Special Issue "Remote Sensing of Boreal Forests"

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 May 2019).

Special Issue Editors

Prof. Dr. Tuomas Häme
Website
Guest Editor
VTT Technical Research Centre of Finland, Espoo, Finland
Interests: forest biomass and carbon; forest management support with remote sensing; forest inventory and statistical techniques; change detection; automatic and adaptive image analysis systems
Special Issues and Collections in MDPI journals
Dr. Garik Gutman
Website
Guest Editor
NASA Headquarters, Washington DC, USA
Interests: Remote sensing of land use/cover change; land-atmospheric interactions; big-data processing; remote sensing of the environment.
Special Issues and Collections in MDPI journals
Dr. Oleg Antropov
Website
Guest Editor
Aalto University, Finland
Interests: imaging radar; SAR polarimetry; SAR interferometry; vegetation mapping; boreal forest; land cover change; machine learning; semantic segmentation
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Boreal forests, the largest terrestrial biome, includes nearly one third of the world’s forests. They have a major role in the global carbon cycle as carbon storage and sinks, with partial compensation by emissions from forest fires, deforestation, and decay of damaged forests. Moreover, the sink can turn into carbon sources by the end of the century because of climate warming, but the predictions include large uncertainties.

Intensive forest management is practiced in boreal forests to provide raw material for industry. Industry requires sustainability in terms of management to ensure continuous wood supply. Requirements of natural conservation and biodiversity impose additional challenges for forest management.

Space-borne Earth observations enable the provision of improved information of boreal forests to serve all information needs. The operational Sentinel satellites of the Copernicus program have opened a new chapter as information sources for the vast boreal forest areas. At local and regional levels, very high-resolution satellite images and airborne drones provide detailed information on boreal forest ecosystems. Radar sensors have an important role in boreal regions with frequent clouds and poor illumination conditions during winter.

For this Special Issue, we welcome submissions on most recent advancements of the remote sensing of boreal forest, including, but not limited to:

  • Carbon storage and fluxes
  • Forest variable estimation for forest management
  • Statistical inventory methods applying satellite data, accuracy assessment
  • Biodiversity
  • Forest fires
  • Change monitoring
  • Utilization of time series
  • Fusion of optical and radar data
  • Combined use of different spatial resolutions
  • Mapping and monitoring of boreal peat lands
  • SAR interferometry
  • Hyperspectral data
  • Physical image interpretation methods
  • Artificial intelligence methods

Prof. Dr. Tuomas Häme
Dr. Garik Gutman
Dr. Oleg Antropov
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 2200 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 forest
  • satellite
  • carbon
  • forest management
  • change
  • data fusion
  • artificial intelligence
  • optical
  • radar

Published Papers (8 papers)

