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Remote Sensing of the Russian Boreal Forest
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Remote Sensing of the Russian Boreal Forest

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 2022) | Viewed by 10763

Special Issue Editors

Dr. Olga Tutubalina

E-Mail Website
Guest Editor
Laboratory of Aerospace Methods, Department of Cartography and Geoinformatics, Faculty of Geography, Lomonosov Moscow State University, GSP-1, Leninskie Gory, 119991 Moscow, Russia
Interests: remote sensing; vegetation; boreal forest
Dr. Gareth Rees

E-Mail Website
Guest Editor
Scott Polar Research Institute, University of Cambridge, Lensfield Road, Cambridge CB2 1ER, UK
Interests: remote sensing of polar regions; snow cover; glaciers; high-latitude vegetation; animals at high latitudes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The boreal forest is the world’s largest terrestrial biome, accounting for about a third of the global forest area, and is of major climatic significance. Its size, remoteness and climate render it particularly difficult to study, and remote sensing methods have already proven themselves extremely valuable, although challenges remain. This is especially true of the Russian boreal forest, for which we have comparatively little up-to-date baseline data on biomass, carbon storage, etc. However, the increasing availability of high-quality data products from visible near-infrared remote sensing systems at a range of spatial and temporal resolutions and swath widths, together with emerging technologies for field-scale and landscape-scale data collection, are beginning to enable us to improve our understanding of the spatiotemporal variations in the biophysical parameters of the Russian boreal forest and their links to climatic and nonclimatic disturbances. As Guest Editors of this Special Issue, we invite contributions across the widest possible range of approaches to this area of research, including but not limited to: field methods, UAV (‘drone’) platforms, LiDAR techniques, upscaling, biomass estimation, allometric relations, hyperspectral remote sensing, optical, radar and thermal imagery, as well as their combinations, very high-resolution imagery, vegetation indices, leaf area index estimation, climate–vegetation interactions, anthropogenic disturbance of forest, forest fire remote sensing, citizen science, etc.

Dr. Olga Tutubalina
Dr. Gareth Rees
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 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 forest
  • Russia
  • Calibration and validation
  • Algorithm development
  • Remote sensing
  • Biomass

Published Papers (4 papers)

