Airborne Hyperspectral Sensor Research Articles

Genus Level Classification of the Mangroves in Indian Sundarbans using Sentinel-2 Multispectral Imagery

Abstract Mangroves are the most productive ecosystems that provide stabilization to the coastlines, help in carbon sequestration, reduce storm surges, defend the coastal inhabitants and play a role in sustaining the local economy. Mangroves are halophytic in nature that typically thrive along tropical and subtropical coastlines in the saline intertidal zone. This paper explores the potentiality of Sentinel-2 MSI Imagery in separating different mangrove genus that has been evaluated using different classification algorithms like Maximum Likelihood Classifier (MLC), Mahalanobis Distance (MD), Minimum Distance (MD), Spectral Angle Mapper (SAM), knowledge-based classifier such as Random Forest (RF) and Support Vector Machine (SVM). The estimated accuracy is higher for Random Forest (88.50%) followed by Support Vector Machine (85.30%) and Maximum Likelihood Classifier (85.10%) as compared to Mahalanobis Distance (81.10%), Spectral Angle Mapper (71.20%), and Minimum Distance (75.30%). The result showed that Sentinel-2 Multispectral Imagery can efficiently discriminate 15 different mangrove genera distributed in the entire Indian Sundarbans and can simultaneously replace the barriers in terms of cost and availability of the spaceborne and airborne hyperspectral sensors.

Combining a climate-permafrost model with fine resolution remote sensor products to quantify active-layer thickness at local scales

Quantification of active-layer thickness (ALT) over seasonally frozen terrains is critical to understand the impacts of climate warming on permafrost ecosystems in cold regions. Current large-scale process-based models cannot characterize the heterogeneous response of local landscapes to homogeneous climatic forcing. Here we linked a climate-permafrost model with a machine learning solution to indirectly quantify soil conditions reflected in the edaphic factor using high resolution remote sensor products, and then effectively estimated ALT across space and time down to local scales. Our nine-year field measurements during 2014–2022 and coincident high resolution airborne hyperspectral, lidar, and spaceborne sensor products provided a unique opportunity to test the developed protocol across two permafrost experiment stations in lowland terrains of Interior Alaska. Our developed model could explain over 60% of the variance of the field measured ALT for estimating the shallowest and deepest ALT in 2015 and 2019, suggesting the potential of the designed procedure for projecting local varying terrain response to long-term climate warming scenarios. This work will enhance the National Aeronautics and Space Administration’s Arctic-Boreal Vulnerability Experiment’s mission of combining field, airborne, and spaceborne sensor products to understand the coupling of permafrost ecosystems and climate change.

Estimation of water-infiltration rate in Mediterranean sandy soils using airborne hyperspectral sensors

The efficiency of spectral-based assessments of soil attributes using soil spectral libraries (SSLs) covering the visible–near-infrared–shortwave-infrared (VNIR–SWIR: 400–2500 nm) region has been proven in many studies. Nevertheless, as traditional SSLs are commonly developed under laboratory conditions, their application is limited for the assessment of soil surface-dependent properties such as water-infiltration rate (WIR) into the soil profile due to the sampling procedure. Currently, few studies are based on field SSLs for the prediction of physical soil properties. This study used a field-based protocol to measure soil reflectance data and WIR simultaneously in the field, and generate spectral-based decision tree models to predict WIR solely from field spectral measurements using the SoilPRO® assembly. The obtained models were applied to both airborne hyperspectral (HySpex) and satellite multispectral (Sentinel 2) data on a pixel-by-pixel basis to generate raster maps of WIR. The study areas were located in Macedonia (Greece), and were optimal for mapping WIR because the soil crust was well developed, and sites were characterized by bare soils (no vegetation coverage) with a sandy structure. Whereas the WIR map generated with the satellite data was poor due to the low spatial and spectral resolution of Sentinel 2 (20 m, 9 bands), the results obtained with the airborne hyperspectral HySpex sensor (5 m, 408 bands) were satisfactorily validated in the ground-truth stage with good prediction accuracy due to high spatial and spectral resolution. Validation accuracy of the HySpex observations using all field samples gave R2 = 0.68, whereas the predictions of the ground-truth samples that were not part of the calibration stage (field validation group) of the model gave R2 = 0.59. We concluded that these results are favourable for rapid estimation of soil surface conditions and pave the way for a wider spatial view from orbital hyperspectral remote-sensing sensors.