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Research

Open AccessArticle
Estimating Stand Age from Airborne Laser Scanning Data to Improve Models of Black Spruce Wood Density in the Boreal Forest of Ontario
Remote Sens. 2019, 11(17), 2022; https://doi.org/10.3390/rs11172022 - 28 Aug 2019
Abstract
Spatial models that provide estimates of wood quality enable value chain optimization approaches that consider the market potential of trees prior to harvest. Ecological land classification units (e.g., ecosite) and structural metrics derived from Airborne Laser Scanning (ALS) data have been shown to [...] Read more.
Spatial models that provide estimates of wood quality enable value chain optimization approaches that consider the market potential of trees prior to harvest. Ecological land classification units (e.g., ecosite) and structural metrics derived from Airborne Laser Scanning (ALS) data have been shown to be useful predictors of wood quality attributes in black spruce stands of the boreal forest of Ontario, Canada. However, age drives much of the variation in wood quality among trees, and has not been included as a predictor in previous models because it is poorly represented in inventory systems. The objectives of this study were (i) to develop a predictive model of mean stem age of black spruce-dominated stands, and (ii) refine models of black spruce wood density by including age as a predictor variable. A non-parametric model of stand age that used a k nearest neighbor (kNN) classification based on a random forests (rf) distance metric performed well, producing a root mean square difference (RMSD) of 15 years and explaining 62% of the variance. The subsequent random forests model of black spruce wood density generated from age and ecosite predictors was useful, with a root mean square error (RMSE) of 59.1 kg·m−3. These models bring large-scale wood quality prediction closer to becoming operational by including age and site effects that can be derived from inventory data. Full article
(This article belongs to the Special Issue Remote Sensing of Boreal Forests)
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Open AccessArticle
Reflectance Properties of Hemiboreal Mixed Forest Canopies with Focus on Red Edge and Near Infrared Spectral Regions
Remote Sens. 2019, 11(14), 1717; https://doi.org/10.3390/rs11141717 - 20 Jul 2019
Cited by 3
Abstract
This study present the results of airborne top-of-canopy measurements of reflectance spectra in the spectral domain of 350–1050 nm over the hemiboreal mixed forest. We investigated spectral transformations that were originally designed for utilization at very different spectral resolutions. We found that the [...] Read more.
This study present the results of airborne top-of-canopy measurements of reflectance spectra in the spectral domain of 350–1050 nm over the hemiboreal mixed forest. We investigated spectral transformations that were originally designed for utilization at very different spectral resolutions. We found that the estimates of red edge inflection point by two methods—the linear four-point interpolation approach (S2REP) and searching the maximum of the first derivative spectrum ( D m a x ) according to the mathematical definition of red edge inflection point—were well related to each other but S2REP produced a continuously shifting location of red edge inflection point while D m a x resulted in a discrete variable with peak jumps between fixed locations around 717 nm and 727 nm for forest canopy (the third maximum at 700 nm appeared only in clearcut areas). We found that, with medium high spectral resolution (bandwidth 10 nm, spectral step 3.3 nm), the in-filling of the O 2 -A Fraunhofer line ( F a r e a ) was very strongly related to single band reflectance factor in NIR spectral region ( ρ = 0.91, p < 0.001) and not related to Photochemical Reflectance Index (PRI). Stemwood volume, basal area and tree height of dominant layer were negatively correlated with reflectance factors at both visible and NIR spectral region due to the increase in roughness of canopy surface and the amount of shade. Forest age was best related to single band reflectance at NIR region ( ρ = −0.48, p < 0.001) and the best predictor for allometric LAI was the single band reflectance at red spectral region ( ρ = −0.52, p < 0.001) outperforming all studied vegetation indices. It suggests that Sentinel-2 MSI bands with higher spatial resolution (10 m pixel size) could be more beneficial than increased spectral resolution for monitoring forest LAI and age. The new index R 751 /R 736 originally developed for leaf chlorophyll content estimation, also performed well at the canopy level and was mainly influenced by the location of red edge inflection point ( ρ = 0.99, p < 0.001) providing similar info in a simpler mathematical form and using a narrow spectral region very close to the O 2 -A Fraunhofer line. Full article
(This article belongs to the Special Issue Remote Sensing of Boreal Forests)
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Open AccessArticle
Benchmarking the Retrieval of Biomass in Boreal Forests Using P-Band SAR Backscatter with Multi-Temporal C- and L-Band Observations
Remote Sens. 2019, 11(14), 1695; https://doi.org/10.3390/rs11141695 - 17 Jul 2019
Cited by 2
Abstract
The planned launch of a spaceborne P-band radar mission and the availability of C- and L-band data from several spaceborne missions suggest investigating the complementarity of C-, L-, and P-band backscatter with respect to the retrieval of forest above-ground biomass. Existing studies on [...] Read more.
