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Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 59164

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Special Issue Editors

Prof. Dr. Michael Vohland

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Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, Johannisallee 19a, 04103 Leipzig, Germany
Interests: remote sensing (hyper- and multispectral, thermal); hyperspectral imaging; portable vis-NIR and MIR spectroscopy; proximal soil sensing; digital soil mapping; vegetation mapping (agriculture, forestry); hydrological modeling; machine learning
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Dr. András Jung

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Guest Editor

1. Department of Geoinformatics and Remote Sensing, Leipzig University (LU), D-04103 Leipzig, Germany
2. Department of Agricultural Engineering, Szent István University, (SZIU), H-1118 Budapest, Hungary
Interests: hyperspectral remote sensing; field spectroscopy; mobile and snapshot imaging spectroscopy; precision farming; agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remarkable advances are currently underway in the field of close range, on-site and near-ground hyperspectral imaging. New sensor types with improved specifications and new platforms, such as unmanned aerial vehicles, have broadened the scope of potential applications in the environmental and earth sciences. In addition to traditional scanning devices, a rapidly growing variety of low-weight hyperspectral cameras with real-time imaging capabilities has been established commercially in recent years. These compact instruments are highly flexible in use as they allow us to capture the entire hyperspectral data cube in real-time (“snapshot”) without limitations to user mobility. Non-scanning cameras that cover the entire vis–NIR–SWIR domain (400–2500 nm), however, are still missing, whereas hyperspectral scanners can achieve full spectral range coverage routinely, either with a single instrument or in a configuration with two co-aligned instruments. In addition to field applications usually carried out with handheld instruments, or with sensors mounted on tripods or mobile platforms, hyperspectral imaging is also routinely used in laboratory setups, where it allows the non-destructive and time-efficient data acquisition even at microscopic scales under controlled illumination conditions and is therefore of interest to a wide range of disciplines.

The aim of this Special Issue is to focus on all applications of remote and proximal hyperspectral imaging at very fine (microscopic) to medium scales. This includes lab and out-of-lab studies, the latter ranging from on-ground to near-ground observations up to typical flight heights of UAVs (usually about 50 m). Researchers from all disciplines in the environmental and earth sciences using hyperspectral imaging in their research are welcome, including e.g. vegetation and soil science, water spectroscopy (inland, ocean and coastal waters), geology, mineralogy and sedimentology, agriculture, crop science, precision farming, biology and biodiversity, climate change, geoarchaeology, palaeoenvironmental sciences and related fields.

Contributions may cover new applications making specific use of image data, but also instrumental settings, sensor integration in multi-sensor approaches, spectral databases, processing workflows, product validation, statistical and computational methods for image analysis, data mining and machine learning methods, etc.

Prof. Dr. Michael Vohland
Dr. András Jung
Guest Editors

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Keywords

Hyperspectral Scanning and Non-Scanning Cameras
High Resolution Imaging Spectroscopy
Machine Learning and Data Mining
Hyperspectral Image Analysis
Processing Workflows and Product Validation
Field and Lab Applications
Multi-Sensor Concepts

Published Papers (10 papers)

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Editorial

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4 pages, 196 KiB

Open AccessEditorial

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

by Michael Vohland and András Jung

Remote Sens. 2020, 12(18), 2962; https://doi.org/10.3390/rs12182962 - 11 Sep 2020

Cited by 5 | Viewed by 2606

Abstract

Hyperspectral imaging (HSI) combines conventional imaging and spectroscopic techniques in a way of spatially organized spectroscopy [...] Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences)

Research

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19 pages, 6646 KiB

Open AccessArticle

Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in the Water Column of Freshwater Lakes

by Michael Seidel, Christopher Hutengs, Felix Oertel, Daniel Schwefel, András Jung and Michael Vohland

Remote Sens. 2020, 12(11), 1745; https://doi.org/10.3390/rs12111745 - 29 May 2020

Cited by 5 | Viewed by 3654

Abstract

Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L⁻¹ for CHLa; R² = 0.75, RMSE = 0.22 m⁻¹ for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L⁻¹ for CHLa; R² = 0.66, RMSE = 0.25 m⁻¹ for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS. Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences)