First evaluation of fire severity retrieval from PRISMA hyperspectral data

The unprecedented availability of spaceborne hyperspectral data has great potential to provide fire severity estimates that align with post-fire management needs, overcoming complex logistics and data acquisition costs of airborne hyperspectral sensors, and the suboptimal sensitivity of broadband data to several post-fire ground components. We analyzed the feasibility of the PRISMA mission -one of the first spaceborne spectrometers operationally active- to assess fire severity by leveraging hyperspectral data dimensionality through the retrieval of sub-pixel components directly related to fire severity in the field. Multispectral data provided by Sentinel-2, commonly used in fire severity quantitative assessments, were used as a benchmark method. Multiple endmember spectral mixture analysis (MESMA) was used to retrieve fractional cover of char, photosynthetic vegetation (PV), as well as non-photosynthetic vegetation and soil (NPVS) from post-fire PRISMA Level 2D and Sentinel-2 Level 2A scenes in one of the largest wildfires ever recorded in the western Mediterranean Basin. Ground-truth data were obtained using the Composite Burn Index (CBI) to procure three field-measured severity metrics: vegetation, soil and site. The relationship between the CBI data on a continuum scale and the cover of char, PV and NPVS image fractions retrieved from PRISMA and Sentinel-2 was assessed through Random Forest regression (RFR). Likewise, Ordinal Forests (OF) algorithm was used to classify categorized CBI data (low, moderate and high fire severity). PRISMA-based RFR fire severity estimates at vegetation, soil and site levels (R2 = 0.64–0.79 and RMSE = 0.33–0.41) outperformed those of Sentinel-2 (R2 = 0.27–0.53 and RMSE = 0.54–0.60), and were in line with previous studies using airborne hyperspectral sensors at higher spatial resolution. Fire severity underestimation for high field CBI values was almost unnoticeable in the PRISMA estimates. Categorical fire severity, not currently estimated using hyperspectral data but with high interest in post-fire management, were accurately predicted by PRISMA-based OF classification, with consistent user's and producer's accuracy for each fire severity category. The high confusion between moderate and low/high fire severity categories, typical when unmixing broadband multispectral data, was overcome by the PRISMA-based classification scheme. Our results suggest that new spaceborne spectrometer missions can support reliable fire severity assessments equivalent to airborne spectrometers, but readily applicable to large-scale assessments of extreme wildfire events.

Comparison of Methane Detection Using Shortwave and Longwave Infrared Hyperspectral Sensors Under Varying Environmental Conditions

Methane is a prevalent greenhouse gas with potent heat trapping capabilities, but methane emissions can be difficult to detect. Hyperspectral imagery is an effective method of detection which can be used to locate methane emission sources, as well as provide accountability for reaching emissions reduction goals. Because of methane's absorption features, both shortwave infrared (SWIR) and longwave infrared (LWIR) hyperspectral sensors have been used to accurately detect methane plumes. However, surface, environmental, and atmospheric background conditions can cause methane detectability to vary, and there have not been previous studies which evaluate this variability over a wide range of conditions. To assess this variation, this trade study compared methane detectability for two airborne hyperspectral sensors: AVIRIS-NG in the SWIR and HyTES in the LWIR. We modeled methane plume detection under a wide range of precisely known conditions by making use of synthetic images which were comprised of MODTRAN-generated radiance curves. We applied a spectral matched filter to these images to assess detection accuracy, and used these results to identify the conditions which have the most significant impact on detectability in the SWIR and LWIR. We then computed the specific boundaries on these conditions which make methane most detectable for each instrument; these novel results explore methane detectability over a broader range of conditions and sensors than previous studies. This trade study and methodology can aid decision making about which sensors are most useful for various types of methane emission analysis, such as leak detection and emission rate quantification.

