Search Results (173)

Plastic waste has become a critical environmental issue, necessitating effective methods for detection and monitoring. This article presents a machine-learning-based methodology and embedded solution to detect plastic waste in the environment using an airborne hyperspectral sensor operating in the short-wave infrared (SWIR) band. Experimental data were obtained from drone flights in several case studies in natural and controlled environments. Data were preprocessed to simply equalize the spectra across the whole band and across different environmental conditions, and machine learning techniques were applied to detect plastics even in real-time. Several algorithms for spectrum calibration, feature selection, and classification were optimized and compared to obtain an optimal solution that has high-quality results under cross-validation. This way, deploying the system in different environments without requiring complicated manual adjustments or re-learning is possible. The results of this work prove the feasibility of the proposed plastic litter detection approach using high-definition aerial remote sensing, with high specificity to plastic polymers that are not obtained using visible and NIR data. Full article

The ongoing advancement of unmanned aerial vehicles (UAVs) and the evolution of small-scale cameras have bridged the gap between traditional ground-based surveys and orbital sensors. However, these systems present challenges, including limited coverage area, image stabilization constraints, and complex image processing. In water quality monitoring, these difficulties are further compounded by sun glint effects, which hinder the construction of accurate orthomosaics in homogeneous water surfaces and affect radiometric accuracy. This study focuses on evaluating these challenges by comparing two distinct airborne imaging platforms with different spectral resolutions, emphasizing Total Suspended Solids (TSS) monitoring. Hyperspectral airborne surveys were undertaken utilizing a pushbroom system comprising 276 bands, whereas multispectral airborne surveys were conducted employing a global shutter frame with 4 bands. Fifteen aerial survey campaigns were carried out over water bodies from two biomes in Brazil (Amazon and Savanna), at varying concentrations of TSS (0.6–130.7 mg L⁻¹, N: 53). Empirical models using near-infrared channels were applied to accurately monitor TSS in all areas (Hyperspectral camera—RMSE = 3.6 mg L⁻¹, Multispectral camera—RMSE = 9.8 mg L⁻¹). Furthermore, a key contribution of this research is the development and application of Sun Glint mitigation techniques, which significantly improve the reliability of airborne reflectance measurements. By addressing these radiometric challenges, this study provides critical insights into the optimal UAV platform for TSS monitoring in inland waters, enhancing the accuracy and applicability of airborne remote sensing in aquatic environments. Full article

Remote sensing from unoccupied aerial systems (UASs) has witnessed exponential growth. The increasing use of imaging spectroscopy sensors and RGB cameras on UAS platforms demands accurate, cross-comparable multi-sensor data. Inherent errors during image capture or processing can introduce spatial offsets, diminishing spatial accuracy and hindering cross-comparison and change detection analysis. To address this, we demonstrate the use of an optical flow algorithm, eFOLKI, for co-registering imagery from two pushbroom imaging spectroscopy sensors (VNIR and NIR/SWIR) to an RGB orthomosaic. Our study focuses on two ecologically diverse vegetative sites in Tasmania, Australia. Both sites are structurally complex, posing challenging datasets for co-registration algorithms with initial georectification spatial errors of up to 9 m planimetrically. The optical flow co-registration significantly improved the spatial accuracy of the imaging spectroscopy relative to the RGB orthomosaic. After co-registration, spatial alignment errors were greatly improved, with RMSE and MAE values of less than 13 cm for the higher-spatial-resolution dataset and less than 33 cm for the lower resolution dataset, corresponding to only 2–4 pixels in both cases. These results demonstrate the efficacy of optical flow co-registration in reducing spatial discrepancies between multi-sensor UAS datasets, enhancing accuracy and alignment to enable robust environmental monitoring. Full article

