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Forest-Climate Interactions in a Changing Environment: Remote Sensing and In Situ Data Analysis

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

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 15415

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Special Issue Editors

Prof. Dr. Alexander Olchev

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Guest Editor

Department of Meteorology and Climatology, Faculty of Geography, Lomonosov Moscow State University, 119991 Moscow, Russia
Interests: climate change; carbon cycle; greenhouse fluxes; mathematical modeling; remote sensing; field flux measurements
Special Issues, Collections and Topics in MDPI journals

Dr. Elena Novenko

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Guest Editor

1. Department of Physical Geography and Landscape Science, Faculty of Geography, Lomonosov Moscow State University, 119991 Moscow, Russia
2. Institute of Geography, Russian Academy of Science, 119017 Moscow, Russia
Interests: forest ecology; climate change; forest-climate interaction; forest fires

Dr. Natalia Levashova

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Guest Editor

Department of Mathematics, Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
Interests: inverse problems in remote sensing; radiative transfer; canopy reflection; forest-climate interaction; solar radiation; three-dimensional modeling; Monte-Karlo method

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to bring together recent studies that focus on providing us with a better understanding of the possible responses of forest ecosystems (species composition, forest functioning, gross and net primary production, evapotranspiration, etc.) to changing environmental conditions and their possible feedbacks to the climate system using integrated approaches based on remote sensing and in situ data. Over the past few decades, a number of remote sensing and field studies were conducted in order to derive the spatial and temporal forest conditions and variability as well as to quantify the energy, water, and carbon fluxes at the land surface–atmosphere interface on global, regional, and local scales. Despite this, a large number of very important questions related to forests’ dynamics and stability, as well as forest–atmosphere interactions, remain open and require new multifaceted studies.

For this Special Issue, we invite scientists working in atmospheric physics, forest ecology, meteorology, hydrology, or biogeochemistry to contribute new aggregated remote sensing and field studies of forest–atmosphere interactions on different spatial scales (from the ecosystem to the global level). Contributions may include, but are not limited to, the following:

remote sensing and in situ data analysis of forest structure, functioning, and damage associated with atmospheric hazards;
the response of various forest ecosystems to climate variability;
sensitivity of forest ecosystems to extreme weather events;
biophysical and biochemical forest feedbacks on atmospheric processes; and
spatial and temporal variability of GHG (greenhouse gas).

Prof. Dr. Alexander Olchev
Dr. Elena Novenko
Dr. Natalia Levashova
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

forest–atmosphere interaction
forest growth and functioning
biogeochemical cycles
GHG fluxes
climate variability
extreme weather events.

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Published Papers (6 papers)

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Research

12 pages, 4084 KiB

Open AccessCommunication

A Record-Setting 2021 Heat Wave in Western Canada Had a Significant Temporary Impact on Greenness of the World’s Largest Protected Temperate Rainforest

by Zihaohan Sang and Andreas Hamann

Remote Sens. 2023, 15(8), 2162; https://doi.org/10.3390/rs15082162 - 19 Apr 2023

Cited by 2 | Viewed by 2274

Abstract

Extreme climate anomalies are expected to become more frequent under climate change, and rare extreme events, such as the 2021 western North American heat wave, provide an opportunity for comparative empirical analysis of ecosystem resilience. This study evaluates anomalies in a remotely sensed enhanced vegetation index (EVI) in the aftermath of the record-setting western North American heat wave in 2021, with temperatures approaching 50 °C in coastal and interior regions of the Pacific Northwest. The results show that the forest ecosystems most affected were not necessarily those that experienced the highest absolute temperature values. Instead, the greatest reductions in greenness were observed across northern coastal temperate rainforests. Most affected were the cooler, very wet, hyper-maritime ecosystems that are normally buffered from large temperature fluctuation by a strong oceanic influence. In contrast, moisture-limited forests of the interior plateau of British Columbia, where most of the all-time record temperatures occurred, generally showed normal or even increased productivity during and after the heat wave. A putative explanation for this heat resistance of interior forests was normal or above average precipitation leading up to the heat event, allowing for transpirational cooling. Nevertheless, the data suggest that the largest protected coastal temperate rainforest in the world, with 6.4 million hectares, is comparatively more vulnerable to extreme heat waves, which are expected to become more frequent under climate warming, than other ecosystems of the Pacific Northwest. Full article

(This article belongs to the Special Issue Forest-Climate Interactions in a Changing Environment: Remote Sensing and In Situ Data Analysis)

