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Feature Paper Special Issue on Ecological Remote Sensing

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

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 47036

Special Issue Editors

Dr. Valerio Amici

E-Mail Website
Guest Editor
GREENARCO SRL, Spin-off of the University of Bologna, 40127 Bologna, Italy
Interests: spatial ecology; landscape ecology; land-use change modeling; ecological complexity
Dr. Margarita Mulero-Pazmany

E-Mail Website
Guest Editor
Department of Animal Biology, University of Malaga, 29010 Malaga, Spain
Interests: drones; biological conservation; wildlife; ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Malavasi

E-Mail Website
Guest Editor
Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcka 129, 165 00 Praha Suchdol, Czech Republic
Interests: spatial ecology; coastal ecology; biodiversity estimate; land use/land cover change; conservation biology
Dr. Vitezslav Moudry

E-Mail Website
Guest Editor
Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcka 129, 165 00 Praha Suchdol, Czech Republic
Interests: GIS; LiDAR; species-environment modelling; restoration ecology
Dr. Gaia Vaglio Laurin

E-Mail Website
Guest Editor
Department of Innovation in Biological, Agri-Food and Forestry Systems, University of Tuscia, 01100 Viterbo, Italy
Interests: forest; biodiversity and vegetation monitoring using multiple remote sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has become a fundamental tool for investigating Earth ecological patterns and processes at different spatial and temporal scales.

Ecological theory has been applied to remote sensing data to monitor species dispersal and diversity over space and time. Ecosystem-based models have also been developed to monitor, at a high temporal resolution, Earth surface changes over large areas. The need for high temporal resolution to study global and local changes is directly related to the use of techniques other than field-based monitoring. Consequently, remote sensing is critical for ecosystems monitoring.

Remote sensing and ecosystems monitoring challenges include (i) scale issues, (ii) data gathering and analysis, and (iii) software development.

The aim of this Special Issue, under the Ecological Remote Sensing Section of Remote Sensing, is to provide robust papers on new ideas involving the use of remote sensing for ecological studies.

Manuscripts for this important Special Issue of Remote Sensing will be accepted by the Editorial Office, the Guest Editors, and editorial board members by invitation only.

Procedure

  • All submissions will be rigorously reviewed according to the Remote Sensing journal guidelines.
  • Manuscripts that are not suitable for this Special Issue will be notified immediately after consultation with the editorial board members. Authors of these manuscripts are invited to consider submitting to the Ecological Remote Sensing Section or any other section of Remote Sensing as a regular paper. Other manuscripts will be forwarded for review.
  • Manuscripts that are not selected as feature papers will be notified after the first round of reviews. The selection will be based on the review. Authors of those manuscripts that are not selected for the Special Issue may decide to revise and submit as a regular paper in the Ecological Remote Sensing Section of the Remote Sensing Please note that authors of these manuscripts need to shoulder the publication fees.
  • Other manuscripts will be sent for a second round of reviews. However, this does not necessarily mean that a manuscript under the second round of reviews will be published as a feature paper. We will still seek comments and suggestions from reviewers.

Please contact Traey Wu ([email protected]), the section managing editor, if you have any questions.

Dr. Valerio Amici
Dr. Margarita Mulero-Pazmany
Dr. Marco Malavasi
Dr. Vitezslav Moudry
Dr. Gaia Vaglio Laurin
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

  • Biodiversity estimate
  • Computational ecology
  • Ecological informatics
  • Ecological modeling
  • Entropy theory
  • Fuzzy set theory applied to remote sensing data classification
  • Global change
  • Software development
  • Spatial ecology
  • Species distribution modeling

Published Papers (15 papers)