Trees are indispensable to ecosystems, yet mortality rates have been increasing due to the abnormal changes in forest growth environments caused by frequent extreme weather events associated with global climate warming. Consequently, the need to monitor, assess, and predict tree mortality has become increasingly urgent to better address climate change and protect forest ecosystems. Over the past few decades, remote sensing has been widely applied to vegetation mortality observation due to its significant advantages. Here, we reviewed and analyzed the major research advancements in the application of remote sensing for tree mortality monitoring, using the Web of Science Core Collection database, covering the period from 1998 to the first half of 2024. We comprehensively summarized the use of different platforms (satellite and UAV) for data acquisition, the application of various sensors (multispectral, hyperspectral, and radar) as image data sources, the primary indicators, the classification models used in monitoring tree mortality, and the influence of tree mortality. Our findings indicated that satellite-based optical remote sensing data were the primary data source for tree mortality monitoring, accounting for 80% of existing studies. Time-series optical remote sensing data have emerged as a crucial direction for enhancing the accuracy of vegetation mortality monitoring. In recent years, studies utilizing airborne LiDAR have shown an increasing trend, accounting for 48% of UAV-based research. NDVI was the most commonly used remote sensing indicator, and most studies incorporated meteorological and climatic factors as environmental variables. Machine learning was increasingly favored for remote sensing data analysis, with Random Forest being the most widely used classification model. People are more focused on the impacts of tree mortality on water and carbon. Finally, we discussed the challenges in monitoring and evaluating tree mortality through remote sensing and offered perspectives for future developments. Full article

The identification of maritime objects is crucial for ensuring navigational safety, enabling effective environmental monitoring, and facilitating efficient maritime search and rescue operations. Given its ability to provide detailed spectral information, hyperspectral imaging has emerged as a powerful tool for analyzing the physical and chemical properties of target objects. This study proposes a novel maritime object identification framework that integrates hyperspectral imaging with machine learning models. Hyperspectral data from six ports in South Korea were collected using airborne sensors and subsequently processed into spectral statistics and RGB images. The processed data were then analyzed using classifier and convolutional neural network (CNN) models. The results obtained in this study show that CNN models achieved an average test accuracy of 90%, outperforming classifier models, which achieved 83%. Among the CNN models, EfficientNet B0 and Inception V3 demonstrated the best performance, with Inception V3 achieving a category-specific accuracy of 97% when weights were excluded. This study presents a robust and efficient framework for marine surveillance utilizing hyperspectral imaging and machine learning, offering significant potential for advancing marine detection and monitoring technologies. Full article

Detecting targets in remote sensing imagery, particularly when identifying sparsely distributed materials, is crucial for applications such as defense, mineral exploration, agriculture, and environmental monitoring. The effectiveness of detection and the precision of the results are influenced by several factors, including sensor configurations, platform properties, interactions between targets and their background, and the spectral contrast of the targets. Environmental factors, such as atmospheric conditions, also play a significant role. Conventionally, target detection in remote sensing has relied on statistical methods that typically assume a linear process for image formation. However, to enhance detection performance, it is critical to account for the geometric and spectral variabilities across multiple imaging platforms. In this research, we conducted a comprehensive target detection experiment using a unique benchmark multi-platform hyperspectral dataset, where man-made targets were deployed on various surface backgrounds. Data were collected using a hand-held spectroradiometer, UAV-mounted hyperspectral sensors, and airborne platforms, all within a half-hour time window. Multi-spectral space-based sensors (i.e., Worldview and Landsat) also flew over the scene and collected data. The experiment took place on 23 July 2021, at the Rochester Institute of Technology’s Tait Preserve in Penfield, NY, USA. We validated the detection outcomes through receiver operating characteristic (ROC) curves and spectral similarity metrics across various detection algorithms and imaging platforms. This multi-platform analysis provides critical insights into the challenges of hyperspectral target detection in complex, real-world landscapes, demonstrating the influence of platform variability on detection performance and the necessity for robust algorithmic approaches in multi-source data integration. Full article

