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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 (31 December 2020) | Viewed by 38624

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Special Issue Editor

Dr. Fernando Espírito- Santo

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Guest Editor

School of Geography, Geology and the Environment, University of Leicester, UK
Interests: remote sensing; tropical forests; ecosystem modelling; carbon cycle; community and landscape ecology; forest productivity

Special Issue Information

Dear Colleagues,

The frequency, severity and intensity of natural forest disturbances play significant roles in forest dynamics. At the small scale, branch or tree-fall gaps and subsequent recovery are important mechanisms for carbon cycling. At the landscape scale, large disturbances (e.g., windthrow, blowdowns, wildfires, droughts, flooding, and others) may also influences the structure and composition of forests. Quantitative studies of natural forest disturbances across the entire sectrum of natural forest disturbances are rare. Remote sensing, coupled with intense fieldwork data collection or models, provides the means to analyse forest dynamics at multiple scales. Thus, this Special Issue focuses on “Remote Sensing of Natural Forest Disturbances.”

We invite authors to submit manuscripts that detail the use of remote sensing approaches to understand and quantify natural processes leading to forest disturbances. Our focus is on natural processes related to different mechanisms of natural forest disturbances that are linked to tree mortality.

The Special Issue will include studies in the following areas:

Ecological or interdisciplinary remote sensing (RS) studies that aim to understand the effects of natural forests disturbances on the carbon cycle, biodiversity, and ecosystem services;
The monitoring of status or stress of natural disturbances in forest ecosystems and their interactions with RS;
Approaches to monitoring natural forest disturbances using RS, ground field collections, models, or combinations;
Statistical methods useful to quantify natural forest disturbances in terms of size, frequency, and intensity;
Satellites or sensors useful to quantify processes and partners of natural forest disturbances at multiple scales.

Dr. Fernando Espírito- Santo
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

natural forest disturbances
tree mortality
forest turnover
remote sensing
carbon cycle
modelling

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Published Papers (8 papers)

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23 pages, 12214 KiB

Open AccessArticle

Responding to Large-Scale Forest Damage in an Alpine Environment with Remote Sensing, Machine Learning, and Web-GIS

by Marco Piragnolo, Francesco Pirotti, Carlo Zanrosso, Emanuele Lingua and Stefano Grigolato

Remote Sens. 2021, 13(8), 1541; https://doi.org/10.3390/rs13081541 - 15 Apr 2021

Cited by 24 | Viewed by 3857

Abstract

This paper reports a semi-automated workflow for detection and quantification of forest damage from windthrow in an Alpine region, in particular from the Vaia storm in October 2018. A web-GIS platform allows to select the damaged area by drawing polygons; several vegetation indices (VIs) are automatically calculated using remote sensing data (Sentinel-2A) and tested to identify the more suitable ones for quantifying forest damage using cross-validation with ground-truth data. Results show that the mean value of NDVI and NDMI decreased in the damaged areas, and have a strong negative correlation with severity. RGI has an opposite behavior in contrast with NDVI and NDMI, as it highlights the red component of the land surface. In all cases, variance of the VI increases after the event between 0.03 and 0.15. Understorey not damaged from the windthrow, if consisting of 40% or more of the total cover in the area, undermines significantly the sensibility of the VIs to detecting and predicting severity. Using aggregational statistics (average and standard deviation) of VIs over polygons as input to a machine learning algorithm, i.e., Random Forest, results in severity prediction with regression reaching a root mean square error (RMSE) of 9.96, on a severity scale of 0–100, using an ensemble of area averages and standard deviations of NDVI, NDMI, and RGI indices. The results show that combining more than one VI can significantly improve the estimation of severity, and web-GIS tools can support decisions with selected VIs. The reported results prove that Sentinel-2 imagery can be deployed and analysed via web-tools to estimate forest damage severity and that VIs can be used via machine learning for predicting severity of damage, with careful evaluation of the effect of understorey in each situation. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Forest Disturbances)

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15 pages, 4134 KiB

Open AccessArticle

Improved Boreal Forest Wildfire Fuel Type Mapping in Interior Alaska Using AVIRIS-NG Hyperspectral Data

by Christopher William Smith, Santosh K. Panda, Uma Suren Bhatt and Franz J. Meyer