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25 pages, 10018 KiB

Open AccessArticle

Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors

by Yoseline Angel, Darren Turner, Stephen Parkes, Yoann Malbeteau, Arko Lucieer and Matthew F. McCabe

Remote Sens. 2020, 12(1), 34; https://doi.org/10.3390/rs12010034 - 20 Dec 2019

Cited by 29 | Viewed by 6459

Abstract

Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for diverse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan. Part of the challenge in obtaining high-quality imagery relates to the lack of a fast processing workflow that can retrieve geometrically accurate mosaics while optimizing the ground data collection efforts. To address this problem, we explored a computationally robust automated georectification and mosaicking methodology. It operates effectively in a parallel computing environment and evaluates results against a number of high-spatial-resolution datasets (mm to cm resolution) collected using a push-broom sensor and an associated RGB frame-based camera. The methodology estimates the luminance of the hyperspectral swaths and coregisters these against a luminance RGB-based orthophoto. The procedure includes an improved coregistration strategy by integrating the Speeded-Up Robust Features (SURF) algorithm, with the Maximum Likelihood Estimator Sample Consensus (MLESAC) approach. SURF identifies common features between each swath and the RGB-orthomosaic, while MLESAC fits the best geometric transformation model to the retrieved matches. Individual scanlines are then geometrically transformed and merged into a single spatially continuous mosaic reaching high positional accuracies only with a few number of ground control points (GCPs). The capacity of the workflow to achieve high spatial accuracy was demonstrated by examining statistical metrics such as RMSE, MAE, and the relative positional accuracy at 95% confidence level. Comparison against a user-generated georectification demonstrates that the automated approach speeds up the coregistration process by 85%. Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences)

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35 pages, 7039 KiB

Open AccessArticle

An Under-Ice Hyperspectral and RGB Imaging System to Capture Fine-Scale Biophysical Properties of Sea Ice

by Emiliano Cimoli, Klaus M. Meiners, Arko Lucieer and Vanessa Lucieer

Remote Sens. 2019, 11(23), 2860; https://doi.org/10.3390/rs11232860 - 02 Dec 2019

Cited by 13 | Viewed by 6194

Abstract

Sea-ice biophysical properties are characterized by high spatio-temporal variability ranging from the meso- to the millimeter scale. Ice coring is a common yet coarse point sampling technique that struggles to capture such variability in a non-invasive manner. This hinders quantification and understanding of ice algae biomass patchiness and its complex interaction with some of its sea ice physical drivers. In response to these limitations, a novel under-ice sled system was designed to capture proxies of biomass together with 3D models of bottom topography of land-fast sea-ice. This system couples a pushbroom hyperspectral imaging (HI) sensor with a standard digital RGB camera and was trialed at Cape Evans, Antarctica. HI aims to quantify per-pixel chlorophyll-a content and other ice algae biological properties at the ice-water interface based on light transmitted through the ice. RGB imagery processed with digital photogrammetry aims to capture under-ice structure and topography. Results from a 20 m transect capturing a 0.61 m wide swath at sub-mm spatial resolution are presented. We outline the technical and logistical approach taken and provide recommendations for future deployments and developments of similar systems. A preliminary transect subsample was processed using both established and novel under-ice bio-optical indices (e.g., normalized difference indexes and the area normalized by the maximal band depth) and explorative analyses (e.g., principal component analyses) to establish proxies of algal biomass. This first deployment of HI and digital photogrammetry under-ice provides a proof-of-concept of a novel methodology capable of delivering non-invasive and highly resolved estimates of ice algal biomass in-situ, together with some of its environmental drivers. Nonetheless, various challenges and limitations remain before our method can be adopted across a range of sea-ice conditions. Our work concludes with suggested solutions to these challenges and proposes further method and system developments for future research. Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences)

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15 pages, 2142 KiB

Open AccessArticle

Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers

by Uwe Knauer, Cornelius Styp von Rekowski, Marianne Stecklina, Tilman Krokotsch, Tuan Pham Minh, Viola Hauffe, David Kilias, Ina Ehrhardt, Herbert Sagischewski, Sergej Chmara and Udo Seiffert

