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Remote Sensing for Mountain Ecosystems

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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 January 2023) | Viewed by 29955

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Dr. Bogdan Andrei Mihai

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Department of Geomorphology-Pedology-Geomatics, Faculty of Geography, University of Bucharest, 050663 Bucharest, Romania
Interests: land use/land cover mapping; vegetation mapping; change detection; image classification; urban remote sensing; GIS; mapping and digital cartography
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Prof. Dr. Mihai Nita

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Department of Forest Engineering, Universitatea Transilvania Brasov, Braşov, Romania
Interests: remote sensing; GIS; forest and water; forest management; machine learning
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Dr. Marcel Torok

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Department of Geography, Faculty of Chemistry, Biology, Geography Timișoara, West University of Timișoara, 300223 Timișoara, Romania
Interests: remote sensing; object based image analysis (OBIA); geographic information systems (GIS); cartography; geomorphology
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Special Issue Information

Dear Colleagues,

Mountain environments represent a dynamic interface of the climate and environmental change, and are a permanent topic for researchers from all over the world. Remote sensing technology advances in terms of sensor resolution, algorithms for data processing, analysis, and product development have opened new directions in the current context of Earth Observation (EO). EO is an important tool to assess mountain environments, which are well known for their limited accessibility and feature diverse and dynamic ecosystems. This Special Issue proposed by Remote Sensing is an opportunity to publish and disseminate some of the up-to-date research results focused on the role of satellite and aerial imagery in the advanced evaluation and mapping of the mountain ecosystem changes at different scales, from local to regional and global levels. Some thematic aspects we propose include: the spatiotemporal modelling of mountain forest and alpine ecosystem disturbances under the impact of climate change and anthropogenic pressure, the quantitative mapping of the treeline ecotone and the recent transformation of montane vegetation zonation, land cover change and ecosystem dynamics mapping in mountain regions, the objective mapping and evaluation of the mountain depopulation impact over the local to regional ecosystem state, and natural hazard management. Authors are encouraged to test new techniques and methods such as big data processing for Earth Observation, machine learning, etc., and to enlarge the evaluation of the recent satellite sensors from different countries and spatial agencies in the context of mountain environmental analysis.

Dr. Bogdan Andrei Mihai
Dr. Mihai Nita
Dr. Marcel Torok
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

Mountain treeline ecotone
Mountain ecosystem disturbances
Change detection
Earth Observation
Big data processing
Machine learning
Mapping
Spatiotemporal modeling

Related Special Issue

Remote Sensing for Mountain Ecosystems II in Remote Sensing (6 articles)

Published Papers (10 papers)

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Research

23 pages, 30423 KiB

Open AccessArticle

Land Cover Changes of the Qilian Mountain National Park in Northwest China Based on Phenological Features and Sample Migration from 1990 to 2020

by Yanyun Nian, Zeyu He, Wenhui Zhang and Long Chen

Remote Sens. 2023, 15(4), 1074; https://doi.org/10.3390/rs15041074 - 16 Feb 2023

Cited by 2 | Viewed by 1580

Abstract

The spatial and temporal variation analysis of land cover classification is important for studying the distribution and transformation of regional land cover changes. The Qilian Mountain National Park (QMNP), an important ecological barrier in northwestern China, has lacked land cover products for long time series. The Landsat images available on the Google Earth Engine (GEE) make it possible to analyze the land cover changes over the past three decades. The purpose of this study was to generate a long time series of datasets of land cover classification based on the method of sample migration in the QMNP in Northwest China. The Landsat 5, 7, and 8 images and field sample data were combined with multiple image features and the random forest algorithm to complete the land cover classification of the QMNP from 1990 to 2020. The results indicate that (1) the method of Jeffries–Matusita (J-M) distance can reduce image feature redundancy and show that elevation and phenological features have good differentiability among land cover types that were easy to mix with feature classes; (2) the spatial distribution of land cover every 10 years between 1990 and 2020 was consistent in the QMNP, and there were obvious differences in land cover from the east to the west part of the QMNP, with a large area of vegetation distribution in Sunan county in the central part and Tianzhu county in the east part of the QMNP; (3) over the past 30 years, forests and grasslands decreased by 62.2 km² and 794.7 km², respectively, while shrubs increased by 442.9 km² in the QMNP. The conversion of bare land to grassland and the interconversion between different vegetation types were the main patterns of land cover changes, and the land cover changes were mainly concentrated in pastoral areas, meaning that human activity was the main factor of land cover changes; and (4) when the samples of 2020 were migrated to 2010, 2000, and 1990, the overall classification accuracies were 89.7%, 88.0%, 86.0%, and 83.9%, respectively. The results show that the vegetation conservation process in the QMNP was closely related to human activities. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)