The planned launch of a spaceborne P-band radar mission and the availability of C- and L-band data from several spaceborne missions suggest investigating the complementarity of C-, L-, and P-band backscatter with respect to the retrieval of forest above-ground biomass. Existing studies on the retrieval of biomass with multi-frequency backscatter relied on single observations of the backscatter and were thus not able to demonstrate the potential of multi-temporal C- and L-band data that are now available from spaceborne missions. Based on spaceborne C- and L-band and airborne P-band images acquired over a forest site in southern Sweden, we investigated whether C- and L-band backscatter may complement retrievals of above-ground biomass from P-band. To this end, a retrieval framework was adopted that utilizes a semi-empirical model for C- and L-bands and an empirical parametric model for P-band. Estimates of above-ground biomass were validated with the aid of 20 m-diameter plots and a LiDAR-derived biomass map with 100 m × 100 m pixel size. The highest retrieval accuracy when not combining frequencies was obtained for P-band with a relative root mean square error (RMSE) of 30% at the hectare scale. The retrieval with multi-temporal L- and C-bands produced errors of the order of 40% and 50%, respectively. The P-band retrieval could be improved for 4% when using P-, L-, and C-bands jointly. The combination of C- and L-bands allowed for retrieval accuracies close to those achieved with P-band. A crucial requirement for achieving an error of 30% with C- and L-bands was the use of multi-temporal observations, which was highlighted by the fact that the retrieval with the best individual L-band image was associated with an error of 61%. The results of this study reconfirmed that P-band is the most suited frequency for the retrieval of above-ground biomass of boreal forests based on backscatter, but also highlight the potential of multi-temporal C- and L-band imagery for mapping above-ground biomass, for instance in areas where the planned ESA BIOMASS P-band mission will not be allowed to acquire data. Full article
(This article belongs to the Special Issue Remote Sensing of Boreal Forests)
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Open AccessArticle
On the Sensitivity of TanDEM-X-Observations to Boreal Forest Structure
Remote Sens. 2019, 11(14), 1644; https://doi.org/10.3390/rs11141644 - 10 Jul 2019
Cited by 2
Abstract
The structure of forests is important to observe for understanding coupling to global dynamics of ecosystems, biodiversity, and management aspects. In this paper, the sensitivity of X-band to boreal forest stem volume and to vertical and horizontal structure in the form of forest [...] Read more.
The structure of forests is important to observe for understanding coupling to global dynamics of ecosystems, biodiversity, and management aspects. In this paper, the sensitivity of X-band to boreal forest stem volume and to vertical and horizontal structure in the form of forest height and horizontal vegetation density is studied using TanDEM-X satellite observations from two study sites in Sweden: Remningstorp and Krycklan. The forest was analyzed with the Interferometric Water Cloud Model (IWCM), without the use of local data for model training, and compared with measurements by Airborne Lidar Scanning (ALS). On one hand, a large number of stands were studied, and in addition, plots with different types of changes between 2010 and 2014 were also studied. It is shown that the TanDEM-X phase height is, under certain conditions, equal to the product of the ALS quantities for height and density. Therefore, the sensitivity of phase height to relative changes in height and density is the same. For stands with a phase height >5 m we obtained an root-mean-square error, RMSE, of 8% and 10% for tree height in Remningstorp and Krycklan, respectively, and for vegetation density an RMSE of 13% for both. Furthermore, we obtained an RMSE of 17% for estimation of above ground biomass at stand level in Remningstorp and in Krycklan. The forest changes estimated with TanDEM-X/IWCM and ALS are small for all plots except clear cuts but show similar trends. Plots without forest management changes show a mean estimated height growth of 2.7% with TanDEM-X/IWCM versus 2.1% with ALS and a biomass growth of 4.3% versus 4.2% per year. The agreement between the estimates from TanDEM-X/IWCM and ALS is in general good, except for stands with low phase height. Full article
(This article belongs to the Special Issue Remote Sensing of Boreal Forests)
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Open AccessArticle
Complementarity of X-, C-, and L-band SAR Backscatter Observations to Retrieve Forest Stem Volume in Boreal Forest
Remote Sens. 2019, 11(13), 1563; https://doi.org/10.3390/rs11131563 - 02 Jul 2019
Cited by 2
Abstract
The simultaneous availability of observations from space by remote sensing platforms operating at multiple frequencies in the microwave domain suggests investigating their complementarity in thematic mapping and retrieval of biophysical parameters. In particular, there is an interest to understand whether the wealth of [...] Read more.
The simultaneous availability of observations from space by remote sensing platforms operating at multiple frequencies in the microwave domain suggests investigating their complementarity in thematic mapping and retrieval of biophysical parameters. In particular, there is an interest to understand whether the wealth of short wavelength Synthetic Aperture Radar (SAR) backscatter observations at X-, C-, and L-band from currently operating spaceborne missions can improve the retrieval of forest stem volume, i.e., above-ground biomass, in the boreal zone with respect to a single frequency band. To this scope, repeated observations from TerraSAR-X, Sentinel-1 and ALOS-2 PALSAR-2 from the test sites of Remningstorp and Krycklan, Sweden, have been analyzed and used to estimate stem volume with a retrieval framework based on the Water Cloud Model. Individual estimates of stem volume were then combined linearly to form single-frequency and multi-frequency estimates. The retrieval was assessed at large 0.5 ha forest inventory plots (Remningstorp) and small 0.03 ha forest inventory plots (Krycklan). The relationship between SAR backscatter and stem volume differed depending on forest structure and environmental conditions, in particular at X- and C-band. The highest retrieval accuracy was obtained at both test sites at L-band. The combination of stem volume estimates from data acquired at two or three frequencies achieved an accuracy that was superior to values obtained at a single frequency. When combining estimates from X-, C-, and L-band data, the relative RMSE for the 0.5 ha inventory plots at Remningstorp was 31.3%. For the 0.03 ha inventory plots at Krycklan, the relative RMSE was above 50%. In a retrieval scenario involving short wavelength SAR backscatter data, these results suggest combining multiple frequencies to ensure the highest possible retrieval accuracy achievable. Retrievals should be undertaken to target spatial scales well above the size of a pixel. Full article