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18 pages, 6491 KiB  
Article
A Deep Learning Time Series Approach for Leaf and Wood Classification from Terrestrial LiDAR Point Clouds
by Tao Han and Gerardo Arturo Sánchez-Azofeifa
Remote Sens. 2022, 14(13), 3157; https://doi.org/10.3390/rs14133157 - 1 Jul 2022
Cited by 9 | Viewed by 2700
Abstract
The accurate separation between leaf and woody components from terrestrial laser scanning (TLS) data is vital for the estimation of leaf area index (LAI) and wood area index (WAI). Here, we present the application of deep learning time series separation of leaves and [...] Read more.
The accurate separation between leaf and woody components from terrestrial laser scanning (TLS) data is vital for the estimation of leaf area index (LAI) and wood area index (WAI). Here, we present the application of deep learning time series separation of leaves and wood from TLS point clouds collected from broad-leaved trees. First, we use a multiple radius nearest neighbor approach to obtain a time series of the geometric features. Second, we compare the performance of Fully Convolutional Neural Network (FCN), Long Short-Term Memory Fully Convolutional Neural Network (LSTM-FCN), and Residual Network (ResNet) on leaf and wood classification. We also compare the effect of univariable (UTS) and multivariable (MTS) time series on classification accuracy. Finally, we explore the utilization of a class activation map (CAM) to reduce the black-box effect of deep learning. The average overall accuracy of the MTS method across the training data is 0.96, which is higher than the UTS methods (0.67 to 0.88). Meanwhile, ResNet spent much more time than FCN and LSTM-FCN in model development. When testing our method on an independent dataset, the MTS models based on FCN, LSTM-FCN, and ResNet all demonstrate similar performance. Our method indicates that the CAM can explain the black-box effect of deep learning and suggests that deep learning algorithms coupled with geometric feature time series can accurately separate leaf and woody components from point clouds. This provides a good starting point for future research into estimation of forest structure parameters. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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18 pages, 6198 KiB  
Article
Developing an Enhanced Ecological Evaluation Index (EEEI) Based on Remotely Sensed Data and Assessing Spatiotemporal Ecological Quality in Guangdong–Hong Kong–Macau Greater Bay Area, China
by Shanshan Feng and Fenglei Fan
Remote Sens. 2022, 14(12), 2852; https://doi.org/10.3390/rs14122852 - 14 Jun 2022
Cited by 12 | Viewed by 2986
Abstract
Ecological changes affected by increasing human activities have highlighted the importance of ecological quality assessments. An appropriate and efficient selection of ecological parameters is fundamental for ecological quality assessments. On the basis of remote sensing data and methods, this study developed an enhanced [...] Read more.
Ecological changes affected by increasing human activities have highlighted the importance of ecological quality assessments. An appropriate and efficient selection of ecological parameters is fundamental for ecological quality assessments. On the basis of remote sensing data and methods, this study developed an enhanced ecological evaluation index (EEEI) with five integrated ecological parameters by containing pixel and sub-pixel information: normalized difference vegetation index, impervious surface coverage, soil coverage, land surface temperature, and wetness component of tasseled cap transformation. Significantly, the EEEI simultaneously considered the five aspects of land surface ecological conditions (i.e., greenness, human activities, dryness, heat, and moisture), which provided an effective guide for the systematic selection of ecological parameters. The EEEI has a clear theoretical framework, and all the parameters can be obtained quickly on the basis of the remote sensing datasets and methods, which is suitable for the promotion and application of ecological quality assessments to various areas and scales. Furthermore, the EEEI was applied to assess and detect the ecological quality of the Guangdong–Hong Kong–Macau Greater Bay Area (GBA) of China. Assessment results indicated that the ecological quality of the GBA is currently facing great challenges with a degradation trend from 2000 to 2020, which emphasizes the significance and urgency for eco-environmental protection of the GBA. This provided evidence that the EEEI can be used as an effective index for scientific, objective, quantitative, and comprehensive ecological quality assessment, which can also aid regional environmental management and ecological protection. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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13 pages, 2690 KiB  
Communication
Delineation of Geomorphological Woodland Key Habitats Using Airborne Laser Scanning
by Hans Ole Ørka, Marie-Claude Jutras-Perreault, Jaime Candelas-Bielza and Terje Gobakken