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27 pages, 35878 KiB

Open AccessArticle

Critical Climate Periods Explain a Large Fraction of the Observed Variability in Vegetation State

by Anikó Kern, Zoltán Barcza, Roland Hollós, Edina Birinyi and Hrvoje Marjanović

Remote Sens. 2022, 14(21), 5621; https://doi.org/10.3390/rs14215621 - 7 Nov 2022

Cited by 2 | Viewed by 2190

Abstract

Previous studies have suggested that a major part of the observed variability in vegetation state might be associated with variability in climatic drivers during relatively short periods within the year. Identification of such critical climate periods, when a particular climate variable most likely has a pronounced influence on the vegetation state of a particular ecosystem, becomes increasingly important in the light of climate change. In this study, we present a method to identify critical climate periods for eight different semi-natural ecosystem categories in Hungary, in Central Europe. The analysis was based on the moving-window correlation between MODIS NDVI/LAI and six climate variables with different time lags during the period 2000–2020. Distinct differences between the important climate variables, critical period lengths, and direction (positive or negative correlations) have been found for different ecosystem categories. Multiple linear models for NDVI and LAI were constructed to quantify the multivariate influence of the environmental conditions on the vegetation state during the late summer. For grasslands, the best models for NDVI explained 65–87% variance, while for broad-leaved forests, the highest explained variance for LAI was up to 50%. The proposed method can be easily implemented in other geographical locations and can provide essential insight into the functioning of different ecosystem types. Full article

(This article belongs to the Special Issue Forest-Climate Interactions in a Changing Environment: Remote Sensing and In Situ Data Analysis)

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18 pages, 33955 KiB

Open AccessArticle

Delineating Fire-Hazardous Areas and Fire-Induced Patterns Based on Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires in Northeast China

by Wenjun Li, Peng Li and Zhiming Feng

Remote Sens. 2022, 14(20), 5115; https://doi.org/10.3390/rs14205115 - 13 Oct 2022

Cited by 5 | Viewed by 1755

Abstract

(1) Background: Fire affects global agricultural and/or forest ecosystems with high biomass accumulation. However, the delineation of fire-hazardous areas based on satellite-derived active fire intensity is not well-studied. Therefore, examining the characteristics of fire occurrence and development plays an important role in zoning fire-hazardous areas and promoting fire management. (2) Methods: A fire intensity (FI) index was developed with Visible Infrared Imaging Radiometer Suite (VIIRS) active fires and then applied to identify fire-hazardous areas in Northeast China. Combined with terrain, land cover and net primary productivity (NPP), the spatial and temporal characteristics of active fire occurrence were consistently analyzed. Next, a conceptual decision tree model was constructed for delineating fire-induced patterns impacted by varied factors in Northeast China. (3) Results: The accidental, frequent, prone and high-incidence areas of active fires defined by the FI index accounted for 31.62%, 30.97%, 26.23% and 11.18%, respectively. More than 90% of active fires occurred in areas with altitude <350 m above sea level (asl), slope <3° and NPP between 2500–5000 kg·C/m². Similarly, about 75% occurred in cropland and forest. Then, four fire-induced conceptual patterns driven by different factors were classified, including the agricultural and forest active fire-induced patterns (i.e., the Agri-pattern and FRST pattern) with NPP ranging 2500–5000 kg·C/m², and two others related to settlements and unused land with an altitude <350 m asl. The Agri-pattern dominates in Northeast China because of agricultural straw burning. (4) Conclusions: Despite the national bans of open burning of straws, active fires due to agricultural production have occurred frequently in Northeast China in the last decade, followed by small and sporadic forest fires. The approach for defining fire-hazardous areas and varied fire occurrence patterns is of significance for fire management and risk prediction at continental to global scales. Full article

(This article belongs to the Special Issue Forest-Climate Interactions in a Changing Environment: Remote Sensing and In Situ Data Analysis)

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26 pages, 4751 KiB

Open AccessArticle

On the Impacts of Historical and Future Climate Changes to the Sustainability of the Main Sardinian Forests

by Sara Simona Cipolla and Nicola Montaldo

Remote Sens. 2022, 14(19), 4893; https://doi.org/10.3390/rs14194893 - 30 Sep 2022

Cited by 3 | Viewed by 2221

Abstract

The Mediterranean Basin is affected by climate changes that may have negative effects on forests. This study aimed to evaluate the ability of 17 forests located in the Island of Sardinia to resist or adapt to the past and future climate. Sardinia is experiencing a decreasing anthropic pressure on forests, but drought-triggered dieback in trees was recently observed and confirmed by the analysis of 20 years of satellite tree-cover data (MOD44B). Significant negative trends in yearly tree cover have affected the broad-leaved vegetation, while significative positive trends were found in the bushy sclerophyllous vegetation. Vegetation behavior resulted in being related to the mean annual precipitation (MAP); for MAP < 700 mm, we found a decline in the tall broad-leaved stands and an increase in the short ones, and the opposite was found for bushy sclerophyllous vegetations. In forests with MAP > 700 mm, both stands are stable, regardless of the growing trends in the vapor-pressure deficit (VPD) and temperature. No significative correlation between bushy sclerophyllous tree cover and the climate drivers was found, while broad-leaved tree cover is positively related to MAP1990–2019 and negatively related to the growing annual VPD. We modeled those relationships, and then we used them to coarsely predict the effects of twelve future scenarios (derived from HADGEM2-AO (CMIP5) and HadGEM3-GC31-LL (CMIP6) models) on forest tree covers. All scenarios show an annual VPD increase, and the higher its increase, the higher the trees-cover loss. The future changes in precipitation were contrasting. SC6, in line with past precipitation trends, predicts a further drop in the mean annual precipitation (−7.6%), which would correspond to an average 2.1-times-greater reduction in the tree cover (−16.09%). The future changes in precipitation for CMIP6 scenarios agree on a precipitation reduction in the range of −3.4% (SC7) to −14.29% (S12). However, although the reduction in precipitation predicted in SC12 is almost double that predicted in SC6, the consequent average reduction in TC is comparable and stands at −16%. On the contrary, SC2 predicts a turnaround with an abrupt increase of precipitation (+21.5%) in the upcoming years, with a reduction in the number of forests in water-limited areas and an increase in the percentage of tree cover in almost all forests. Full article