Restored wetlands should be closely monitored to fully evaluate the effectiveness of restoration efforts. However, regular post-restoration monitoring can be time-consuming and expensive, and is often absent or inadequate. Satellite and airborne remote sensing systems have proven to be cost-effective tools in many fields, but they have not been widely used to monitor ecological restoration. This study assessed the potential of airborne hyperspectral remote sensing to monitor water mass characteristics of experimental temporary ponds in the Mediterranean region. These ponds were created during marsh restoration in Doñana National Park (south-west Spain). We used hyperspectral images acquired by the CASI-1500 hyperspectral airborne sensor to estimate and map water depth, turbidity and chlorophyll a in a subset of the 96 new ponds. The high spatial and spectral resolution of the CASI sensor allowed us to detect differences between ponds in water depth, turbidity and chlorophyll a, providing accurate mapping of these three variables, and a useful method to assess restoration success. High levels of spatial variation were recorded between different ponds, which likely generates high diversity in the animal and plant species that they contain. These results highlight the great potential of hyperspectral sensors for the long-term monitoring of wetland complexes in the Mediterranean region and elsewhere. Full article

On a global scale, wetlands are suffering from a steady decline in surface area and environmental quality. Protecting them is essential and requires a careful spatialisation of their natural habitats. Traditionally, in our study area, species discrimination for floristic mapping has been achieved through on-site field inventories, but this approach is very time-consuming in these difficult-to-access environments. Usually, the resulting maps are also not spatially exhaustive and are not frequently updated. In this paper, we propose to establish a complete map of the study area using remote sensors and set up a long-term and regular observatory of environmental changes to monitor the evolution of a major French wetland. This methodology combines three dataset acquisition technologies, airborne hyperspectral and WorldView-3 multispectral images, supplemented by LiDAR images, which we compared to evaluate the difference in performances. To do so, we applied the Random Forest supervised classification methods using ground reference areas and compared the out-of-bag score (OOB score) as well as the matrix of confusion resulting from each dataset. Thirteen habitats were discriminated at level 4 of the European Nature Information System (EUNIS) typology, at a spatial resolution of around 1.2 m. We first show that a multispectral image with 19 variables produces results which are almost as good as those produced by a hyperspectral image with 58 variables. The experiment with different features also demonstrates that the use of four bands derived from LiDAR datasets can improve the quality of the classification. Invasive alien species Ludwigia grandiflora and Crassula helmsii were also detected without error which is very interesting when applied to these endangered environments. Therefore, since WV-3 images provide very good results and are easier to acquire than airborne hyperspectral data, we propose to use them going forward for the regular observation of the Brière marshes habitat we initiated. Full article

Optical satellite data products (e.g., Sentinel-2, PlanetScope, Landsat) require proper validation across diverse ecosystems. This has conventionally been achieved using airborne and more recently unmanned aerial vehicle (UAV) based hyperspectral sensors which constrain operations by both their cost and complexity of use. The MicaSense Altum is an accessible multispectral sensor that integrates a radiometric thermal camera with 5 bands (475 nm–840 nm). In this work we assess the spectral reflectance accuracy of a UAV-mounted MicaSense Altum at 25, 50, 75, and 100 m AGL flight altitudes using the manufacturer provided panel-based reflectance conversion technique for atmospheric correction at the Mer Bleue peatland supersite near Ottawa, Canada. Altum derived spectral reflectance was evaluated through comparison of measurements of six known nominal reflectance calibration panels to in situ spectroradiometer and hyperspectral UAV reflectance products. We found that the Altum sensor saturates in the 475 nm band viewing the 18% reflectance panel, and for all brighter panels for the 475, 560, and 668 nm bands. The Altum was assessed against pre-classified hummock-hollow-lawn microtopographic features using band level pair-wise comparisons and common vegetation indices to investigate the sensor’s viability as a validation tool of PlanetScope Dove 8 band and Sentinel-2A satellite products. We conclude that the use of the Altum needs careful consideration, and its field deployment and reflectance output does not meet the necessary cal/val requirements in the peatland site. Full article

