates that information provided by the synergetic use of data from different sensors can be used in mapping and monitoring both Natura 2000 wetland habitats for its future functional assessment and/or protection activities planning with high accuracy.

Observing Water Surface Temperature from Two Different Airborne Platforms over Temporarily Flooded Wadden Areas at the Elbe Estuary—Methods for Corrections and Analysis

Over the Hahnöfer Nebenelbe, a part of the Elbe estuary near Hamburg, Germany, a combined aerial survey with an unmanned aerial system (UAV) and a gyrocopter was conducted to acquire information about the water surface temperatures. The water temperature in the estuary is important for biological processes and living conditions of riverine organisms. This study aimed to develop a workflow that allows for comparing and analysing surface temperatures acquired by two different remote sensing systems. The thermal infrared (TIR) datasets were compared with in situ measurements gathered during the data acquisition, where both TIR datasets showed a varying bias. Potential error sources regarding the absolute and relative accuracy were investigated and modelled based on the available measurements, including emissivity, atmosphere, skin effect at the water surface, camera flat field correction and calibration. The largest effects on the observed TIR water temperature had the camera calibration and the modelled atmospheric effects. After the correction steps, both datasets could be combined to create a multitemporal representation of the temperature pattern and profiles over the survey area’s wadden flats.

Image Fusion-The Pioneering Technique for Real-Time Image Processing Applications

An image is a two-dimensional function that is expressed through spatial coordinates X, Y. At any pair of coordinates (x, y), the amplitude of a point is called the intensity of that pixel. Digital Image comprises a predictable number of components, each of which has a precise value at a given region. Those components are called pixels. Image Fusion is a phenomenon of transforming data from two or more images of a scenario into a single, more descriptive image taken than both of the input images, and is more appropriate for information processing. Image Fusion (IF) has been utilized in numerous application regions/areas. Remote Sensing Satellites (RSS) produce different images based on their sensory characteristics. Among those images, Panchromatic (PAN) and Multi-Spectral (MS) images are widely used in Satellite Image Fusion (SIF). The Image Fusion (IF) techniques are broadly classified as methods for the Spatial and Frequency domains. Wavelet Fusion Techniques (WFT) based on the Frequency-Domain (FD) are having applications in medical, space, and military applications. This literature delivers a study of some of the Image Fusion (IF) techniques. Remote Sensing Image (RSI) and Data Fusion (DF) seeks to merge the data acquired from sensors installed on satellites, airborne platforms, and ground-based sensors with specific spatial, spectral and temporal resolutions to produce merged data containing more accurate information than is found in each of the individual data sources.

An approach for the precise DEM generation in urban environments using multi-GNSS

Digital Elevation Models (DEMs) of large areas are usually prepared by satellite images and airborne Light Detection and Ranging (LiDAR). However, obstacles due to urban canyon and others negatively affect the precision of DEM generated through the airborne platform. Global Navigation Satellite System networks, utilizing Real-Time Kinematic networks (NRTK), or post-processing kinematic (PPK), play an active role in supporting applications requiring high positioning accuracies. The principal objective of this research is to generate urban DEM using multi-GNSS. The methodology comprises collecting and processing multi-GNSS data by PPK technique, automatic filtering of processed data for outliers in elevation and density, and DEM generation. The PPK resulted in 71.92% fixed solutions. Spatial outlier filtering algorithm (SOFA) detected the outliers with 99.92% overall accuracy. A decimeter level precise DEM is generated. Overall, the present research strengthens the scope of multi-GNSS, especially in the application, which requires high accuracy.

Distributed Deep Learning for Remote Sensing Data Interpretation

As a newly emerging technology, deep learning (DL) is a very promising field in big data applications. Remote sensing often involves huge data volumes obtained daily by numerous in-orbit satellites. This makes it a perfect target area for data-driven applications. Nowadays, technological advances in terms of software and hardware have a noticeable impact on Earth observation applications, more specifically in remote sensing techniques and procedures, allowing for the acquisition of data sets with greater quality at higher acquisition ratios. This results in the collection of huge amounts of remotely sensed data, characterized by their large spatial resolution (in terms of the number of pixels per scene), and very high spectral dimensionality, with hundreds or even thousands of spectral bands. As a result, remote sensing instruments on spaceborne and airborne platforms are now generating data cubes with extremely high dimensionality, imposing several restrictions in terms of both processing runtimes and storage capacity. In this article, we provide a comprehensive review of the state of the art in DL for remote sensing data interpretation, analyzing the strengths and weaknesses of the most widely used techniques in the literature, as well as an exhaustive description of their parallel and distributed implementations (with a particular focus on those conducted using cloud computing systems). We also provide quantitative results, offering an assessment of a DL technique in a specific case study (source code available: https://github.com/mhaut/cloud-dnn-HSI). This article concludes with some remarks and hints about future challenges in the application of DL techniques to distributed remote sensing data interpretation problems. We emphasize the role of the cloud in providing a powerful architecture that is now able to manage vast amounts of remotely sensed data due to its implementation simplicity, low cost, and high efficiency compared to other parallel and distributed architectures, such as grid computing or dedicated clusters.

Vegetation structural complexity and biodiversity in the Great Smoky Mountains

AbstractVegetation structural complexity and biodiversity tend to be positively correlated, but understanding of this relationship is limited in part by structural metrics tending to quantify only horizontal or vertical variation, and that do not reflect internal structure. We developed new metrics for quantifying internal vegetation structural complexity using terrestrial LiDAR scanning and applied them to 12 NEON forest plots across an elevational gradient in Great Smoky Mountains National Park, USA. We asked (1) How do our newly developed structure metrics compare to traditional metrics? (2) How does forest structure vary with elevation in a high‐biodiversity, high topographic complexity region? (3) How do forest structural metrics vary in the strength of their relationships with vascular plant biodiversity? Our new measures of canopy density (Depth) and structural complexity (σDepth), and their canopy height‐normalized counterparts, were sensitive to structural variations and effectively summarized horizontal and vertical dimensions of structural complexity. Forest structure varied widely across plots spanning the elevational range of GRSM, with taller, more structurally complex forests at lower elevation. Vascular plant biodiversity was negatively correlated with elevation and more strongly positively correlated with vegetation structure variables. The strong correlations we observed between canopy structural complexity and biodiversity suggest that structural complexity metrics could be used to assay plant biodiversity over large areas in concert with airborne and spaceborne platforms.