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25 pages, 24809 KiB

Open AccessArticle

Synergism of Multi-Modal Data for Mapping Tree Species Distribution—A Case Study from a Mountainous Forest in Southwest China

by Pengfei Zheng, Panfei Fang, Leiguang Wang, Guanglong Ou, Weiheng Xu, Fei Dai and Qinling Dai

Remote Sens. 2023, 15(4), 979; https://doi.org/10.3390/rs15040979 - 10 Feb 2023

Cited by 2 | Viewed by 1289

Abstract

Accurately mapping tree species is crucial for forest management and conservation. Most previous studies relied on features derived from optical imagery, and digital elevation data and the potential of synthetic aperture radar (SAR) imagery and other environmental factors have, generally, been underexplored. Therefore, the aim of this study is to evaluate the potential of fusing freely available multi-modal data for accurately mapping tree species. Sentinel-2, Sentinel-1, and various environmental datasets over a large mountainous forest in Southwest China were obtained and analyzed using Google Earth Engine (GEE). Seven data cases considering the individual or joint performance of different features, and four additional cases considering a novel clustering-based feature selection method, were analyzed. All 11 cases were assessed using three machine learning algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient boosting tree (XGBoost). The best performance, with an overall accuracy of 77.98%, was attained from the case with all features and the random forest classifier. Sentinel-2 data alone exhibited similar performance as environmental data in terms of overall accuracy. Similar species, such as oak and birch, cannot be spectrally discriminated based on Sentinel-2-based features alone. The addition of SAR features improved discrimination, especially when distinguishing between some coniferous and deciduous species, but also decreased accuracy for oak. The analysis based on different data cases and feature importance rankings indicated that environmental features are important. The random forest outperformed other models, and a better prediction was achieved for planted tree species compared to that for the natural forest. These results suggest that accurately mapping tree species over large mountainous areas is feasible with freely accessible multi-modal data, especially when considering environmental factors. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)

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18 pages, 5158 KiB

Open AccessArticle

Dynamic Detection of Forest Change in Hunan Province Based on Sentinel-2 Images and Deep Learning

by Jun Xiang, Yuanjun Xing, Wei Wei, Enping Yan, Jiawei Jiang and Dengkui Mo

Remote Sens. 2023, 15(3), 628; https://doi.org/10.3390/rs15030628 - 20 Jan 2023

Cited by 5 | Viewed by 2119

Abstract

Dynamic detection of forest change is the fundamental method of monitoring forest resources and an essential means of preserving the accuracy and timeliness of forest land resource data. This study focuses on a deep learning-based method for dynamic forest change detection using Sentinel-2 satellite data, especially within mountainous areas. First, the performance of various deep learning models (U-Net++, U-Net, LinkNet, DeepLabV3+, and STANet) and various loss functions (CrossEntropyLoss(CELoss), DiceLoss, FocalLoss, and their combinations) are compared on a self-made dataset. Next, the best model and loss function is used to predict the annual forest change in Hunan Province from 2017 to 2021, and the detection results are evaluated in 12 sample areas. Finally, forest changes are detected in Sentinel-2 images for each quarter of 2017–2021. In addition, a dynamic detection map of forest change in Hunan Province from 2017 to 2021 is drawn. The results reveal that the U-Net++ model and the CELoss performed the best on the self-made dataset, with a Precision of 0.795, a Recall of 0.748, and an F1-score of 0.771. The results of annual and quarterly forest change detection were consistent with the changes in the Sentinel-2 images with accurate boundaries. This result demonstrates the high practicality and generalizability of the method used in this paper. This paper achieves a rapid and accurate extraction of multi-temporal Sentinel-2 image forest change areas based on the U-Net++ model, which can be used as a benchmark for future large territorial areas monitoring and management of forest resources. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)

