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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

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Keywords

Hyperspectral
Imaging Spectroscopy
Forest
Ecosystem
Tree

Published Papers (2 papers)

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30 pages, 150287 KiB

Open AccessArticle

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://0-doi-org.brum.beds.ac.uk/10.3390/rs13224659 - 18 Nov 2021

Cited by 14 | Viewed by 2865

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 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

Open AccessEditor’s ChoiceArticle

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 12 | Viewed by 6716

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 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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