The new generation of satellite hyperspectral (HS) sensors provides remarkable potential for regional-scale mineralogical mapping. However, as with any satellite sensor, mapping results are dependent on a typically complex correction procedure needed to remove atmospheric, topographic and geometric distortions before accurate reflectance spectra can be retrieved. These are typically applied by the satellite operators but use different approaches that can yield different results. In this study, we conduct a comparative analysis of PRISMA, EnMAP, and EMIT hyperspectral satellite data, alongside airborne data acquired by the HyMap sensor, to investigate the consistency between these datasets and their suitability for geological mapping. Two sites in Namibia were selected for this comparison, the Marinkas-Quellen and Epembe carbonatite complexes, based on their geological significance, relatively good exposure, arid climate and data availability. We conducted qualitative and three different quantitative comparisons of the hyperspectral data from these sites. These included correlative comparisons of (1) the reflectance values across the visible-near infrared (VNIR) to shortwave infrared (SWIR) spectral ranges, (2) established spectral indices sensitive to minerals we expect in each of the scenes, and (3) spectral abundances estimated using linear unmixing. The results highlighted a notable shift in inter-sensor consistency between the VNIR and SWIR spectral ranges, with the VNIR range being more similar between the compared sensors than the SWIR. Our qualitative comparisons suggest that the SWIR spectra from the EnMAP and EMIT sensors are the most interpretable (show the most distinct absorption features) but that latent features (i.e., endmember abundances) from the HyMap and PRISMA sensors are consistent with geological variations. We conclude that our results reinforce the need for accurate radiometric and topographic corrections, especially for the SWIR range most commonly used for geological mapping. Full article

The launch of the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the Surface Biology and Geology (SBG) satellite sensors will provide increased spectral resolution compared to existing platforms. These new sensors will require robust calibration and validation datasets, but existing field-based instrumentation is limited in its availability and potential for geographic coverage, particularly for coastal and inland waters, where optical complexity is substantially greater than in the open ocean. The minimum signal-to-noise ratio (SNR) is an important metric for assessing the reliability of derived biogeochemical products and their subsequent use as proxies, such as for biomass, in aquatic systems. The SNR can provide insight into whether legacy sensors can be used for algorithm development as well as calibration and validation activities for next-generation platforms. We extend our previous evaluation of SNR and associated uncertainties for representative coastal and inland targets to include the imaging sensors PRISM and AVIRIS-NG, the airborne-deployed C-AIR radiometers, and the shipboard HydroRad and HyperSAS radiometers, which were not included in the original analysis. Nearly all the assessed hyperspectral sensors fail to meet proposed criteria for SNR or uncertainty in remote sensing reflectance (R_rs) for some part of the spectrum, with the most common failures (>20% uncertainty) below 400 nm, but all the sensors were below the proposed 17.5% uncertainty for derived chlorophyll-a. Instrument suites for both in-water and airborne platforms that are capable of exceeding all the proposed thresholds for SNR and R_rs uncertainty are commercially available. Thus, there is a straightforward path to obtaining calibration and validation data for current and next-generation sensors, but the availability of suitable high spectral resolution sensors is limited. Full article

The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. Therefore, current soil property estimation by remote sensing is limited to bare soil pixels, which are identified based on spectral indices of vegetation. Our study proposes a composite mapping approach to extend the soil properties mapping beyond bare soil pixels, associated with an uncertainty map. The proposed approach first classified the pixels based on their bare soil fractional cover by spectral unmixing. Then, a specific regression model was built and applied to each bare soil fractional cover class to estimate clay content. Finally, the clay content maps created for each bare soil fractional cover class were mosaicked to create a composite map of clay content estimations. A bootstrap procedure was used to estimate the standard deviation of clay content predictions per bare soil fractional cover dataset, which represented the uncertainty of estimations. This study used a hyperspectral image acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over cultivated fields in South India. The proposed approach provided modest performances in prediction ($R_{val}^2$ ranging from 0.53 to 0.63) depending on the bare soil fractional cover class and showed a correct spatial pattern, regardless of the bare soil fraction classes. The model’s performance was observed to increase with the adoption of higher bare soil fractional cover thresholds. The mapped area ranged from 10.4% for pixels with bare soil fractional cover >0.7 to 52.7% for pixels with bare soil fractional cover >0.3. The approach thus extended the mapped surface by 42.4%, while maintaining acceptable prediction performances. Finally, the proposed approach could be adopted to extend the mapping capability of planned and current hyperspectral satellite missions. Full article

Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH₄) spectral features, including the ν1 vibrational band near 1300 cm⁻¹ (7.7 μm); therefore, they can be used to monitor CH₄ concentrations in the atmosphere. However, retrieving CH₄ remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH₄ is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH₄ data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH₄ profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH₄ signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements. An inter-comparison study revealed that the spectral fingerprinting results can capture the vertical variation characteristics of CH₄ profiles that operational sounder products may not provide. The latitudinal variations in CH₄ concentration in these results appear more realistic than those shown in existing sounder products. The methodology presented herein could enhance the utilization of satellite data to comprehend methane’s role as a greenhouse gas and facilitate the tracking of methane sources and sinks with increased reliability. Full article

At present, it is critical to accurately monitor wheat crops to help decision-making processes in precision agriculture. This research aims to retrieve various wheat crop traits from hyperspectral data using machine learning regression algorithms (MLRAs) and dimensionality reduction (DR) techniques. This experiment was conducted in an agricultural field in Arborea, Oristano-Sardinia, Italy, with different factors such as cultivars, N-treatments, and soil ploughing conditions. Hyperspectral data were acquired on the ground using a full-range Spectral Evolution spectrometer (350–2500 nm). Four DR techniques, including (i) variable influence on projection (VIP), (ii) principal component analysis (PCA), (iii) vegetation indices (VIs), and (iv) spectroscopic feature (SF) calculation, were undertaken to reduce the dimension of the hyperspectral data while maintaining the information content. We used five MLRA models, including (i) partial least squares regression (PLSR), (ii) random forest (RF), (iii) support vector regression (SVR), (iv) Gaussian process regression (GPR), and (v) neural network (NN), to retrieve wheat traits at either leaf and canopy levels. The studied traits were leaf area index (LAI), leaf and canopy water content (LWC and CWC), leaf and canopy chlorophyll content (LCC and CCC), and leaf and canopy nitrogen content (LNC and CNC). MLRA models were able to accurately retrieve wheat traits at the canopy level with PLSR and NN indicating the highest modelling performance. On the contrary, MLRA models indicated less accurate retrievals of the leaf-level traits. DR techniques were found to notably improve the retrieval accuracy of crop traits. Furthermore, the generated models were re-calibrated using soil spectra and then transferred to an airborne dataset collected using a CASI-SASI hyperspectral sensor, allowing the estimation of wheat traits across the entire field. The predicted crop trait maps illustrated consistent patterns while also preserving the real-field characteristics well. Lastly, a statistical paired t-test was undertaken to conduct a proof of concept of wheat phenotyping analysis considering the different agricultural variables across the study site. N-treatment caused significant differences in wheat crop traits in many instances, whereas the observed differences were less pronounced between the cultivars. No particular impact of soil ploughing conditions on wheat crop characteristics was found. Using such combinations of MLRA and DR techniques based on hyperspectral data can help to effectively monitor crop traits throughout the cropping seasons and can also be readily applied to other agricultural settings to help both precision farming applications and the implementation of high-throughput phenotyping solutions. Full article

An outdoor experiment was conducted to measure the thickness of oil films (0~3000 μm) using an airborne hyperspectral imager and thermal infrared imager, and the spectral response and thermal response of oil films of different thicknesses were analyzed. The classic support vector regression (SVR) model was used to retrieve the oil film thickness. On this basis, the suitable range for retrieving oil film thickness using hyperspectral and thermal infrared remote sensing was explored, and the decision-level fusion algorithm was developed to fuse the retrieval capabilities of hyperspectral and thermal infrared remote sensing for oil film thickness. The following conclusions can be drawn: (1) Based on airborne hyperspectral data and thermal infrared data, the retrieval accuracy of oil films of different thicknesses reached 154.31 μm and 116.59 μm, respectively. (2) The S185 hyperspectral data were beneficial for retrieving thicknesses greater than or equal to 400 μm, and the H20T thermal infrared data were beneficial for retrieving thicknesses greater than 500 μm. (3) The result of the decision-level fusion model based on a fuzzy membership degree was superior to those obtained using a single sensor (hyperspectral or thermal infrared), indicating that it can better integrate the retrieval results of hyperspectral and thermal infrared remote sensing for oil film thickness. Furthermore, the feasibility of using hyperspectral and thermal infrared remote sensing to detect water-in-oil emulsions of different thicknesses was investigated through spectral response and thermal response analysis. Full article