Special Issue "Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences"

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

Deadline for manuscript submissions: 31 October 2019.

Special Issue Editors

Prof. Dr. Michael Vohland
E-Mail Website
Guest Editor
Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, Johannisallee 19a, D-04103 Leipzig, Germany
Interests: Remote sensing (hyper- and multispectral, thermal); portable vis-NIR and FTIR spectroscopy; digital soil mapping; vegetation mapping (agriculture, forestry); multivariate data analysis; spatial modeling (SVAT, hydrology)
Special Issues and Collections in MDPI journals
Dr. András Jung
E-Mail Website
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 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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (5 papers)

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Open AccessArticle
Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images
Remote Sens. 2019, 11(7), 736; https://doi.org/10.3390/rs11070736 - 27 Mar 2019
Cited by 3
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 [...] Read more.
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 (ECa) 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
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Open AccessArticle
Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques
Remote Sens. 2018, 10(12), 2047; https://doi.org/10.3390/rs10122047 - 16 Dec 2018
Cited by 2
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 [...] Read more.
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
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Open AccessArticle
High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery
Remote Sens. 2018, 10(8), 1180; https://doi.org/10.3390/rs10081180 - 26 Jul 2018
Cited by 3
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 [...] Read more.
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
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Open AccessFeature PaperArticle
Radiometric Correction and 3D Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops
Remote Sens. 2018, 10(2), 176; https://doi.org/10.3390/rs10020176 - 26 Jan 2018
Cited by 8
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 [...] Read more.
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
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Open AccessTechnical Note
Multi-Channel Optical Receiver for Ground-Based Topographic Hyperspectral Remote Sensing
Remote Sens. 2019, 11(5), 578; https://doi.org/10.3390/rs11050578 - 09 Mar 2019
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 [...] Read more.
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
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