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22 pages, 27291 KiB  
Article
Assessing Changes in Boreal Vegetation of Kola Peninsula via Large-Scale Land Cover Classification between 1985 and 2021
by Ekaterina Sklyar and Gareth Rees
Remote Sens. 2022, 14(21), 5616; https://doi.org/10.3390/rs14215616 - 07 Nov 2022
Cited by 3 | Viewed by 1730
Abstract
The effective monitoring of boreal and tundra vegetation at different scales and environmental management at latitudes above 50 degrees North relies heavily on remote sensing. The vastness, remoteness and, in the case of Russia, the difficulty of access to boreal–tundra vegetation make it [...] Read more.
The effective monitoring of boreal and tundra vegetation at different scales and environmental management at latitudes above 50 degrees North relies heavily on remote sensing. The vastness, remoteness and, in the case of Russia, the difficulty of access to boreal–tundra vegetation make it an ideal technique for vegetation monitoring in the Kola peninsula, located predominantly beyond the Arctic circle in the European part of Russia. Since the 1930s, this area has been highly urbanised and exposed to strong influence by a number of different types of human impact, such as toxic pollutions, fires, mineral excavation, grazing, logging, etc. Extensive open archives of remote sensing imagery as well as recent advances in machine learning further enable the efficient use of remote sensing methods for assessing land cover changes. Here, we present the results of mapping northern vegetation land cover and changes in it over a large territory, in time and under human impact based on remote imagery from Landsat TM, ETM+ and OLI. We study the area of about 37,000 km2 located in the central part of the Kola peninsula in the boreal, pre-tundra and tundra between 1985 and 2021 with a time interval of approximately 5 years and confirm the correlations between the human pressure and the level of vegetation changes. We put those into the perspective of year-on-year changes in the temperature and precipitation regimes and describe the recovery of the damaged original boreal vegetation (dominated by spruce) through pine and deciduous vegetation. As a by-product of this study, we develop and test an approach for the semi-automated processing and classification of Landsat images using the novel TensorFlow machine learning technique (widely spread across other disciplines) that enables high-throughput classification, even on conventional hardware. Full article
(This article belongs to the Special Issue Remote Sensing of the Russian Boreal Forest)
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21 pages, 5762 KiB  
Article
High-Resolution Daily Emission Inventory of Biomass Burning in the Amur-Heilong River Basin Based on MODIS Fire Radiative Energy Data
by Zhenghan Lv, Yusheng Shi, Dianfan Guo, Yue Zhu, Haoran Man, Yang Zhang and Shuying Zang
Remote Sens. 2022, 14(16), 4087; https://doi.org/10.3390/rs14164087 - 21 Aug 2022
Cited by 1 | Viewed by 1866
Abstract
Open biomass burning (OBB) is one of the major factors that influences the regional climate environment and surface vegetation landscape, and it significantly affects the regional carbon cycle process and atmospheric environment. The Amur-Heilong River Basin (ARB) is a fire-prone region in high-latitude [...] Read more.
Open biomass burning (OBB) is one of the major factors that influences the regional climate environment and surface vegetation landscape, and it significantly affects the regional carbon cycle process and atmospheric environment. The Amur-Heilong River Basin (ARB) is a fire-prone region in high-latitude boreal forests. In this study, we used fire radiative power (FRP) obtained from a Moderate-resolution Imaging Spectroradiometer (MODIS) to estimate OBB emissions from the ARB and established a long-term series (2003–2020) with a high spatiotemporal resolution and a daily 1 km emissions inventory. The results show that the annual average emissions of CO2, CO, CH4, NMHCs, NOx, NH3, SO2, BC, OC, PM2.5, and PM10 were estimated to be 153.57, 6.16, 0.21, 0.78, 0.28, 0.08, 0.06, 0.04, 0.39, 0.66, and 0.85 Tg/a, respectively. Taking CO2 as an example, grassland fire in the dry season (mainly in April and October) was the largest contributor (87.18 Tg/a), accounting for 56.77% of the total CO2 emissions from the ARB, followed by forest fire prone to occur in April–May (56.53 Tg/a, 36.81%) and crop fire during harvest season (9.86 Tg/a, 6.42%). Among the three countries in the ARB, Russia released the most total CO2 emissions (2227.04 Tg), much higher than those of China (338.41 Tg) and Mongolia (198.83 Tg). The major fire types were crop fires (40.73%) on the Chinese side and grass fires on the Russian (56.67%) and Mongolian (97.56%) sides. Over the past decade, OBB CO2 emissions have trended downward (−0.79 Tg/a) but crop burning has increased significantly (+0.81 Tg/a). Up to 83.7% of crop fires occurred in China (2010–2020), with a concentrated and southward trend. Comparisons with the Global Fire Emission Dataset (GFED4.1s), the Fire INventory from NCAR (FINNv2.2), and the Global Fire Assimilation System (GFASv1.2) showed that our newly established emission inventory was in good agreement with these three datasets in the ARB. However, this multi-year, daily 1 km high-resolution emission inventory has the advantages of detecting more small fire emissions that were overlooked by coarse-grid datasets. The methods described here can be used as an effective means of estimating greenhouse gas and aerosol emissions from biomass combustion. Full article
(This article belongs to the Special Issue Remote Sensing of the Russian Boreal Forest)
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20 pages, 5151 KiB  
Article
Climate Variability May Delay Post-Fire Recovery of Boreal Forest in Southern Siberia, Russia
by Qiaoqi Sun, Arden Burrell, Kirsten Barrett, Elena Kukavskaya, Ludmila Buryak, Jörg Kaduk and Robert Baxter
Remote Sens. 2021, 13(12), 2247; https://doi.org/10.3390/rs13122247 - 09 Jun 2021
Cited by 9 | Viewed by 2871
Abstract
Prolonged dry periods and increased temperatures that result from anthropogenic climate change have been shown to increase the frequency and severity of wildfires in the boreal region. There is growing evidence that such changes in fire regime can reduce forest resilience and drive [...] Read more.
Prolonged dry periods and increased temperatures that result from anthropogenic climate change have been shown to increase the frequency and severity of wildfires in the boreal region. There is growing evidence that such changes in fire regime can reduce forest resilience and drive shifts in post-fire plant successional trajectories. The response of post-fire vegetation communities to climate variability is under-studied, despite being a critical phase determining the ultimate successional conclusion. This study investigated the responses of post-fire recruited species to climate change and inter-annual variability at 16 study sites that experienced high-severity fire events, mostly in early 2000, within the Scots pine forest-steppe zone of southeastern Siberia, Russia. These sites were originally dominated by Scots pine, and by 2018, they were recruited by different successional species. Additionally, three mature Scots pine stands were included for comparison. A Bayesian Additive Regression Trees (BART) approach was used to model the relationship between Landsat-derived Normalized Difference Vegetation Index (NDVI) time series, temperature and precipitation in the 15 years after a stand-replacing fire. Using the resulting BART models, together with six projected climate scenarios with increased temperature and enhanced inner-annual precipitation variability, we simulated NDVI at 5-year intervals for 15 years post-fire. Our results show that the BART models performed well, with in-sample Pseudo-R2 varying from 0.49 to 0.95 for fire-disturbed sites. Increased temperature enhanced greenness across all sites and across all three time periods since fires, exhibiting a positive feedback in a warming environment. Repeatedly dry spring periods reduced NDVI at all the sites and wetter summer periods following such dry springs could not compensate for this, indicating that a prolonged dry spring has a strong impact consistently over the entire early developmental stages from the initial 5 years to 15 years post-fire. Further, young forests showed higher climate sensitivity compared to the mature forest, irrespective of species and projected climatic conditions. Our findings suggest that a dry spring not only increases fire risk, but also delays recovery of boreal forests in southern Siberia. It also highlights the importance of changing rainfall seasonality as well as total rainfall in a changing climate for post-fire recovery of forest. Full article
(This article belongs to the Special Issue Remote Sensing of the Russian Boreal Forest)
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17 pages, 5342 KiB  
Technical Note
Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification
by W. Gareth Rees, Jack Tomaney, Olga Tutubalina, Vasily Zharko and Sergey Bartalev
Remote Sens. 2021, 13(21), 4483; https://doi.org/10.3390/rs13214483 - 08 Nov 2021
Cited by 9 | Viewed by 3078
Abstract
Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower [...] Read more.
Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data. Full article
(This article belongs to the Special Issue Remote Sensing of the Russian Boreal Forest)
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