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Global Vegetation Monitoring by Hyperspectral Imaging

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 4028

Special Issue Editor


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Guest Editor
Flemish Institute for Technological Research, Center for Remote Sensing and Earth Observation Processes (VITO-TAP), 2400 Mol, Belgium
Interests: pattern recognition; image processing; computer vision; image analysis; feature selection; wavelet; calibration; classification; hyperspectral image analysis; hyperspectral remote sensing
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Special Issue Information

Dear Colleagues,

Global vegetation monitoring is a main driver for earth observation missions, with major missions providing long term data series of multispectral images, containing valuable information on the status of vegetation. Over the years, increasing spatial resolution imaging has made it possible to reveal more local aspects and dynamics, and derive more accurate results also for areas with less uniform vegetation. Still, the gap between local onsite observations and earth observation data is considerable.

A wealth of additional information is contained in the spectral reflectance of vegetation cover.  Detailed spectra allow scientists to distinguish different types of species very precisely, allowing them to study biodiversity as well as to reveal the status of vegetation growth, drought stress, and diseases. Early hyperspectral satellite missions like Hyperion EO-1 and CHRIS PROBA have been very important in demonstrating such capability.

Nowadays, new (DESIS, PRISMA) and upcoming (ENMAP, SBG) large hyperspectral missions, as well as some innovative smaller missions, will be able to provide much larger coverage and increased spatial detail, which opens up greater opportunities to monitor global vegetation using hyperspectral data. 

Many questions are still open, e.g., those related to the spatial scale at which hyperspectral information is most effective and which spectral range and resolution are needed to achieve specific goals.  To provide regularly updated hyperspectral imagery at a global scale is a tremendous challenge which requires efficient strategies for efficient data acquisition, processing, and product delivery.  As solutions increasingly use data from multiple satellites, calibration and product validation activities become ever more crucial.  The combination of hyperspectral data with higher resolution multispectral data is also being studied. The methods needed to maximize the benefits from doing so are to be investigated.

In this Special Issue, we aim to publish papers which bridge the gap between the technological developments of hyperspectral instruments and satellite missions, and topical investigations on global vegetation using the data arising from these developments. The combination of both approaches will contribute to a better understanding of possibilities and needs, and lead the way towards novel applications advancing hyperspectral vegetation monitoring. Potential authors are encouraged to reflect also on aspects complementing their main area of expertise.

Dr. Stefan Livens
Guest Editor

Manuscript Submission Information

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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 2700 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 imaging
  • Global satellite missions
  • Vegetation monitoring
  • Species and biodiversity
  • Vegetation spectra
  • Hyperspectral image analysis

Published Papers (1 paper)

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Research

20 pages, 25179 KiB  
Article
Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery
by Katja Berger, Tobias Hank, Andrej Halabuk, Juan Pablo Rivera-Caicedo, Matthias Wocher, Matej Mojses, Katarina Gerhátová, Giulia Tagliabue, Miguel Morata Dolz, Ana Belen Pascual Venteo and Jochem Verrelst
Remote Sens. 2021, 13(22), 4711; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224711 - 21 Nov 2021
Cited by 16 | Viewed by 3289
Abstract
Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the [...] Read more.
Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non-photosynthetic vegetation states. Active learning was employed to reduce and optimize the training data set. In addition, we applied spectral dimensionality reduction to condense essential information of non-photosynthetic signals. The resulting NPV-GPR model was successfully validated against soybean field data with normalized root mean square error (nRMSE) of 13.4% and a coefficient of determination (R2) of 0.85. To demonstrate mapping capability, the NPV-GPR model was tested on a PRISMA hyperspectral image acquired over agricultural areas in the North of Munich, Germany. Reliable estimates were mainly achieved over senescent vegetation areas as suggested by model uncertainties. The proposed workflow is the first step towards the quantification of non-photosynthetic cropland biomass as a next-generation product from near-term operational missions, such as CHIME. Full article
(This article belongs to the Special Issue Global Vegetation Monitoring by Hyperspectral Imaging)
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