Remote Sens. 2021, 13(5), 897; https://doi.org/10.3390/rs13050897 - 27 Feb 2021

Cited by 14 | Viewed by 5962

Abstract

In Alaska the current wildfire fuel map products were generated from low spatial (30 m) and spectral resolution (11 bands) Landsat 8 satellite imagery which resulted in map products that not only lack the granularity but also have insufficient accuracy to be effective in fire and fuel management at a local scale. In this study we used higher spatial and spectral resolution AVIRIS-NG hyperspectral data (acquired as part of the NASA ABoVE project campaign) to generate boreal forest vegetation and fire fuel maps. Based on our field plot data, random forest classified images derived from 304 AVIRIS-NG bands at Viereck IV level (Alaska Vegetation Classification) had an 80% accuracy compared to the 33% accuracy of the LANDFIRE’s Existing Vegetation Type (EVT) product derived from Landsat 8. Not only did our product more accurately classify fire fuels but was also able to identify 20 dominant vegetation classes (percent cover >1%) while the EVT product only identified 8 dominant classes within the study area. This study demonstrated that highly detailed and accurate fire fuel maps can be created at local sites where AVIRIS-NG is available and can provide valuable decision-support information to fire managers to combat wildfires. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Forest Disturbances)

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24 pages, 4888 KiB

Open AccessArticle

Foliage Biophysical Trait Prediction from Laboratory Spectra in Norway Spruce Is More Affected by Needle Age Than by Site Soil Conditions

by Zuzana Lhotáková, Veronika Kopačková-Strnadová, Filip Oulehle, Lucie Homolová, Eva Neuwirthová, Marian Švik, Růžena Janoutová and Jana Albrechtová

Remote Sens. 2021, 13(3), 391; https://doi.org/10.3390/rs13030391 - 23 Jan 2021

Cited by 13 | Viewed by 2806

Abstract

Scaling leaf-level optical signals to the canopy level is essential for airborne and satellite-based forest monitoring. In evergreen trees, biophysical and optical traits may change as foliage ages. This study aims to evaluate the effect of age in Norway spruce needle on biophysical trait-prediction based on laboratory leaf-level spectra. Mature Norway spruce trees were sampled at forest stands in ten headwater catchments with different soil properties. Foliage biophysical traits (pigments, phenolics, lignin, cellulose, leaf mass per area, water, and nitrogen content) were assessed for three needle-age classes. Complementary samples for needle reflectance and transmittance were measured using an integrating sphere. Partial least square regression (PLSR) models were constructed for predicting needle biophysical traits from reflectance—separating needle age classes and assessing all age classes together. The ten study sites differed in soil properties rather than in needle biophysical traits. Optical properties consistently varied among age classes; however, variation related to the soil conditions was less pronounced. The predictive power of PLSR models was needle-age dependent for all studied traits. The following traits were predicted with moderate accuracy: needle pigments, phenolics, leaf mass per area and water content. PLSR models always performed better if all needle age classes were included (rather than individual age classes separately). This also applied to needle-age independent traits (water and lignin). Thus, we recommend including not only current but also older needle traits as a ground truth for evergreen conifers with long needle lifespan. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Forest Disturbances)

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31 pages, 12860 KiB

Open AccessArticle

Automatic Tree Crown Extraction from UAS Multispectral Imagery for the Detection of Bark Beetle Disturbance in Mixed Forests

by Robert Minařík, Jakub Langhammer and Theodora Lendzioch

Remote Sens. 2020, 12(24), 4081; https://doi.org/10.3390/rs12244081 - 13 Dec 2020

Cited by 32 | Viewed by 4983

Abstract

Multispectral imaging using unmanned aerial systems (UAS) enables rapid and accurate detection of pest insect infestations, which are an increasing threat to midlatitude natural forests. Pest detection at the level of an individual tree is of particular importance in mixed forests, where it enables a sensible forest management approach. In this study, we propose a method for individual tree crown delineation (ITCD) followed by feature extraction to detect a bark beetle disturbance in a mixed urban forest using a photogrammetric point cloud (PPC) and a multispectral orthomosaic. An excess green index (ExG) threshold mask was applied before the ITCD to separate targeted coniferous trees from deciduous trees and backgrounds. The individual crowns of conifer trees were automatically delineated as (i) a full tree crown using marker-controlled watershed segmentation (MCWS), Dalponte2016 (DAL), and Li 2012 (LI) region growing algorithms or (ii) a buffer (BUFFER) around a treetop from the masked PPC. We statistically compared selected spectral and elevation features extracted from automatically delineated crowns (ADCs) of each method to reference tree crowns (RTC) to distinguish between the forest disturbance classes and two tree species. Moreover, the effect of PPC density on the ITCD accuracy and feature extraction was investigated. The ExG threshold mask application resulted in the excellent separability of targeted conifer trees and the increasing shape similarity of ADCs compared to RTC. The results revealed a strong effect of PPC density on treetop detection and ITCD. If the PPC density is sufficient (>10 points/m²), the ADCs produced by DAL, MCWS, and LI methods are comparable, and the extracted feature statistics of ADCs insignificantly differ from RTC. The BUFFER method is less suitable for detecting a bark beetle disturbance in the mixed forest because of the simplicity of crown delineation. It caused significant differences in extracted feature statistics compared to RTC. Therefore, the point density was found to be more significant than the algorithm used. We conclude that automatic ITCD methods may constitute a substitute for the time-consuming manual tree crown delineation in tree-based bark beetle disturbance detection and sanitation of individual infested trees using the suggested methodology and high-density (>20 points/m², 10 points/m² minimum) PPC. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Forest Disturbances)