(This article belongs to the Special Issue Forest-Climate Interactions in a Changing Environment: Remote Sensing and In Situ Data Analysis)

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19 pages, 9540 KiB

Open AccessArticle

Estimating Long-Term Average Carbon Emissions from Fires in Non-Forest Ecosystems in the Temperate Belt

by Andrey Ostroukhov, Elena Klimina, Viktoriya Kuptsova and Daisuke Naito

Remote Sens. 2022, 14(5), 1197; https://doi.org/10.3390/rs14051197 - 28 Feb 2022

Cited by 5 | Viewed by 2461

Abstract

Research into pyrogenic carbon emissions in the temperate belt of the Russian Federation has traditionally focused on the impact of forest fires. Nevertheless, ecosystems in which wildfires also make a significant contribution to anthropogenic CO₂ emissions are poorly studied. We evaluated the carbon emissions of fires in the non-forest ecosystems of the Middle Amur Lowland, in the Khabarovsk Territory of the Russian Federation. Our study is based on long-term Earth remote sensing data of medium spatial resolution (Landsat 5, 7, and 8) and expeditionary studies (2018–2021). The assessment of carbon directly emitted from wildfires in meadow and meadow–mire temperate ecosystems in the Middle Amur lowland shows that specific emissions from such ecosystems vary, from 1.09 t/ha in dwarf shrub–sphagnum and sphagnum–ledum and sedge–reed fens to 6.01 t/ha in reed–forb, forb, reed, and sedge meadows. Meanwhile, carbon emissions specifically from fires in meadow and meadow–mire ecosystems are less significant—often an order of magnitude less than carbon emissions from forest fires (which reach 37 tC/ha). However, due to their high frequency and the large areas of land burned annually, the total carbon emissions from such fires are comparable to annual emissions from fires in forested areas. The results obtained show that the inadequacy of the methods used in the automatic mapping of burns leads to a significant underestimation of the area of grassland fires and carbon emissions from non-forest fires. Full article

(This article belongs to the Special Issue Forest-Climate Interactions in a Changing Environment: Remote Sensing and In Situ Data Analysis)

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13 pages, 2478 KiB

Open AccessArticle

Wildfire Dynamics along a North-Central Siberian Latitudinal Transect Assessed Using Landsat Imagery

by Yury Dvornikov, Elena Novenko, Mikhail Korets and Alexander Olchev

Remote Sens. 2022, 14(3), 790; https://doi.org/10.3390/rs14030790 - 8 Feb 2022

Cited by 5 | Viewed by 2910

Abstract

The history of wildfires along a latitudinal transect from forest–tundra to middle taiga in North-Central Siberia was reconstructed for the period from 1985 to 2020 using Landsat imagery. The transect passed through four key regions (75 × 75 km²) with different climate and landscape conditions that allowed us to evaluate regional wildfire dynamics as well as estimate differences in post-fire forest recovery. The Level-2A Landsat data (TM, ETM+, and OLI) were used to derive: (i) burned area (BA) locations, (ii) timing of wildfire occurrence (date, month, or season), (iii) fire severity, and (iv) trends in post-fire vegetation recovery. We used pre-selected and pre-processed scenes suitable for BA mapping taken within four consecutive time intervals covering the entire period of data analysis (1985–2020). Pre- and post-fire dynamics of forest vegetation were described using spectral indices, i.e., NBR and NDVI. We found that during the last three decades, the maximum BA occurred in the southernmost Vanavara region where ≈58% of the area burned. Total BA gradually decreased to the northwest with a minimum in the Igarka region (≈1%). Nearly half of these BAs appeared between summer 2013 and autumn 2020 due to higher frequency of hot and dry weather. The most severe wildfires were detected in the most northeastern Tura region. Analysis of NDVI and NBR dynamics showed that the mean period of post-fire vegetation recovery ranged between 20 and 25 years. The time of vegetation recovery at BAs with repeat wildfires and high severity was significantly longer. Full article

(This article belongs to the Special Issue Forest-Climate Interactions in a Changing Environment: Remote Sensing and In Situ Data Analysis)

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