Spectral mixture analysis of AVIRIS-NG hyperspectral data for material identification and classification for the part of Kolkata city

Identification and extraction of material in the urban scenario are always challenging due to the presence of different heterogeneous materials. Airborne hyperspectral sensors with the advantage of both high spectral and spatial resolution can be used in such areas for the identification and mapping of urban materials. Manual extraction of the endmembers in highly dense heterogeneous urban regions is a very difficult task to achieve, but with automated extraction techniques, it yields promising results. To counter the mixed pixels problem in heterogeneous urban areas automated endmember extraction techniques like Endmember Average RMS (EAR), Minimum Average Spectral Angle (MASA), and Count Based Endmember Selection (CoB) was used for pure endmember identification in the densely populated urban area of Kolkata region using AVIRIS NG imagery. Spectral Unmixing Multiple Endmember Spectral Mixture Analysis (MESMA), Linear Spectral Unmixing (LSU), and Mixture Tuned Matched Filtering (MTMF) was performed on AVIRIS NG data using the extracted endmembers of different urban materials. The rule-based classification was performed on abundance images obtained from MESMA, MTMF, and LSU techniques for comparative analysis. Classification using MESMA abundance images outperformed in mapping mixed urban environments with an overall accuracy (OA) of 88.6 % followed by the rule-based classified output of MTMF and LSU with OA of 75.27% and 72.45% respectively.

Assessment of crop traits retrieved from airborne hyperspectral and thermal remote sensing imagery to predict wheat grain protein content

Wheat (Triticum spp.) is among the world’s most widely grown crops and receives large quantities of nitrogen (N) fertiliser. Grain protein content (GPC) is influenced by genetic, agronomic and weather conditions affecting crops’ physiological status and stress levels; accurate GPC prediction has potential to reduce N losses and improve profit. Success in GPC estimation from remotely sensed plant traits has been limited. For progress to be made, it is necessary to robustly identify imaging spectroscopy-based physiological traits most closely associated with GPC in both experimental and commercial contexts. We present results from piloted hyperspectral flights and ground campaigns at two dryland field experiments with divergent water supply and wide-ranging N treatments, and from two years’ flights over 17 commercial fields planted to either bread (T. aestivum) or durum (T. durum) wheat, in the southern Australian wheat belt. Imagery was acquired with airborne hyperspectral and thermal sensors, with spatial resolutions of approx. 0.3 m and 0.5 m for experimental plots and 1 m/1.7 m in commercial fields. Leaf clip measurements, leaf and grain samples were collected and, in commercial fields, ∼40,000 records obtained from harvester-mounted protein monitors. Crop Water Stress Index (CWSI), solar-induced fluorescence (SIF), reflectance indices and PRO4SAIL radiative transfer model inverted parameters were retrieved for each plot and GPC record location. The photochemical reflectance index (PRI) related to xanthophyll pigments was consistently associated with GPC at both leaf and canopy scale in the plots and transect. In the commercial crops, a gradient boosted machine learning algorithm (GBM) ranked CWSI as the strongest indicator of GPC under severe water stress, while SIF, PRI and inverted biochemical constituents anthocyanins and carotenoids were consistently important under more benign conditions. Structural parameters inverted from the hyperspectral reflectance imagery were not prominent except under severe drought. We attained statistically significant results estimating GPC in unseen samples, with best relationships between predicted and observed GPC of r2 = 0.80 in a model built with thermal and physiological traits obtained from the hyperspectral and thermal imagery.

Soil clay minerals abundance mapping using AVIRIS-NG data

Advanced hyperspectral remote sensing technology can identify various clay minerals in the soil. The improved spectral and spatial resolution of the airborne hyperspectral sensor can be used for a better interpretation of clay mineral abundance in the soil. This research utilizes the high-resolution image data acquired from Advanced Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) on an airborne platform to map the abundance of clay minerals in the Udaipur region of Rajasthan (24° 34′ 16.5720′' N, 73° 41′ 29.5584′' E). The representative soil samples for various soil clay minerals, e.g., kaolinite, montmorillonite, and illite, were classified from AVIRIS-NG data using a spectral feature fitting algorithm. A total of thirty sites were selected from agricultural and wastelands for soil sample collection. X-ray diffraction (XRD) analysis of representative soil samples was carried out to find the percentage and type of clay minerals present. Regression analysis between absorption peak depth values estimated from the hyperspectral image of the sampling sites in the spectral region from 2205 to 2214 nm and corresponding clay content value showed a significant relationship. The regression line obtained for the known pixels was used to prepare the mineral abundance map over the study area. This study significantly indicates the dominance of montmorillonite clay mineral over kaolinite and illite in the soils of the Udaipur region.