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20 pages, 16645 KiB

Open AccessArticle

Landsat-Based Indices Reveal Consistent Recovery of Forested Stream Catchments from Acid Deposition

by Marian Švik, Filip Oulehle, Pavel Krám, Růžena Janoutová, Kateřina Tajovská and Lucie Homolová

Remote Sens. 2020, 12(12), 1944; https://doi.org/10.3390/rs12121944 - 16 Jun 2020

Cited by 6 | Viewed by 4214

Abstract

Central European forests suffered from severe, large-scale atmospheric depositions of sulfur and nitrogen due to coal-based energy production during the 20th century. High deposition of acid compounds distorted soil chemistry and had negative effects on forest physiology and growth. Since 1994, continuous data on atmospheric deposition and stream runoff fluxes have provided evidence of ecosystem recovery from acidification. In this study, we combined for the first time mass budget data (sulfur deposition and total dissolved inorganic nitrogen (DIN) export) from the GEOMON monitoring network of headwater catchments with annual trajectories of vegetation indices derived from Landsat remote sensing observations. Time series of selected vegetation indices was constructed from Landsat 5, 7, and 8 using Google Earth Engine. Linear regression between the field data and vegetation indices was analyzed using R software. Biogeochemical responses of the forested catchment to declining acid deposition (driven by SO₂ emission reduction) were consistent across all catchments covering various forest stands from different regions of the Czech Republic. Significant correlations were found with total sulfur depositions, suggesting that the forests are continuously and consistently prospering from reductions in acid deposition. Disturbance index (DI) was the only vegetation index that was well-related to changes in forest cover associated with salvage loggings (due to the forest decline) during the 1980s and 1990s. A significant relationship (R² = 0.82) was found between the change in DI and DIN export in stream water. Regrowth of young forests in these highly affected areas tracks the most pronounced changes in total DIN export, suggesting a prominent role of vegetation in nitrogen retention. With the Landsat-derived DI, we could map decennial changes in forest disturbances beyond the small scale of the catchments to the regional level (demonstrated here for two protected landscape areas). This analysis showed the peak in forest disturbances to have occurred around the mid-1990s, followed by forest recovery and regrowth. Despite the improvement in forest ecosystem functioning over the past three decades in mountainous areas, emerging threats connected to changing climate will shape forest development in the near future. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Forest Disturbances)

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17 pages, 7358 KiB

Open AccessArticle

Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning

by Dmitry E. Kislov and Kirill A. Korznikov

Remote Sens. 2020, 12(7), 1145; https://doi.org/10.3390/rs12071145 - 3 Apr 2020

Cited by 50 | Viewed by 7828

Abstract

Wind disturbances are significant phenomena in forest spatial structure and succession dynamics. They cause changes in biodiversity, impact on forest ecosystems at different spatial scales, and have a strong influence on economics and human beings. The reliable recognition and mapping of windthrow areas are of high importance from the perspective of forest management and nature conservation. Recent research in artificial intelligence and computer vision has demonstrated the incredible potential of neural networks in addressing image classification problems. The most efficient algorithms are based on artificial neural networks of nested and complex architecture (e.g., convolutional neural networks (CNNs)), which are usually referred to by a common term—deep learning. Deep learning provides powerful algorithms for the precise segmentation of remote sensing data. We developed an algorithm based on a U-Net-like CNN, which was trained to recognize windthrow areas in Kunashir Island, Russia. We used satellite imagery of very-high spatial resolution (0.5 m/pixel) as source data. We performed a grid search among 216 parameter combinations defining different U-Net-like architectures. The best parameter combination allowed us to achieve an overall accuracy for recognition of windthrow sites of up to 94% for forested landscapes by coniferous and mixed coniferous forests. We found that the false-positive decisions of our algorithm correspond to either seashore logs, which may look similar to fallen tree trunks, or leafless forest stands. While the former can be rectified by applying a forest mask, the latter requires the usage of additional information, which is not always provided by satellite imagery. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Forest Disturbances)