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29 pages, 8926 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping

by Anna Candotti, Michaela De Giglio, Marco Dubbini and Enrico Tomelleri

Remote Sens. 2022, 14(23), 6105; https://doi.org/10.3390/rs14236105 - 01 Dec 2022

Cited by 7 | Viewed by 3278

Abstract

The occurrence of extreme windstorms and increasing heat and drought events induced by climate change leads to severe damage and stress in coniferous forests, making trees more vulnerable to spruce bark beetle infestations. The combination of abiotic and biotic disturbances in forests can cause drastic environmental and economic losses. The first step to containing such damage is establishing a monitoring framework for the early detection of vulnerable plots and distinguishing the cause of forest damage at scales from the management unit to the region. To develop and evaluate the functionality of such a monitoring framework, we first selected an area of interest affected by windthrow damage and bark beetles at the border between Italy and Austria in the Friulian Dolomites, Carnic and Julian Alps and the Carinthian Gailtal. Secondly, we implemented a framework for time-series analysis with open-access Sentinel-2 data over four years (2017–2020) by quantifying single-band sensitivity to disturbances. Additionally, we enhanced the framework by deploying vegetation indices to monitor spectral changes and perform supervised image classification for change detection. A mean overall accuracy of 89% was achieved; thus, Sentinel-2 imagery proved to be suitable for distinguishing stressed stands, bark-beetle-attacked canopies and wind-felled patches. The advantages of our methodology are its large-scale applicability to monitoring forest health and forest-cover changes and its usability to support the development of forest management strategies for dealing with massive bark beetle outbreaks. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)

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24 pages, 5242 KiB

Open AccessArticle

Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy

by Moritz Rösch, Ruth Sonnenschein, Sebastian Buchelt and Tobias Ullmann

Remote Sens. 2022, 14(13), 3190; https://doi.org/10.3390/rs14133190 - 02 Jul 2022

Cited by 5 | Viewed by 2344

Abstract

The mountain pine (Pinus mugo ssp. Mugo Turra) is an important component of the alpine treeline ecotone and fulfills numerous ecosystem functions. To understand and quantify the impacts of increasing logging activities and climatic changes in the European Alps, accurate information on the occurrence and distribution of mountain pine stands is needed. While Earth observation provides up-to-date information on land cover, space-borne mapping of mountain pines is challenging as different coniferous species are spectrally similar, and small-structured patches may remain undetected due to the sensor’s spatial resolution. This study uses multi-temporal optical imagery from PlanetScope (3 m) and Sentinel-2 (10 m) and combines them with additional features (e.g., textural statistics (homogeneity, contrast, entropy, spatial mean and spatial variance) from gray level co-occurrence matrix (GLCM), topographic features (elevation, slope and aspect) and canopy height information) to overcome the present challenges in mapping mountain pine stands. Specifically, we assessed the influence of spatial resolution and feature space composition including the GLCM window size for textural features. The study site is covering the Sarntal Alps, Italy, a region known for large stands of mountain pine. Our results show that mountain pines can be accurately mapped (PlanetScope (90.96%) and Sentinel-2 (90.65%)) by combining all features. In general, Sentinel-2 can achieve comparable results to PlanetScope independent of the feature set composition, despite the lower spatial resolution. In particular, the inclusion of textural features improved the accuracy by +8% (PlanetScope) and +3% (Sentinel-2), whereas accuracy improvements of topographic features and canopy height were low. The derived map of mountain pines in the Sarntal Alps supports local forest management to monitor and assess recent and ongoing anthropogenic and climatic changes at the treeline. Furthermore, our study highlights the importance of freely available Sentinel-2 data and image-derived textural features to accurately map mountain pines in Alpine environments. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)