CO2Flux Model Assessment and Comparison between an Airborne Hyperspectral Sensor and Orbital Multispectral Imagery in Southern Amazonia

In environmental research, remote sensing techniques are mostly based on orbital data, which are characterized by limited acquisition and often poor spectral and spatial resolutions in relation to suborbital sensors. This reflects on carbon patterns, where orbital remote sensing bears devoted sensor systems for CO2 monitoring, even though carbon observations are performed with natural resources systems, such as Landsat, supported by spectral models such as CO2Flux adapted to multispectral imagery. Based on the considerations above, we have compared the CO2Flux model by using four different imagery systems (Landsat 8, PlanetScope, Sentinel-2, and AisaFenix) in the northern part of the state of Mato Grosso, southern Brazilian Amazonia. The study area covers three different land uses, which are primary tropical forest, bare soil, and pasture. After the atmospheric correction and radiometric calibration, the scenes were resampled to 30 m of spatial resolution, seeking for a parametrized comparison of CO2Flux, as well as NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index). The results obtained here suggest that PlanetScope, MSI/Sentinel-2, OLI/Landsat-8, and AisaFENIX can be similarly scaled, that is, the data variability along a heterogeneous scene in evergreen tropical forest is similar. We highlight that the spatial-temporal dynamics of rainfall seasonality relation to CO2 emission and uptake should be assessed in future research. Our results provide a better understanding on how the merge and/or combination of different airborne and orbital datasets that can provide reliable estimates of carbon emission and absorption within different terrestrial ecosystems in southern Amazonia.

Mapping phosphorus concentration in Mediterranean forests using different remote-sensing methods

ABSTRACT Mineral nutrition is essential for optimal plant growth. Phosphorus (P) is a relatively small component of leaf dry weight, with a concentration in plant foliage of less than 1%. Despite its low concentration, P is an essential element in plants, mainly used for energy transfer. Mapping P concentration using traditional methods is expensive and usually limited to a small area; it is time-consuming and covers only a few plant individuals or species. In this study, we demonstrate the use of remote-sensing (RS) data acquired from the feld and airborne hyperspectral sensors to predict and map the P concentration in leaves of different woody Mediterranean plant species. Comprehensive field work included leaf sampling, laboratory analyses, and spectral measurements using a visible, near-infrared and shortwave-infrared (VIS–NIR–SWIR) field spectrometer. Using different spectral configurations, we built accurate models to predict P concentration in leaf samples. The models were built using a NIR data analysis technique with the data mining software PARACUDA II. This software allowed us to identify the correlative wavelengths for P-bearing molecules in selected woody Mediterranean plant species. The hyperspectral-based model for leaf P concentration was extracted from the reflectance data acquired using a manned aircraft carrying a hyperspectral sensor (Specim AisaFenix 1 K). The model gave a reliable correlation between points extracted from the hyperspectral image and samples measured in the field. We believe that the methodology used in this study will help forest ecologists better understand the concentration of P in the foliage of woody Mediterranean plant species.

Semi-supervised DNN regression on airborne hyperspectral imagery for improved spatial soil properties prediction

A number of algorithms have been developed for soil organic matter (SOM) or soil heavy metal detection in airborne hyperspectral imagery with high spatial and spectral resolutions. However, to achieve improved land management, the problems of the inconsistent features and low accuracy still need to be solved. In this paper, we propose a novel regression model to estimate the concentrations of SOM, arsenic (As), and chromium (Cr) in soil. Firstly, a hyperspectral unmixing technique is utilized to extract the bare soil pixels. We then combine the absorption depth feature after continuum removal, the original absorption feature, the band ratio feature, and the first-order differential feature, to form a set of features for parameter inversion. To solve the over-fitting problem caused by the small number of samples and the weak expression problem, the semi-supervised deep neural network regression (Semi-DNNR) model is introduced. The experimental were conducted using several datasets collected by HyMap, which is an airborne hyperspectral imaging sensor in VNIR-SWIR spectral range in Yitong county, Jilin province, China. The proposed Semi-DNNR model shows a good performance in this study, with the prediction Rp2 values for SOM, As, and Cr being 0.71, 0.82, and 0.63, respectively. After the spatial distribution map of the soil components of the study area was overlaid with the stream network, which was obtained from the digital elevation model (DEM). It was found that snowmelt, the melting of frozen soil, and surface rainfall can transport SOM to low-lying areas. A similar phenomenon was also observed for As, due to SOM adsorption and dissolved organic matter (DOM) complexation. A comparison of the proposed method with both feature selection methods (competitive adaptive reweighted sampling (CARS), genetic algorithm (GA)) and regression methods (partial least squares regression (PLSR), support vector regression (SVR)) shows that the proposed feature selection method is more robust than the CARS and GA methods. The proposed Semi-DNNR model was found to be at least 18.80% higher in prediction accuracy for As than the SVR or PLSR methods, at least 25.71% higher for Cr, and at least 19.73% higher for SOM.