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19 pages, 4422 KiB

Open AccessArticle

Landscape Representation by a Permanent Forest Plot and Alternative Plot Designs in a Typhoon Hotspot, Fushan, Taiwan

by Jonathan Peereman, James Aaron Hogan and Teng-Chiu Lin

Remote Sens. 2020, 12(4), 660; https://doi.org/10.3390/rs12040660 - 17 Feb 2020

Cited by 5 | Viewed by 4605

Abstract

Permanent forest dynamics plots have provided valuable insights into many aspects of forest ecology. The evaluation of their representativeness within the landscape is necessary to understanding the limitations of findings from permanent plots at larger spatial scales. Studies on the representativeness of forest plots with respect to landscape heterogeneity and disturbance effect have already been carried out, but knowledge of how multiple disturbances affect plot representativeness is lacking—particularly in sites where several disturbances can occur between forest plot censuses. This study explores the effects of five typhoon disturbances on the Fushan Forest Dynamics Plot (FFDP) and its surrounding landscape, the Fushan Experimental Forest (FEF), in Taiwan where typhoons occur annually. The representativeness of the FFDP for the FEF was studied using four topographical variables derived from a digital elevation model and two vegetation indices (VIs), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII), calculated from Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI data. Representativeness of four alternative plot designs were tested by dividing the FFDP into subplots over wider elevational ranges. Results showed that the FFDP neither represents landscape elevational range (<10%) nor vegetation cover (<7% of the interquartile range, IQR). Although disturbance effects (i.e., ΔVIs) were also different between the FFDP and the FEF, comparisons showed no under- or over-exposure to typhoon damage frequency or intensity within the FFDP. In addition, the ΔVIs were of the same magnitudes in the plots and the reserve, and the plot covered 30% to 75.9% of IQRs of the reserve ΔVIs. Unexpectedly, the alternative plot designs did not lead to increased representation of damage for 3 out of the 4 tested typhoons and they did not suggest higher representativeness of rectangular vs. square plots. Based on the comparison of mean Euclidian distances, two rectangular plots had smaller distances than four square or four rectangular plots of the same area. Therefore, this study suggests that the current FFDP provides a better representation of its landscape disturbances than alternatives, which contained wider topographical variation and would be more difficult to conduct ground surveys. However, upscaling needs to be done with caution as, in the case of the FEF, plot representativeness varied among typhoons. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Forest Disturbances)

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11 pages, 3206 KiB

Open AccessLetter

How Initial Forest Cover, Site Characteristics and Fire Severity Drive the Dynamics of the Southern Boreal Forest

by Victor Danneyrolles, Osvaldo Valeria, Ibrahim Djerboua, Sylvie Gauthier and Yves Bergeron

Remote Sens. 2020, 12(23), 3957; https://doi.org/10.3390/rs12233957 - 3 Dec 2020

Cited by 4 | Viewed by 3217

Abstract

Forest fires are a key driver of boreal landscape dynamics and are expected to increase with climate change in the coming decades. A profound understanding of the effects of fire upon boreal forest dynamics is thus critically needed for our ability to manage these ecosystems and conserve their services. In the present study, we investigate the long-term post-fire forest dynamics in the southern boreal forests of western Quebec using historical aerial photographs from the 1930s, alongside with modern aerial photographs from the 1990s. We quantify the changes in forest cover classes (i.e., conifers, mixed and broadleaved) for 16 study sites that were burned between 1940 and 1970. We then analyzed how interactions between pre-fire forest composition, site characteristics and a fire severity weather index (FSWI) affected the probability of changes in forest cover. In the 1930s, half of the cover of sampled sites were coniferous while the other half were broadleaved or mixed. Between the 1930s and the 1990s, 41% of the areas maintained their initial cover while 59% changed. The lowest probability of changes was found with initial coniferous cover and well drained till deposits. Moreover, an important proportion of 1930s broadleaved/mixed cover transitioned to conifers in the 1990s, which was mainly associated with high FSWI and well-drained deposits. Overall, our results highlight a relatively high resistance and resilience of southern boreal coniferous forests to fire, which suggest that future increase in fire frequency may not necessarily result in a drastic loss of conifers. Full article

(This article belongs to the Special Issue Remote Sensing of Natural Forest Disturbances)

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