Remote Sens. 2022, 14(5), 1184; https://doi.org/10.3390/rs14051184 - 27 Feb 2022
Cited by 4 | Viewed by 1949
Abstract
Forest ecosystems provide a range of services and function as habitats for many species. The concept of woodland key habitats (WKH) is important for biodiversity management in forest planning standards and certification schemes. The main idea of the WKH is to preserve biodiversity [...] Read more.
Forest ecosystems provide a range of services and function as habitats for many species. The concept of woodland key habitats (WKH) is important for biodiversity management in forest planning standards and certification schemes. The main idea of the WKH is to preserve biodiversity hotspots in the forest landscape. Current methods used in delineating WKH rely on costly field inventories. Furthermore, it is well known that the surveyor introduces an error because of the subjective assessment. Remote sensing may reduce this error in a cost-efficient way. The current study develops automated methods using airborne laser scanning (ALS) data to delineate geomorphological WKH, i.e., rock walls and stream gorges. The methods were evaluated based on a complete field inventory of WKH in a 1600 ha area in south-eastern Norway. The delineated WKH showed high detection rates, minor omission errors, but high commissions errors. Combining the delineation into a map of potential WKH suitable to guide field surveyors resulted in detecting all field reference WKH, i.e., a detection rate of 100% and a commission error of 25%. It is concluded that a higher degree of automatization might be possible to improve results and increase the efficiency of WKH inventories. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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21 pages, 8882 KiB  
Article
Multi-Species Inference of Exotic Annual and Native Perennial Grasses in Rangelands of the Western United States Using Harmonized Landsat and Sentinel-2 Data
by Devendra Dahal, Neal J. Pastick, Stephen P. Boyte, Sujan Parajuli, Michael J. Oimoen and Logan J. Megard
Remote Sens. 2022, 14(4), 807; https://doi.org/10.3390/rs14040807 - 9 Feb 2022
Cited by 11 | Viewed by 3563
Abstract
The invasion of exotic annual grass (EAG), e.g., cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), into rangeland ecosystems of the western United States is a broad-scale problem that affects wildlife habitats, increases wildfire frequency, and adds to land management [...] Read more.
The invasion of exotic annual grass (EAG), e.g., cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), into rangeland ecosystems of the western United States is a broad-scale problem that affects wildlife habitats, increases wildfire frequency, and adds to land management costs. However, identifying individual species of EAG abundance from remote sensing, particularly at early stages of invasion or growth, can be problematic because of overlapping controls and similar phenological characteristics among native and other exotic vegetation. Subsequently, refining and developing tools capable of quantifying the abundance and phenology of annual and perennial grass species would be beneficial to help inform conservation and management efforts at local to regional scales. Here, we deploy an enhanced version of the U.S. Geological Survey Rangeland Exotic Plant Monitoring System to develop timely and accurate maps of annual (2016–2020) and intra-annual (May 2021 and July 2021) abundances of exotic annual and perennial grass species throughout the rangelands of the western United States. This monitoring system leverages field observations and remote-sensing data with artificial intelligence/machine learning to rapidly produce annual and early season estimates of species abundances at a 30-m spatial resolution. We introduce a fully automated and multi-task deep-learning framework to simultaneously predict and generate weekly, near-seamless composites of Harmonized Landsat Sentinel-2 spectral data. These data, along with auxiliary datasets and time series metrics, are incorporated into an ensemble of independent XGBoost models. This study demonstrates that inclusion of the Normalized Difference Vegetation Index and Normalized Difference Wetness Index time-series data generated from our deep-learning framework enables near real-time and accurate mapping of EAG (Median Absolute Error (MdAE): 3.22, 2.72, and 0.02; and correlation coefficient (r): 0.82, 0.81, and 0.73; respectively for EAG, cheatgrass, and medusahead) and native perennial grass abundance (MdAE: 2.51, r:0.72 for Sandberg bluegrass (Poa secunda)). Our approach and the resulting data provide insights into rangeland grass dynamics, which will be useful for applications, such as fire and drought monitoring, habitat suitability mapping, as well as land-cover and land-change modelling. Spatially explicit, timely, and accurate species-specific abundance datasets provide invaluable information to land managers. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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19 pages, 3616 KiB  