An evaluation of airborne SWIR imaging spectrometers for CH4 mapping: Implications of band positioning, spectral sampling and noise

The development of instruments and methods to assist in methane (CH4) emissions mapping is essential to the fossil fuel industry. Early identification of local CH4 sources can benefit both petroleum hydrocarbon prospecting and environmental monitoring. Generic airborne imaging spectrometers operating in the shortwave infrared (SWIR) wavelength range (1000−2400 nm) have shown their suitability for this task. However, to date, there is no airborne scanner specifically designed for the detection of CH4 plumes. To overcome current handicaps and achieve better results at local scale, further investigation in sensor design is needed to evaluate which components can be adjusted to improve CH4 mapping with airborne sensors. Here, we focus on the evaluation of currently operational airborne imaging spectrometers for CH4 mapping in the SWIR range. Data acquired over areas with known CH4 emissions by scientific and industry-grade airborne hyperspectral sensors were examined. The research was conducted in three steps: analysis of sensor design, image processing and noise simulation. In the first step, differences in the spectral sampling between the sensors were analyzed. A reference CH4 signature from HITRAN spectral database was resampled to the spectral sampling of each airborne sensor. The center wavelength of diagnostic CH4 absorption features (identified in the convolved signatures) in relation to the position of band centers from each equipment was examined. For the image processing stage, a new CH4 index and a classic matched filtering were used to map CH4 plumes. To assess the impact of the signal-to-noise ratio (SNR) of the airborne sensors on CH4 plume mapping, white noise was added to the data to simulate images with varying SNR levels. Results demonstrated that the wavelength position of band centers is a key for CH4 mapping. The CH4 plumes could be mapped only with scientific-grade sensors, in which the band centers were closer to the center of CH4 features. Simulations with the addition of random noise demonstrated that a noisier signal is probably the reason why the industry-grade sensor tested here failed to map CH4 plumes, given that all instruments have a comparable spectral sampling. Furthermore, the simulations also demonstrated that the density of the plume has also a weight on the mapping of CH4 sources, once the image that captured the densest plume requested a higher addition of noise to be lost. The overall investigation indicates that a hyperspectral airborne sensor with bands properly positioned and scientific-grade SNR would better resolve the narrow CH4 features in the SWIR range.

Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture

Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming. In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features. Due to limited accessibility outside of the scientific community, hyperspectral images have not been widely used in precision agriculture. In recent years, different mini-sized and low-cost airborne hyperspectral sensors (e.g., Headwall Micro-Hyperspec, Cubert UHD 185-Firefly) have been developed, and advanced spaceborne hyperspectral sensors have also been or will be launched (e.g., PRISMA, DESIS, EnMAP, HyspIRI). Hyperspectral imaging is becoming more widely available to agricultural applications. Meanwhile, the acquisition, processing, and analysis of hyperspectral imagery still remain a challenging research topic (e.g., large data volume, high data dimensionality, and complex information analysis). It is hence beneficial to conduct a thorough and in-depth review of the hyperspectral imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing hyperspectral information, and recent advances of hyperspectral imaging in agricultural applications. Publications over the past 30 years in hyperspectral imaging technology and applications in agriculture were thus reviewed. The imaging platforms and sensors, together with analytic methods used in the literature, were discussed. Performances of hyperspectral imaging for different applications (e.g., crop biophysical and biochemical properties’ mapping, soil characteristics, and crop classification) were also evaluated. This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology. Recommendations for future hyperspectral imaging research for precision agriculture are also presented.