(This article belongs to the Special Issue Remote Sensing of Boreal Forests)
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Open AccessArticle
Extending ALS-Based Mapping of Forest Attributes with Medium Resolution Satellite and Environmental Data
Remote Sens. 2019, 11(9), 1092; https://doi.org/10.3390/rs11091092 - 08 May 2019
Cited by 5
Abstract
Airborne laser scanner (ALS) data are used to map a range of forest inventory attributes at operational scales. However, when wall-to-wall ALS coverage is cost prohibitive or logistically challenging, alternative approaches are needed for forest mapping. We evaluated an indirect approach for extending [...] Read more.
Airborne laser scanner (ALS) data are used to map a range of forest inventory attributes at operational scales. However, when wall-to-wall ALS coverage is cost prohibitive or logistically challenging, alternative approaches are needed for forest mapping. We evaluated an indirect approach for extending ALS-based maps of forest attributes using medium resolution satellite and environmental data. First, we developed ALS-based models and predicted a suite of forest attributes for a 950 km2 study area covered by wall-to-wall ALS data. Then, we used samples extracted from the ALS-based predictions to model and map these attributes with satellite and environmental data for an extended 5600 km2 area with similar forest and ecological conditions. All attributes were predicted well with the ALS data (R2 ≥ 0.83; RMSD% < 26). The satellite and environmental models developed using the ALS-based predictions resulted in increased correspondence between observed and predicted values by 13–49% and decreased prediction errors by 8–28% compared with models developed directly with the ground plots. Improvements were observed for both multiple regression and random forest models, and for the suite of forest attributes assessed. We concluded that the use of ALS-based predictions in this study improved the estimation of forest attributes beyond an approach linking ground plots directly to the satellite and environmental data. Full article
(This article belongs to the Special Issue Remote Sensing of Boreal Forests)
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Open AccessArticle
S-RVoG Model Inversion Based on Time-Frequency Optimization for P-Band Polarimetric SAR Interferometry
Remote Sens. 2019, 11(9), 1033; https://doi.org/10.3390/rs11091033 - 01 May 2019
Cited by 2
Abstract
This paper investigates the potential of the time-frequency optimization on the basis of the sublook decomposition for forest height estimation. The optimization is deemed to be capable of extracting a relatively accurate volume contribution when P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) systems [...] Read more.
This paper investigates the potential of the time-frequency optimization on the basis of the sublook decomposition for forest height estimation. The optimization is deemed to be capable of extracting a relatively accurate volume contribution when P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) systems are adopted to observe forest-covered areas. The highest and the lowest phase centers acquired by the time-frequency optimization modify the conventional three-stage inversion process. This paper presents, for the first time, a performance assessment of the time-frequency optimization on P-band Pol-InSAR data over boreal forests. Simultaneously, to alleviate the model inversion errors caused by topographic fluctuations, forest height is estimated based on the sloped Random Volume over Ground (S-RVoG) model in which the incidence angle is corrected with the terrain slope. The E-SAR P-band Pol-InSAR data acquired during the BIOSAR 2008 campaign in Northern Sweden is utilized to evaluate the performance of the proposed method. From the results of the forest height estimation preprocessed with time-frequency optimization, the root mean square error (RMSE) of Random Volume over Ground (RVoG) and S-RVoG model on negative slope are 5.09 m and 4.71 m, respectively. It is concluded that the time-frequency processing and negative terrain slope compensation improve the inversion performance by 41 . 49 % and 11 . 96 % , respectively. Full article
(This article belongs to the Special Issue Remote Sensing of Boreal Forests)
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Open AccessArticle
Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data
Remote Sens. 2019, 11(4), 384; https://doi.org/10.3390/rs11040384 - 13 Feb 2019
Cited by 3
Abstract
Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by disturbances are critical for estimating extent of damage, assessing economical influence and guiding forest management activities. In this study we focus on snow load damage [...] Read more.
Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by disturbances are critical for estimating extent of damage, assessing economical influence and guiding forest management activities. In this study we focus on snow load damage detection from C-Band SAR images. Snow damage is one of the least studied forest damages, which is getting more common due to current climate trends. The study site was located in the southern part of Northern Finland and the SAR data were represented by the time series of C-band SAR scenes acquired by the Sentinel-1 sensor. Methods used in the study included improved k nearest neighbour method, logistic regression analysis and support vector machine classification. Snow damage recordings from a large snow damage event that took place in Finland during late 2018 were used as reference data. Our results showed an overall detection accuracy of 90%, indicating potential of C-band SAR for operational use in snow damage mapping. Additionally, potential of multitemporal Sentinel-1 data in estimating growing stock volume in damaged forest areas were carried out, with obtained results indicating strong potential for estimating the overall volume of timber within the affected areas. The results and research questions for further studies are discussed. Full article
(This article belongs to the Special Issue Remote Sensing of Boreal Forests)
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