Article
Estimated Biomass Loss Caused by the Vaia Windthrow in Northern Italy: Evaluation of Active and Passive Remote Sensing Options
by Gaia Vaglio Laurin, Nicola Puletti, Clara Tattoni, Carlotta Ferrara and Francesco Pirotti
Remote Sens. 2021, 13(23), 4924; https://doi.org/10.3390/rs13234924 - 3 Dec 2021
Cited by 12 | Viewed by 6365
Abstract
Windstorms are a major disturbance factor for European forests. The 2018 Vaia storm, felled large volumes of timber in Italy causing serious ecological and financial losses. Remote sensing is fundamental for primary assessment of damages and post-emergency phase. An explicit estimation of the [...] Read more.
Windstorms are a major disturbance factor for European forests. The 2018 Vaia storm, felled large volumes of timber in Italy causing serious ecological and financial losses. Remote sensing is fundamental for primary assessment of damages and post-emergency phase. An explicit estimation of the timber loss caused by Vaia using satellite remote sensing was not yet undertaken. In this investigation, three different estimates of timber loss were compared in two study sites in the Alpine area: pre-existing local growing stock volume maps based on lidar data, a recent national-level forest volume map, and an novel estimation of AGB values based on active and passive remote sensing. The compared datasets resemble the type of information that a forest manager might potentially find or produce. The results show a significant disagreement in the different biomass estimates, related to the methods used to produce them, the study areas characteristics, and the size of the damaged areas. These sources of uncertainty highlight the difficulty of estimating timber loss, unless a unified national or regional European strategy to improve preparedness to forest hazards is defined. Considering the frequent impacts on forest resources that occurred in the last years in the European Union, remote sensing-based surveys targeting forests is urgent, particularly for the many European countries that still lack reliable forest stocks data. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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20 pages, 9467 KiB  
Article
Testing the Height Variation Hypothesis with the R rasterdiv Package for Tree Species Diversity Estimation
by Daniel Tamburlin, Michele Torresani, Enrico Tomelleri, Giustino Tonon and Duccio Rocchini
Remote Sens. 2021, 13(18), 3569; https://doi.org/10.3390/rs13183569 - 8 Sep 2021
Cited by 11 | Viewed by 2627
Abstract
Forest biodiversity is a key element to support ecosystem functions. Measuring biodiversity is a necessary step to identify critical issues and to choose interventions to be applied in order to protect it. Remote sensing provides consistent quality and standardized data, which can be [...] Read more.
Forest biodiversity is a key element to support ecosystem functions. Measuring biodiversity is a necessary step to identify critical issues and to choose interventions to be applied in order to protect it. Remote sensing provides consistent quality and standardized data, which can be used to estimate different aspects of biodiversity. The Height Variation Hypothesis (HVH) represents an indirect method for estimating species diversity in forest ecosystems from the LiDAR data, and it assumes that the higher the variation in tree height (height heterogeneity, HH), calculated through the ’Canopy Height Model’ (CHM) metric, the more complex the overall structure of the forest and the higher the tree species diversity. To date, the HVH has been tested exclusively with CHM data, assessing the HH only with a single heterogeneity index (the Rao’s Q index) without making use of any moving windows (MW) approach. In this study, the HVH has been tested in an alpine coniferous forest situated in the municipality of San Genesio/Jenesien (eastern Italian Alps) at 1100 m, characterized by the presence of 11 different tree species (mainly Pinus sylvestris, Larix decidua, Picea abies followed by Betula alba and Corylus avellana). The HH has been estimated through different heterogeneity measures described in the new R rasterdiv package using, besides the CHM, also other LiDAR metrics (as the percentile or the standard deviation of the height distribution) at various spatial resolutions and MWs (ALS LiDAR data with mean point cloud density of 2.9 point/m2). For each combination of parameters, and for all the considered plots, linear regressions between the Shannon’s H′ (used as tree species diversity index based on field data) and the HH have been derived. The results showed that the Rao’s Q index (singularly and through a multidimensional approach) performed generally better than the other heterogeneity indices in the assessment of the HH. The CHM and the LiDAR metrics related to the upper quantile point cloud distribution at fine resolution (2.5 m, 5 m) have shown the most important results for the assessment of the HH. The size of the used MW did not influence the general outcomes but instead, it increased when compared to the results found in the literature, where the HVH was tested without MW approach. The outcomes of this study underline that the HVH, calculated with certain heterogeneity indices and LiDAR data, can be considered a useful tool for assessing tree species diversity in considered forest ecosystems. The general results highlight the strength and importance of LiDAR data in assessing the height heterogeneity and the related biodiversity in forest ecosystems. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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15 pages, 3357 KiB  