CWV-Net: A Deep Neural Network for Atmospheric Column Water Vapor Retrieval From Hyperspectral VNIR Data

Estimation of the total column water vapor (CWV) content of the atmosphere plays an important role in the atmospheric compensation (AC) of remotely sensed hyperspectral images collected in the visible and near infrared (VNIR) spectral range. Most of the proposed CWV retrieval methods provide accurate estimates as long as other significant atmospheric parameters are known. Those parameters are not generally available and must in turn be estimated. In this article, a new approach based on deep learning is proposed that allows the estimation of CWV without the knowledge of the atmospheric visibility, the solar zenith angle, and the atmospheric point spread function (PSF). The proposed approach includes a training strategy based on synthetic data that are generated according to an accurate radiative-transfer model, and by exploiting reflectance spectral libraries and the MODTRAN radiative-transfer code. Experiments on simulated data are carried out to analyze the performance of the proposed deep neural network with reference to both aerial and satellite applications. Furthermore, an example of the results provided by the network in a real application is shown. For this purpose, the network is applied to data acquired by an airborne hyperspectral sensor operating in the VNIR spectral range.

Sensor-based evaluation of maize (Zea mays) and weed response to post-emergence herbicide applications of Isoxaflutole and Cyprosulfamide applied as crop seed treatment or herbicide mixing partner.

Some maize post-emergence herbicides obtain their crop/weed selectivity only through the use of chemical crop safeners. Safeners improve the tolerance of maize to herbicidal active ingredients. In order to investigate the crop response to safener (cyprosulfamide) spray application and seed treatment, greenhouse and field trials were conducted on three maize development stages (2-, 4-, and 6-leaf stage). Visual estimations on crop vitality were compared to ground-based and airborne hyperspectral and multispectral sensors. The reduction of cyprosulfamide by 88% when applied as seed treatment did not significantly reduce maize biomass yields at the field. The crop deterioration in both trials was stronger in the cyprosulfamide seed treatments compared to the spray applications but was found to be transient in the field trial. The hyperspectral sensor and multispectral camera data correlated with R2 = 0.84 (CropSpec Vegetation Index) and R2 = 0.64 (Green Normalized Difference Vegetation Index). The sensor-based collection of crop responses to treatments enables early, quantifiable and auditor-independent assessments. In particular, the airborne multispectral imagery assessment of field experiments provides more detailed and comprehensive information than visually collected data. © 2019 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Airborne Hyperspectral Imaging for Submerged Archaeological Mapping in Shallow Water Environments

Nearshore areas around the world contain a wide variety of archeological structures, including prehistoric remains submerged by sea level rise during the Holocene glacial retreat. While natural processes, such as erosion, rising sea level, and exceptional climatic events have always threatened the integrity of this submerged cultural heritage, the importance of protecting them is becoming increasingly critical with the expanding effects of global climate change and human activities. Aerial archaeology, as a non-invasive technique, contributes greatly to documentation of archaeological remains. In an underwater context, the difficulty of crossing the water column to reach the bottom and its potential archaeological information usually requires active remote-sensing technologies such as airborne LiDAR bathymetry or ship-borne acoustic soundings. More recently, airborne hyperspectral passive sensors have shown potential for accessing water-bottom information in shallow water environments. While hyperspectral imagery has been assessed in terrestrial continental archaeological contexts, this study brings new perspectives for documenting submerged archaeological structures using airborne hyperspectral remote sensing. Airborne hyperspectral data were recorded in the Visible Near Infra-Red (VNIR) spectral range (400–1000 nm) over the submerged megalithic site of Er Lannic (Morbihan, France). The method used to process these data included (i) visualization of submerged anomalous features using a minimum noise fraction transform, (ii) automatic detection of these features using Isolation Forest and the Reed–Xiaoli detector and (iii) morphological and spectral analysis of archaeological structures from water-depth and water-bottom reflectance derived from the inversion of a radiative transfer model of the water column. The results, compared to archaeological reference data collected from in-situ archaeological surveys, showed for the first time the potential of airborne hyperspectral imagery for archaeological mapping in complex shallow water environments.