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22 pages, 8926 KiB

Open AccessArticle

Improved Object-Based Estimation of Forest Aboveground Biomass by Integrating LiDAR Data from GEDI and ICESat-2 with Multi-Sensor Images in a Heterogeneous Mountainous Region

by Lin Chen, Chunying Ren, Guangdao Bao, Bai Zhang, Zongming Wang, Mingyue Liu, Weidong Man and Jiafu Liu

Remote Sens. 2022, 14(12), 2743; https://doi.org/10.3390/rs14122743 - 07 Jun 2022

Cited by 19 | Viewed by 3381

Abstract

Accurate and effective mapping of forest aboveground biomass (AGB) in heterogeneous mountainous regions is a huge challenge but an urgent demand for resource managements and carbon storage monitoring. Conventional studies have related the plot-measured or LiDAR-based biomass to remote sensing data using pixel-based approaches. The object-based relationship between AGB and multi-source data from LiDAR, multi-frequency radar, and optical sensors were insufficiently studied. It deserves the further exploration that maps forest AGB using the object-based approach and combines LiDAR data with multi-sensor images, which has the smaller uncertainty of positional discrepancy and local heterogeneity, in heterogeneous mountainous regions. To address the improvement of mapping accuracy, satellite LiDAR data from GEDI and ICEsat-2, and images of ALOS-2 yearly mosaic L band SAR (Synthetic Aperture Radar), Sentinel-1 C band SAR, Sentinel-2 MSI, and ALOS-1 DSM were combined for pixel- and object-based forest AGB mapping in a vital heterogeneous mountainous forest. For the object-based approach, optimized objects during a multiresolution segmentation were acquired by the ESP (Estimation of the Scale Parameter) tool, and suitable predictors were selected using an algorithm named VSURF (Variable Selection Using Random Forests). The LiDAR variables at the footprint-level were extracted to connect field plots to the multi-sensor objects as a linear bridge. It was shown that forests’ AGB values varied by elevations with a mean value of 142.58 Mg/ha, ranging from 12.61 to 514.28 Mg/ha. The north slope with the lowest elevation (<1100 m) had the largest mean AGB, while the smallest mean AGB was located in the south slope with the altitude above 2000 m. Using independent validation samples, it was indicated by the accuracy comparison that the object-based approach performed better on the precision with relative improvement based on root-mean-square errors (RI_RMSE) of 4.46%. The object-based approach also selected more optimized predictors and markedly decreased the prediction time than the pixel-based analysis. Canopy cover and height explained forest AGB with their effects on biomass varying according to the elevation. The elevation from DSM and variables involved in red-edge bands from MSI were the most contributive predictors in heterogeneous temperate forests. This study is a pioneering exploration of object-based AGB mapping by combining satellite data from LiDAR, MSI, and SAR, which offers an improved methodology for regional carbon mapping in the heterogeneous mountainous forests. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)

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24 pages, 10770 KiB

Open AccessArticle

Forest Habitat Fragmentation in Mountain Protected Areas Using Historical Corona KH-9 and Sentinel-2 Satellite Imagery

by Bogdan Olariu, Marina Vîrghileanu, Bogdan-Andrei Mihai, Ionuț Săvulescu, Liviu Toma and Maria-Gianina Săvulescu

Remote Sens. 2022, 14(11), 2593; https://doi.org/10.3390/rs14112593 - 28 May 2022