Article
Detection of Spatial and Temporal Patterns of Liana Infestation Using Satellite-Derived Imagery
by Chris J. Chandler, Geertje M. F. van der Heijden, Doreen S. Boyd and Giles M. Foody
Remote Sens. 2021, 13(14), 2774; https://doi.org/10.3390/rs13142774 - 14 Jul 2021
Cited by 2 | Viewed by 2624
Abstract
Lianas (woody vines) play a key role in tropical forest dynamics because of their strong influence on tree growth, mortality and regeneration. Assessing liana infestation over large areas is critical to understand the factors that drive their spatial distribution and to monitor change [...] Read more.
Lianas (woody vines) play a key role in tropical forest dynamics because of their strong influence on tree growth, mortality and regeneration. Assessing liana infestation over large areas is critical to understand the factors that drive their spatial distribution and to monitor change over time. However, it currently remains unclear whether satellite-based imagery can be used to detect liana infestation across closed-canopy forests and therefore if satellite-observed changes in liana infestation can be detected over time and in response to climatic conditions. Here, we aim to determine the efficacy of satellite-based remote sensing for the detection of spatial and temporal patterns of liana infestation across a primary and selectively logged aseasonal forest in Sabah, Borneo. We used predicted liana infestation derived from airborne hyperspectral data to train a neural network classification for prediction across four Sentinel-2 satellite-based images from 2016 to 2019. Our results showed that liana infestation was positively related to an increase in Greenness Index (GI), a simple metric relating to the amount of photosynthetically active green leaves. Furthermore, this relationship was observed in different forest types and during (2016), as well as after (2017–2019), an El Niño-induced drought. Using a neural network classification, we assessed liana infestation over time and showed an increase in the percentage of severely (>75%) liana infested pixels from 12.9% ± 0.63 (95% CI) in 2016 to 17.3% ± 2 in 2019. This implies that reports of increasing liana abundance may be more wide-spread than currently assumed. This is the first study to show that liana infestation can be accurately detected across closed-canopy tropical forests using satellite-based imagery. Furthermore, the detection of liana infestation during both dry and wet years and across forest types suggests this method should be broadly applicable across tropical forests. This work therefore advances our ability to explore the drivers responsible for patterns of liana infestation at multiple spatial and temporal scales and to quantify liana-induced impacts on carbon dynamics in tropical forests globally. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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15 pages, 2123 KiB  
Article
Water Mixing Conditions Influence Sentinel-2 Monitoring of Chlorophyll Content in Monomictic Lakes
by Michela Perrone, Massimiliano Scalici, Luisa Conti, David Moravec, Jan Kropáček, Maria Sighicelli, Francesca Lecce and Marco Malavasi
Remote Sens. 2021, 13(14), 2699; https://doi.org/10.3390/rs13142699 - 8 Jul 2021
Cited by 6 | Viewed by 2972
Abstract
Prompt estimation of phytoplankton biomass is critical in determining the ecological quality of freshwaters. Remote Sensing (RS) may provide new opportunities to integrate with situ traditional monitoring techniques. Nonetheless, wide regional and temporal variability in freshwater optical constituents makes it difficult to design [...] Read more.
Prompt estimation of phytoplankton biomass is critical in determining the ecological quality of freshwaters. Remote Sensing (RS) may provide new opportunities to integrate with situ traditional monitoring techniques. Nonetheless, wide regional and temporal variability in freshwater optical constituents makes it difficult to design universally applicable RS protocols. Here, we assessed the potential of two neural networks-based models, namely the Case 2 Regional CoastColour (C2RCC) processor and the Mixture Density Network (MDN), applied to MSI Sentinel-2 data for monitoring Chlorophyll (Chl) content in three monomictic volcanic lakes while accounting for the effect of their specific water circulation pattern on the remotely-sensed and in situ data relation. Linear mixed models were used to test the relationship between the remote sensing indices calculated through C2RCC (INN) and