Remote Sens. 2019, 11(23), 2788; https://doi.org/10.3390/rs11232788 - 26 Nov 2019

Cited by 35 | Viewed by 6140

Abstract

In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial–spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75). Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences)

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16 pages, 2183 KiB

Open AccessArticle

Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

by Jie Hu, Jie Peng, Yin Zhou, Dongyun Xu, Ruiying Zhao, Qingsong Jiang, Tingting Fu, Fei Wang and Zhou Shi

Remote Sens. 2019, 11(7), 736; https://doi.org/10.3390/rs11070736 - 27 Mar 2019

Cited by 101 | Viewed by 7361

Abstract

Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed on a UAV platform. In addition, 30 soil samples (0~20 cm) were collected in each field for the lab measurements of electrical conductivity. First, the apparent electrical conductivity (EC_a) values measured by EMI were calibrated using the lab measured electrical conductivity derived from soil samples based on empirical line method. Second, the soil salinity was quantitatively estimated using the random forest (RF) regression method based on the reflectance factors of UAV hyperspectral images and satellite multispectral data. The performance of models was assessed by Lin’s concordance coefficient (CC), ratio of performance to deviation (RPD), and root mean square error (RMSE). Finally, the soil salinity of three study fields with different land cover were mapped. The results showed that bare land (field A) exhibited the most severe salinity, followed by dense vegetation area (field C) and sparse vegetation area (field B). The predictive models using UAV data outperformed those derived from GF-2 data with lower RMSE, higher CC and RPD values, and the most accurate UAV-derived model was developed using 62 hyperspectral bands of the image of the field A with the RMSE, CC, and RPD values of 1.40 dS m⁻¹, 0.94, and 2.98, respectively. Our results indicated that UAV-borne hyperspectral imager is a useful tool for field-scale soil salinity monitoring and mapping. With the help of the EMI technique, quantitative estimation of surface soil salinity is critical to decision-making in arid land management and saline soil reclamation. Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences)

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22 pages, 7820 KiB

Open AccessArticle

Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques

by Jingjing Cao, Kai Liu, Lin Liu, Yuanhui Zhu, Jun Li and Zhi He

Remote Sens. 2018, 10(12), 2047; https://doi.org/10.3390/rs10122047 - 16 Dec 2018

Cited by 39 | Viewed by 6959

Abstract

Investigating mangrove species composition is a basic and important topic in wetland management and conservation. This study aims to explore the potential of close-range hyperspectral imaging with a snapshot hyperspectral sensor for identifying mangrove species under field conditions. Specifically, we assessed the data pre-processing and transformation, waveband selection and machine-learning techniques to develop an optimal classification scheme for eight mangrove species in Qi’ao Island of Zhuhai, Guangdong, China. After data pre-processing and transformation, five spectral datasets, which included the reflectance spectra R and its first-order derivative d(R), the logarithm of the reflectance spectra log(R) and its first-order derivative d[log(R)], and hyperspectral vegetation indices (VIs), were used as the input data for each classifier. Consequently, three waveband selection methods, including the stepwise discriminant analysis (SDA), correlation-based feature selection (CFS), and successive projections algorithm (SPA) were used to reduce dimensionality and select the effective wavebands for identifying mangrove species. Furthermore, we evaluated the performance of mangrove species classification using four classifiers, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). Application of the four considered classifiers on the reflectance spectra of all wavebands yielded overall classification accuracies of the eight mangrove species higher than 80%, with SVM having the highest accuracy of 93.54% (Kappa = 0.9256). Using the selected wavebands derived from SPA, the accuracy of SVM reached 93.13% (Kappa = 0.9208). The addition of hyperspectral VIs and d[log(R)] spectral datasets further improves the accuracies to 93.54% (Kappa = 0.9253) and 96.46% (Kappa = 0.9591), respectively. These results suggest that it is highly effective to apply field close-range snapshot hyperspectral images and machine-learning classifiers to classify mangrove species. Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences)

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31 pages, 12093 KiB

Open AccessArticle

High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery

by Jong Cheol Pyo, Mayzonee Ligaray, Yong Sung Kwon, Myoung-Hwan Ahn, Kyunghyun Kim, Hyuk Lee, Taegu Kang, Seong Been Cho, Yongeun Park and Kyung Hwa Cho