Cited by 5 | Viewed by 3237

Abstract

Forest habitat fragmentation is one of the global environmental issues of concern as a result of forest management practices and socioeconomic drivers. In this context, a constant evaluation of natural habitat conditions still remains a challenge in order to achieve a general image of the environmental state of a protected area for proper sustainable management. The purpose of our study was to evaluate the evolution of forest habitat in the last 40 years, focusing on Bucegi Natural Park, one of the most frequented protected areas in Romania, as relevant for highly human-impacted areas. Our approach integrates a historical panchromatic Corona KH-9 image from 1977 and present-day Sentinel-2 multispectral data from 2020 in order to calculate a series of spatial metrics that reveal changes in the pattern of the forest habitat and illustrate forest habitat fragmentation density. Object-based oriented analysis with supervised maximum likelihood classification was employed for the production of forest cover fragmentation maps. Ten landscape metrics were adapted to the analysis context, from patch statistics to proximity index. The results show a general growth of the forest surface but also an increase in habitat fragmentation in areas where tourism was developed. Fragmentation indices explain that larger and compact patches feature natural park protected forests after the spruce–fir secondary canopies were grown during the last 4–5 decades. The number of patches decreased to half, and their average size is double that of before. The method can be of extensive use for environmental monitoring in protected areas management and for understanding the environmental history connected to present-day problems that are to be fixed under rising human pressure. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)

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15 pages, 4042 KiB

Open AccessCommunication

Temperature Mediates the Dynamic of MODIS NPP in Alpine Grassland on the Tibetan Plateau, 2001–2019

by Jinxia Cui, Yanding Wang, Tiancai Zhou, Lili Jiang and Qingwen Qi

Remote Sens. 2022, 14(10), 2401; https://doi.org/10.3390/rs14102401 - 17 May 2022

Cited by 13 | Viewed by 1632

Abstract

Although alpine grassland net primary productivity (NPP) plays an important role in balancing the carbon cycle and is extremely vulnerable to climate factors, on the Tibetan Plateau, the generalized effect of climate factors on the NPP in areas with humid and arid conditions is still unknown. Hence, we determined the effects of precipitation and temperature on the MODIS NPP in alpine grassland areas from 2001 to 2019 according to information from humid and arid climatic regions. On a spatial scale, we found that temperature generated a larger effect on the NPP than precipitation did in humid regions, but as a primary factor, precipitation had an impact on the NPP in arid regions. These results suggest that temperature and precipitation are the primary limiting factors for plant growth in humid and arid regions. We also found that temperature produced a greater effect on the NPP in humid regions than in arid regions, but no significant differences were observed in the effects of precipitation on the NPP in humid and arid regions. In a time series (2001–2019), the effects of precipitation and temperature on the NPP presented fluctuating decrease (R² = 0.28, p < 0.05) and increase (R² = 0.24, p < 0.05) trends in arid regions. However, the effect of the climate on the NPP remained stable in humid regions. In both humid and arid regions, the dynamics of the NPP from 2001 to 2019 were mediated by an increase in temperature. Specifically, 35.9% and 2.57% of the dynamic NPP in humid regions and 45.1 and 7.53% of the dynamic NPP in arid regions were explained by variations in the temperature and precipitation, respectively. Our findings highlighted that grassland areas in humid regions can adapt to dynamic climates, but plants in arid regions are sensitive to changes in the climate. These findings can increase our understanding of climate and ecological responses and provide a framework for adapting management practices. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)

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21 pages, 5015 KiB

Open AccessArticle

A Novel Approach for Forest Fragmentation Susceptibility Mapping and Assessment: A Case Study from the Indian Himalayan Region

by Amit Kumar Batar, Hideaki Shibata and Teiji Watanabe

Remote Sens. 2021, 13(20), 4090; https://doi.org/10.3390/rs13204090 - 13 Oct 2021