MDN (IMDN), and in situ Chl concentration. Both indices proved to explain a large portion of the variability in the field data and exhibited a positive and significant relationship between Chl concentration and satellite data, but only during the mixing phase. The significant effect of the water circulation period can be explained by the low responsiveness of the RS approaches applied here to the low phytoplankton biomass, typical of the stratification phase. Sentinel-2 data proved their valuable potential for the remote sensing of phytoplankton in small inland water bodies, otherwise challenging with previous sensors. However, caution should be taken, since the applicability of such an approach on certain water bodies may depend on hydrological and ecological parameters (e.g., thermal stratification and seasonal nutrient availability) potentially altering RS chlorophyll detection by neural networks-based models, despite their alleged global validity. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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20 pages, 4580 KiB  
Article
A Remote Sensing Approach to Understanding Patterns of Secondary Succession in Tropical Forest
by Eric Chraibi, Haley Arnold, Sandra Luque, Amy Deacon, Anne E. Magurran and Jean-Baptiste Féret
Remote Sens. 2021, 13(11), 2148; https://doi.org/10.3390/rs13112148 - 30 May 2021
Cited by 9 | Viewed by 3821
Abstract
Monitoring biodiversity on a global scale is a major challenge for biodiversity conservation. Field assessments commonly used to assess patterns of biodiversity and habitat condition are costly, challenging, and restricted to small spatial scales. As ecosystems face increasing anthropogenic pressures, it is important [...] Read more.
Monitoring biodiversity on a global scale is a major challenge for biodiversity conservation. Field assessments commonly used to assess patterns of biodiversity and habitat condition are costly, challenging, and restricted to small spatial scales. As ecosystems face increasing anthropogenic pressures, it is important that we find ways to assess patterns of biodiversity more efficiently. Remote sensing has the potential to support understanding of landscape-level ecological processes. In this study, we considered cacao agroforests at different stages of secondary succession, and primary forest in the Northern Range of Trinidad, West Indies. We assessed changes in tree biodiversity over succession using both field data, and data derived from remote sensing. We then evaluated the strengths and limitations of each method, exploring the potential for expanding field data by using remote sensing techniques to investigate landscape-level patterns of forest condition and regeneration. Remote sensing and field data provided different insights into tree species compositional changes, and patterns of alpha- and beta-diversity. The results highlight the potential of remote sensing for detecting patterns of compositional change in forests, and for expanding on field data in order to better understand landscape-level patterns of forest diversity. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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19 pages, 3462 KiB  
Article
Use of Sentinel-2 Satellite Data for Windthrows Monitoring and Delimiting: The Case of “Vaia” Storm in Friuli Venezia Giulia Region (North-Eastern Italy)
by Valentina Olmo, Enrico Tordoni, Francesco Petruzzellis, Giovanni Bacaro and Alfredo Altobelli
Remote Sens. 2021, 13(8), 1530; https://doi.org/10.3390/rs13081530 - 15 Apr 2021
Cited by 11 | Viewed by 3822
Abstract
On the 29th of October 2018, a storm named “Vaia” hit North-Eastern Italy, causing the loss of 8 million m3 of standing trees and creating serious damage to the forested areas, with many economic and ecological implications. This event brought up the [...] Read more.
On the 29th of October 2018, a storm named “Vaia” hit North-Eastern Italy, causing the loss of 8 million m3 of standing trees and creating serious damage to the forested areas, with many economic and ecological implications. This event brought up the necessity of a standard procedure for windthrow detection and monitoring based on satellite data as an alternative to foresters’ fieldwork. The proposed methodology was applied in Carnic Alps (Friuli Venezia Giulia, NE Italy) in natural stands dominated by Picea abies and Abies alba. We used images from the Sentinel-2 mission: 1) to test vegetation indices performance in monitoring the vegetation dynamics in the short period after the storm, and 2) to create a windthrow map for the whole Friuli Venezia Giulia region. Results showed that windthrows in forests have a significant influence on visible and short-wave infrared (SWIR) spectral bands of Sentinel-2, both in the short and the long-term timeframes. NDWI8A and NDWI were the best indices for windthrow detection (R2 = 0.80 and 0.77, respectively) and NDVI, PSRI, SAVI and GNDVI had an overall good performance in spotting wind-damaged areas (R2 = 0.60–0.76). Moreover, these indices allowed to monitor post-Vaia forest die-off and showed a dynamic recovery process in cleaned sites. The NDWI8A index, employed in the vegetation index differencing (VID) change detection technique, delimited damaged areas comparable to the estimations provided by Regional Forest System (2545 ha and 3183 ha, respectively). Damaged forests detected by NDWI8A VID ranged from 500 m to 1500 m a.s.l., mainly covering steep slopes in the south and east aspects (42% and 25%, respectively). Our results suggested that the NDWI8A VID method may be a cost-effective and accurate way to produce windthrow maps, which could limit the risks associated with fieldwork and may provide a valuable tool to plan tree removal interventions in a more efficient way. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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20 pages, 2169 KiB  
Article
Influence of Soil Moisture vs. Climatic Factors in Pinus Halepensis Growth Variability in Spain: A Study with Remote Sensing and Modeled Data
by Ángel González-Zamora, Laura Almendra-Martín, Martín de Luis and José Martínez-Fernández
Remote Sens. 2021, 13(4), 757; https://doi.org/10.3390/rs13040757 - 18 Feb 2021
Cited by 10 | Viewed by 2074
Abstract
The influence of soil water content on Aleppo pine growth variability is analyzed against climatic variables, using satellite and modeled soil moisture databases. The study was made with a dendrochronological series of 22 forest sites in Spain with different environmental conditions. From the [...] Read more.
The influence of soil water content on Aleppo pine growth variability is analyzed against climatic variables, using satellite and modeled soil moisture databases. The study was made with a dendrochronological series of 22 forest sites in Spain with different environmental conditions. From the results of the correlation analysis, at both daily and monthly scales, it was observed that soil moisture was the variable that correlated the most with tree growth and the one that better identified the critical periods for this growth. The maximum correlation coefficients obtained with the rest of the variables were less than half of that obtained for soil moisture. Multiple linear regression analysis with all combinations of variables indicated that soil moisture was the most important variable, showing the lowest p-values in all cases. While identifying the role of soil moisture, it was noted that there was appreciable variability between the sites, and that this variability is mainly modulated by water availability, rather than thermal conditions. These results can contribute to new insights into the ecohydrological dynamics of Aleppo pine and a methodological approach to the study of many other species. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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15 pages, 6345 KiB  
Article
The Regenerative Potential of Managed Calluna Heathlands—Revealing Optical and Structural Traits for Predicting Recovery Dynamics
by Carsten Neumann, Anne Schindhelm, Jörg Müller, Gabriele Weiss, Anna Liu and Sibylle Itzerott
Remote Sens. 2021, 13(4), 625; https://doi.org/10.3390/rs13040625 - 9 Feb 2021
Cited by 2 | Viewed by 2573
Abstract
The potential of vegetation recovery through resprouting of plant tissue from buds after the removal of aboveground biomass is a key resilience strategy for populations under abrupt environmental change. Resprouting leads to fast regeneration, particularly after the implementation of mechanical mowing as part [...] Read more.
The potential of vegetation recovery through resprouting of plant tissue from buds after the removal of aboveground biomass is a key resilience strategy for populations under abrupt environmental change. Resprouting leads to fast regeneration, particularly after the implementation of mechanical mowing as part of active management for promoting open habitats. We investigated whether recovery dynamics of resprouting and the threat of habitat conversion can be predicted by optical and structural stand traits derived from drone imagery in a protected heathland area. We conducted multivariate regression for variable selection and random forest regression for predictive modeling using 50 spectral predictors, textural features and height parameters to quantify Calluna resprouting and grass invasion in before-mowing images that were related to vegetation recovery in after-mowing imagery. The study reveals that Calluna resprouting can be explained by significant optical predictors of mainly green reflectance in parental individuals. In contrast, grass encroachment is identified by structural canopy properties that indicate before-mowing grass interpenetration as starting points for after-mowing dispersal. We prove the concept of trait propagation through time providing significant derivates for a low-cost drone system. It can be utilized to build drone-based decision support systems for evaluating consequences and requirements of habitat management practice. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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Review