Remote Sens. 2018, 10(8), 1180; https://doi.org/10.3390/rs10081180 - 26 Jul 2018

Cited by 36 | Viewed by 5224

Abstract

Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R² values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area. Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences)

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23 pages, 9851 KiB

Open AccessFeature PaperArticle

Radiometric Correction and 3D Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops

by Sandra Lorenz, Sara Salehi, Moritz Kirsch, Robert Zimmermann, Gabriel Unger, Erik Vest Sørensen and Richard Gloaguen

Remote Sens. 2018, 10(2), 176; https://doi.org/10.3390/rs10020176 - 26 Jan 2018

Cited by 44 | Viewed by 8567

Abstract

Recently, ground-based hyperspectral imaging has come to the fore, supporting the arduous task of mapping near-vertical, difficult-to-access geological outcrops. The application of outcrop sensing within a range of one to several hundred metres, including geometric corrections and integration with accurate terrestrial laser scanning models, is already developing rapidly. However, there are few studies dealing with ground-based imaging of distant targets (i.e., in the range of several kilometres) such as mountain ridges, cliffs, and pit walls. In particular, the extreme influence of atmospheric effects and topography-induced illumination differences have remained an unmet challenge on the spectral data. These effects cannot be corrected by means of common correction tools for nadir satellite or airborne data. Thus, this article presents an adapted workflow to overcome the challenges of long-range outcrop sensing, including straightforward atmospheric and topographic corrections. Using two datasets with different characteristics, we demonstrate the application of the workflow and highlight the importance of the presented corrections for a reliable geological interpretation. The achieved spectral mapping products are integrated with 3D photogrammetric data to create large-scale now-called “hyperclouds”, i.e., geometrically correct representations of the hyperspectral datacube. The presented workflow opens up a new range of application possibilities of hyperspectral imagery by significantly enlarging the scale of ground-based measurements. Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences)

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12 pages, 2959 KiB

Open AccessTechnical Note

Multi-Channel Optical Receiver for Ground-Based Topographic Hyperspectral Remote Sensing

by Sean E. Salazar and Richard A. Coffman

Remote Sens. 2019, 11(5), 578; https://doi.org/10.3390/rs11050578 - 09 Mar 2019

Cited by 4 | Viewed by 4368

Abstract

Receiver design is integral to the development of a new remote sensor. An effective receiver delivers backscattered light to the detector while optimizing the signal-to-noise ratio at the desired wavelengths. Towards the goal of effective receiver design, a multi-channel optical receiver was developed to collect range-resolved, backscattered energy for simultaneous hyperspectral and differential absorption spectrometry (LAS) measurements. The receiver is part of a new, ground-based, multi-mode lidar instrument for remote characterization of soil properties. The instrument, referred to as the soil observation laser absorption spectrometer (SOLAS), was described previously in the literature. A detailed description of the multi-channel receiver of the SOLAS is presented herein. The hyperspectral channel receives light across the visible near-infrared (VNIR) to shortwave infrared (SWIR) spectrum (350–2500 nm), while the LAS channel was optimized for detection in a narrower portion of the near-infrared range (820–850 nm). The range-dependent field of view for each channel is presented and compared with the beam evolution of the SOLAS instrument transmitter. Laboratory-based testing of each of the receiver channels was performed to determine the effectiveness of the receiver. Based on reflectance spectra collected for four soil types, at distances of 20, 35, and 60 m from the receiver, reliable hyperspectral measurements were gathered, independent of the range to the target. Increased levels of noise were observed at the edges of the VNIR and SWIR detector ranges, which were attributed to the lack of sensitivity of the instrument in these regions. The suitability of the receiver design, for the collection of both hyperspectral and LAS measurements at close-ranges, is documented herein. Future development of the instrument will enable the combination of long-range, ground-based hyperspectral measurements with the LAS measurements to correct for absorption, due to atmospheric water vapor. The envisioned application for the instrument includes the rapid characterization of bare or vegetated soils and minerals, such as are present in mine faces and tailings, or unstable slopes. Full article

(This article belongs to the Special Issue Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences)

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