Cited by 7 | Viewed by 4782

Abstract

An estimation of where forest fragmentation is likely to occur is critically important for improving the integrity of the forest landscape. We prepare a forest fragmentation susceptibility map for the first time by developing an integrated model and identify its causative factors in the forest landscape. Our proposed model is based upon the synergistic use of the earth observation data, forest fragmentation approach, patch forests, causative factors, and the weight-of-evidence (WOE) method in a Geographical Information System (GIS) platform. We evaluate the applicability of the proposed model in the Indian Himalayan region, a region of rich biodiversity and environmental significance in the Indian subcontinent. To obtain a forest fragmentation susceptibility map, we used patch forests as past evidence of completely degraded forests. Subsequently, we used these patch forests in the WOE method to assign the standardized weight value to each class of causative factors tested by the Variance Inflation Factor (VIF) method. Finally, we prepare a forest fragmentation susceptibility map and classify it into five levels: very low, low, medium, high, and very high and test its validity using 30% randomly selected patch forests. Our study reveals that around 40% of the study area is highly susceptible to forest fragmentation. This study identifies that forest fragmentation is more likely to occur if proximity to built-up areas, roads, agricultural lands, and streams is low, whereas it is less likely to occur in higher altitude zones (more than 2000 m a.s.l.). Additionally, forest fragmentation will likely occur in areas mainly facing south, east, southwest, and southeast directions and on very gentle and gentle slopes (less than 25 degrees). This study identifies Himalayan moist temperate and pine forests as being likely to be most affected by forest fragmentation in the future. The results suggest that the study area would experience more forest fragmentation in the future, meaning loss of forest landscape integrity and rich biodiversity in the Indian Himalayan region. Our integrated model achieved a prediction accuracy of 88.7%, indicating good accuracy of the model. This study will be helpful to minimize forest fragmentation and improve the integrity of the forest landscape by implementing forest restoration and reforestation schemes. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)

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22 pages, 7780 KiB

Open AccessArticle

Assessment of Sentinel-2 Images, Support Vector Machines and Change Detection Algorithms for Bark Beetle Outbreaks Mapping in the Tatra Mountains

by Robert Migas-Mazur, Marlena Kycko, Tomasz Zwijacz-Kozica and Bogdan Zagajewski

Remote Sens. 2021, 13(16), 3314; https://doi.org/10.3390/rs13163314 - 21 Aug 2021

Cited by 16 | Viewed by 4021

Abstract

Cambiophagous insects, fires and windthrow cause significant forest disturbances, generating ecological changes and economical losses. The bark beetle (Ips typographus L.), inhabiting coniferous forests and eliminating weakened trees, plays a key role in posing a threat to tree stands, which are dominated by Norway spruce (Picea abies) and covers a large part of mountain areas, as well as the lowlands of Northern, Central and Eastern Europe. Due to the dynamics of the phenomena taking place, the EU recommends constant monitoring of forests in terms of large-area disturbances and factors affecting tree stands’ susceptibility to destruction. The right tools for this are multispectral satellite images, which regularly and free of charge provide up-to-date information on changes in the environment. The aim of this study was to develop a method of identifying disturbances of spruce stands, including the identification of bark beetle outbreaks. Sentinel 2 images from 2015–2018 were used for this purpose; the reference data were high-resolution aerial images, satellite WorldView 2, as well as field verification data. Support Vector Machines (SVM) distinguished six classes: deciduous forests, coniferous forests, grasslands, rocks, snags (dieback of standing trees) and cuts/windthrow. Remote sensing vegetation indices, Multivariate Alteration Detection (MAD), Multivariate Alteration Detection/Maximum Autocorrelation Factor (MAD/MAF), iteratively re-weighted Multivariate Alteration Detection (iMAD) and trained SVM signatures from another year, stacked band rasters allowed us to identify: (1) no changes; (2) dieback of standing trees; (3) logging or falling down of trees. The overall accuracy of the SVM classification oscillated between 97–99%; it was observed that in 2015–2018, as a result of the windthrow and bark beetle outbreaks and the consequences of those natural disturbances (e.g., sanitary cuts), approximately 62.5 km² of coniferous stands (29%) died in the studied area of the Tatra Mountains. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)

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