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34 pages, 5604 KiB  
Review
Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis
by Marcin Siłuch, Piotr Bartmiński and Wojciech Zgłobicki
Remote Sens. 2022, 14(6), 1331; https://doi.org/10.3390/rs14061331 - 9 Mar 2022
Cited by 5 | Viewed by 3345
Abstract
Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in [...] Read more.
Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively). Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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Other

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12 pages, 725 KiB  
Technical Note
An Ornithologist’s Guide for Including Machine Learning in a Workflow to Identify a Secretive Focal Species from Recorded Audio
by Ming Liu, Qiyu Sun, Dustin E. Brewer, Thomas M. Gehring and Jesse Eickholt
Remote Sens. 2022, 14(15), 3816; https://doi.org/10.3390/rs14153816 - 8 Aug 2022
Cited by 2 | Viewed by 1726
Abstract
Reliable and efficient avian monitoring tools are required to identify population change and then guide conservation initiatives. Autonomous recording units (ARUs) could increase both the amount and quality of monitoring data, though manual analysis of recordings is time consuming. Machine learning could help [...] Read more.
Reliable and efficient avian monitoring tools are required to identify population change and then guide conservation initiatives. Autonomous recording units (ARUs) could increase both the amount and quality of monitoring data, though manual analysis of recordings is time consuming. Machine learning could help to analyze these audio data and identify focal species, though few ornithologists know how to cater this tool for their own projects. We present a workflow that exemplifies how machine learning can reduce the amount of expert review time required for analyzing audio recordings to detect a secretive focal species (Sora; Porzana carolina). The deep convolutional neural network that we trained achieved a precision of 97% and reduced the amount of audio for expert review by ~66% while still retaining 60% of Sora calls. Our study could be particularly useful, as an example, for those who wish to utilize machine learning to analyze audio recordings of a focal species that has not often been recorded. Such applications could help to facilitate the effective conservation of avian populations. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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7 pages, 718 KiB  
Technical Note
Integrating Hierarchical Statistical Models and Machine-Learning Algorithms for Ground-Truthing Drone Images of the Vegetation: Taxonomy, Abundance and Population Ecological Models
by Christian Damgaard
Remote Sens. 2021, 13(6), 1161; https://doi.org/10.3390/rs13061161 - 18 Mar 2021
Cited by 3 | Viewed by 1828
Abstract
In order to fit population ecological models, e.g., plant competition models, to new drone-aided image data, we need to develop statistical models that may take the new type of measurement uncertainty when applying machine-learning algorithms into account and quantify its importance for statistical [...] Read more.
In order to fit population ecological models, e.g., plant competition models, to new drone-aided image data, we need to develop statistical models that may take the new type of measurement uncertainty when applying machine-learning algorithms into account and quantify its importance for statistical inferences and ecological predictions. Here, it is proposed to quantify the uncertainty and bias of image predicted plant taxonomy and abundance in a hierarchical statistical model that is linked to ground-truth data obtained by the pin-point method. It is critical that the error rate in the species identification process is minimized when the image data are fitted to the population ecological models, and several avenues for reaching this objective are discussed. The outlined method to statistically model known sources of uncertainty when applying machine-learning algorithms may be relevant for other applied scientific disciplines. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ecological Remote Sensing)
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