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Remote Sensing
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Ecological Monitoring of Northern Forests Based on Hyperspectral Imagery
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Ecological Monitoring of Northern Forests Based on Hyperspectral Imagery

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 December 2021) | Viewed by 10369

Special Issue Editors

Dr. Sarita Keski-Saari

E-Mail Website
Guest Editor
Section of Forest Sciences, University of Eastern Finland, Joensuu, Finland
Interests: imaging spectroscopy; plant ecophysiology; forests; metabolomics; biodiversity; natural variation; deciduous trees; aspen; birch
Dr. Lea Hallik

E-Mail Website1 Website2
Guest Editor
University of Tartu
Interests: leaf optical properties; light acclimation; photosynthetic pigments; plant ecophysiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Northern forests are subject to rapid environmental changes due to global change, biodiversity decline and shifts in forest management. The timing of phenological events is especially vulnerable to global warming. The spread of new pests or diseases to northern areas may increase and have a substantial effect on ecosystems with a limited number of species. The species distribution and diversity, including current status to allow later monitoring of changes, needs incorporation of efficient monitoring techniques. 

Hyperspectral imaging i.e. imaging spectroscopy gives detailed information of light reflectance properties of biological samples. Hyperspectral imaging can be utilized at different scales including satellite, airborne, unmanned aerial vehicles and proximal sensing. Different camera techniques utilize different wavelength ranges, commonly visible and near infrared or short-wave infrared, but also mid-wave infrared or long-wave infrared. Since hyperspectral imaging is a non-invasive technique, it allows repeated data collection and can be utilized in ecological and environmental monitoring.

Spectral reflectance differs among plant species due differences in biochemical composition and structural properties. In forest research, spectral reflectance can be utilized in recognition of both tree species and understory vegetation. Spectral diversity within a forest can be considered as an estimate of species diversity, and utilized in estimation of biodiversity. However, environmental factors that affect biochemical constituents of plants may have an effect on spectral reflectance of plants. Within-species natural variation in spectral reflectance has not been studied as much as among species species variation and would deserve more attention. Hyperspectral imaging can be utilized in forest health assessment due to efficient disease symptom detection. It allows also phenological monitoring due to changes in the spectrum during bud break and leaf senescence.

Analysis of hyperspectral data can utilize different types of multifactorial analyses and classification approaches. On the other hand, the data can be used in calculation of different types of indices that have been widely utilized in remote sensing applications. New hyperspectral satellite missions, such as the German EnMAP, will allow large scale data collection with new analysis-related challenges.

For this special issue, we welcome submissions of most recent research advances in hyperspectral imaging of northern forests. Northern forest cover the whole boreal reagion, but can be interpreted to include semiboreal and temperate coniferous forests. All scales including remote and proximal sensing are welcome. The topics include but are not restricted to

  • Tree species detection and recognition
  • Ground and understory vegetation detection
  • Spectral and species diversity
  • Retrieval of biochemical composition within or among species
  • Forest health assessment
  • Monitoring of seasonal changes, assessment of phenological events

Dr. Sarita Keski-Saari
Dr. Lea Hallik
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

  • Boreal forests
  • Imaging spectroscopy
  • Environmental monitoring
  • Spectral reflectance
  • Spectral diversity
  • Biodiversity
  • Tree species recognition

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Published Papers (2 papers)

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Research

15 pages, 3858 KiB  
Article
Spectral Reflectance in Silver Birch Genotypes from Three Provenances in Finland
by Maya Deepak, Sarita Keski-Saari, Laure Fauch, Lars Granlund, Elina Oksanen and Markku Keinänen
Remote Sens. 2020, 12(17), 2677; https://doi.org/10.3390/rs12172677 - 19 Aug 2020
Cited by 3 | Viewed by 2955
Abstract
The goal of this study was to investigate the variation in the leaf spectral reflectance and its association with other leaf traits from 12 genotypes among three provenances of origin (populations) in a common garden for Finnish silver birch trees in 2015 and [...] Read more.
The goal of this study was to investigate the variation in the leaf spectral reflectance and its association with other leaf traits from 12 genotypes among three provenances of origin (populations) in a common garden for Finnish silver birch trees in 2015 and 2016. The spectral reflectance was measured in the laboratory from the detached leaves in the wavelength range of visible and near-infrared (VNIR, 400–1000 nm) and shortwave infrared (SWIR, 1000–2500 nm). The variation among the provenance was initially visualized with principal component analysis (PCA) and a clear separation among the provenances was detected with the discriminant analysis of principal components (DAPC) and partial least squares discriminant analysis (PLS-DA) depicting a less strong variation among the genotypes within the provenances. Wavelengths contributing to the separation of the genotypes and provenances were identified from the contribution plot of DAPC and the red edge was strongly related to the differences. Chlorophyll content showed clear provenance variation and was associated with the separation among the genotypes and provenances in the DAPC space. The normalized difference vegetation index (NDVI705,750) and chlorophyll reflectance index (CRI) showed clear significance among the provenances, whereas NDVI670,780 showed no variation. The variation in the chlorophyll content and the CRI and red edge-based NDVI indices indicated seasonal variation as the chlorophyll content starts increasing in early June. The correlation of foliar chlorophyll content and the chlorophyll-related spectral indices for the discrimination of provenances and genotypes are reported for the first time in a naturally occurring tree species consecutively for two years. Full article
(This article belongs to the Special Issue Ecological Monitoring of Northern Forests Based on Hyperspectral Imagery)
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27 pages, 6831 KiB  
Article
Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data
by Arto Viinikka, Pekka Hurskainen, Sarita Keski-Saari, Sonja Kivinen, Topi Tanhuanpää, Janne Mäyrä, Laura Poikolainen, Petteri Vihervaara and Timo Kumpula
Remote Sens. 2020, 12(16), 2610; https://doi.org/10.3390/rs12162610 - 13 Aug 2020
Cited by 18 | Viewed by 6702
Abstract
Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremula L.) is one key feature in boreal forests contributing significantly to the [...] Read more.
Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremula L.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455–2500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiers—support vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests. Full article
(This article belongs to the Special Issue Ecological Monitoring of Northern Forests Based on Hyperspectral Imagery)
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