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Remote Sensing
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Imaging Spectroscopy of Forest Ecosystems
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Imaging Spectroscopy of Forest Ecosystems

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 12033

Special Issue Editor

Dr. Henning Buddenbaum

E-Mail Website
Guest Editor
Environmental Remote Sensing and Geoinformatics, Trier University, 54286 Trier, Germany
Interests: imaging spectroscopy; forestry; LiDAR

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing, also known as imaging spectroscopy, has been available since the 1980s and is still an expanding and vibrant field of study. After the early satellite sensors HYPERION and CHRIS, there were no new hyperspectral satellites for a long time. With PRISMA and DESIS, the first few of a new generation of satellite hyperspectral imagers have now been deployed, such as EnMAP, HISUI, SHALOM, FLEX, and others are soon to follow. Airborne and UAV-based hyperspectral sensors have become affordable, so nowadays getting a hyperspectral dataset is no longer the exclusive privilege of a handful of institutions. The wavelength range of hyperspectral sensors has been extended into the thermal infrared, opening the pathway to numerous novel research questions.

Despite all that, studies about using Imaging Spectroscopy to better understand Forest Ecosystems are still scarce. This Special Issue therefore aims at collecting high-quality papers on applications of hyperspectral remote sensing for forest research. Studies about species distribution, forest health, growth conditions, photosynthesis, fluorescence, forest structure, and similar topics are welcome, as well as studies on sensor fusion and synergies between imaging spectroscopy and other techniques like Lidar, Radar, or multispectral imaging. Methodological papers on hyperspectral data-processing techniques like machine learning, deep learning, unmixing, feature reduction, and others are welcome if they have a clear application in forest science. Review papers, technical notes, and research contributions are suitable.

Dr. Henning Buddenbaum
Guest Editor

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 submissions that pass pre-check are 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 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 Spectroscopy
  • Forest
  • Ecosystem
  • Tree

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Published Papers (2 papers)

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Research

30 pages, 150287 KiB  
Article
Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements
by Florian M. Hellwig, Martyna A. Stelmaszczuk-Górska, Clémence Dubois, Marco Wolsza, Sina C. Truckenbrodt, Herbert Sagichewski, Sergej Chmara, Lutz Bannehr, Angela Lausch and Christiane Schmullius
Remote Sens. 2021, 13(22), 4659; https://doi.org/10.3390/rs13224659 - 18 Nov 2021
Cited by 19 | Viewed by 3450
Abstract
The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected [...] Read more.
The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks. Full article
(This article belongs to the Special Issue Imaging Spectroscopy of Forest Ecosystems)
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19 pages, 119585 KiB  
Article
Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska
by Anushree Badola, Santosh K. Panda, Dar A. Roberts, Christine F. Waigl, Uma S. Bhatt, Christopher W. Smith and Randi R. Jandt
Remote Sens. 2021, 13(9), 1693; https://doi.org/10.3390/rs13091693 - 27 Apr 2021
Cited by 14 | Viewed by 7759
Abstract
Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land [...] Read more.
Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest. Full article
(This article belongs to the Special Issue Imaging Spectroscopy of Forest Ecosystems)
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