Detection of heavy hydrocarbon plumes (Ethane, propane and Butane) using airborne longwave (7.6–13.5 μm) infrared hyperspectral data

Recent developments in the detection of gaseous hydrocarbons (HCs) through remote sensing are focused on the characterization of methane (CH4). Basically, there is no research related to remote sensing of other heavier HC gases (ethane: C2H6, propane: C3H8, butane: C4H10), whether derived from natural seeps or from leaks at various stages of production, transportation and storage of petroleum resources. Just as methane; ethane, propane and butane have typical spectral signatures in the longwave infrared region (7.6–13.5 μm), which potentiates their detection by means of airborne hyperspectral sensors (nadir or oblique measurements) or by fixed, ground-based hyperspectral and ultraspectral sensors (horizontal/profile measurements). In this context, the aim of this work is to evaluate the detection limits of the Spatially-Enhanced Broadband Array Spectrograph System (SEBASS) for the identification of ethane, propane and butane in gaseous emissions under controlled leaks. Due to the low content of heavy HCs (C2-C4) in the tested mixtures, they were only identified in plumes derived from sources with emission rates equal to or greater than 1000 standard cubic feet per hour (scf/h). Despite the complex mixture comprised in the plume, the spectral absorption features of each HC allowed their individualization in the SEBASS imagery. The methodology introduced here demonstrates that airborne hyperspectral thermal sensors can identify the different types of HC gases composing the plume, strengthening the possibilities for the evaluation of gas leaks from petroleum installations and natural seeps, assisting in exploration campaigns and diminishing the risk for technological accidents in industrial plants.

Mapping salt marsh soil properties using imaging spectroscopy

Tidal salt marshes sequester and store blue carbon at both short and long time scales. Marsh soils shape and maintain the ecosystem by supporting complex biogeochemical reactions, deposition of sediment, and accumulation of organic matter. In this study, we examined the potential of imaging spectroscopy techniques to indirectly quantify and map tidal marsh soil properties at a National Estuarine Research Reserve in Georgia, USA. A framework was developed to combine modern digital image processing techniques for marsh soil mapping, including object-based image analysis (OBIA), machine learning modeling, and ensemble analysis. We also evaluated the efficacy of airborne hyperspectral sensors in estimating marsh soil properties compared to spaceborne multispectral sensors, WorldView-2 and QuickBird. The pros and cons of object-based modeling and mapping were assessed and compared with traditional pixel-based mapping methods. The results showed that the designed framework was effective in quantifying and mapping three marsh soil properties using the composite reflectance from salt marsh environment: soil salinity, soil water content, and soil organic matter content. Multispectral sensors were successful in quantifying soil salinity and soil water content but failed to model soil organic matter. The study also demonstrated the value of minimum noise fraction transformation and ensemble analysis techniques for marsh soil mapping. The results suggest that imaging spectroscopy based modeling is a promising tool to quantify and map marsh soil properties at a local scale, and is a potential alternative to traditional soil data acquisition to support carbon cycle research and the conservation and restoration of tidal marshes.

Exploring correlations between hyper-spectral signatures acquired in the laboratory and in-situ observation for heavy metal concentrations in soil

This study is a preliminary study for monitoring of heavy metal pollution in soil using airborne hyperspectral sensor. This paper presents an approach to exploring the correlation between the spectrum of heavy metals in soil from hyperspectral imagery taken in the laboratory and its observed concentration. In order to identify spectral wavelengths with a high correlation coefficient, hyperspectral data were collected from soil containing various concentrations (10–5000 ppm) of As, Cd, Cu, and Pb. Correlation analysis was performed with the hyperspectral imagery between the spectrum and its observed concentration obtained from instrumental analysis. The selective spectral wavelengths with a correlation coefficient (R2) above 0.80 will make a major contribution to study and monitoring of heavy metal pollution in soil using airborne hyperspectral sensor in the future. In order to accomplish desired result, further investigation is required.

Hyperspectral Data: Efficient and Secure Transmission

Airborne and spaceborne hyperspectral sensors collect information which is derived from the electromagnetic spectrum of an observed area. Hyperspectral data are used in several studies and they are an important aid in different real-life applications (e.g., mining and geology applications, ecology, surveillance, etc.). A hyperspectral image has a three-dimensional structure (a sort of datacube): it can be considered as a sequence of narrow and contiguous spectral channels (bands). The objective of this paper is to present a framework permits the efficient storage/transmission of an input hyperspectral image, and its protection. The proposed framework relies on a reversible invisible watermarking scheme and an efficient lossless compression algorithm. The reversible watermarking scheme is used in conjunction with digital signature techniques in order to permit the verification of the integrity of a hyperspectral image by the receiver.