remotesensing-logo

Journal Browser

Journal Browser

Applications of Remote Sensing in Forest Management and Biodiversity Conservation

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

Deadline for manuscript submissions: closed (20 May 2023) | Viewed by 23712

Special Issue Editors


E-Mail Website
Guest Editor
Institute of Geodesy and Cartography, 27 Modzelewskiego St., 02-679 Warsaw, Poland
Interests: remote sensing; land surface temperature; Cal/Val; biodiversity; vegetation mapping and monitoring; wetlands dynamics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Chemistry and Geosciences, Vilnius University, LT-03101 Vilnius, Lithuania
Interests: land use change; wetlands geography; climate change; spatial analysis; ecology; reptiles; lakes
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Geomatics, Forest Research Institute, Braci Leśnej 3 Street, Sękocin Stary, 05-090 Raszyn, Poland
Interests: remote sensing; laser scanning; precision forestry; forest management; forest health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the years, remote sensing techniques have increasingly contributed to determining biodiversity characteristics, as well as monitoring large-scale areas. Currently, biodiversity needs to be protected primarily to maintain the mechanisms of the functioning of living nature in forests and ecosystems; maintain the ability to withstand environmental changes, as well as discover and take advantage of new features that may facilitate development and guarantee the survival of future generations. The evolution of remote sensing tools allows the refinement of existing approaches and the development of innovative new ones for a better evaluation of the biodiversity response to natural ecosystems management and conservation.

With the launch of new Earth observation satellites, and the growing use of unmanned aerial vehicles, wider applications of remote sensing for monitoring and mapping of forest ecosystems biodiversity can be foreseen. Remote sensing-based approaches to biodiversity can further improve management and policy decisions. Moreover, rapid advances in remote sensing methods have also promoted the application of machine learning algorithms and techniques to problems in many related fields, such as classification and environmental changes. This Special Issue aims to report the latest advances and trends concerning multimodal remote sensing image processing methods and applications for the biodiversity.

Dr. Maciej Bartold
Dr. Rasa Šimanauskienė
Dr. Krzysztof Stereńczak
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

  • applications in remote sensing
  • machine learning / deep learning
  • multispectral / hyperspectral image processing
  • LiDAR
  • SAR
  • forest management

Related Special Issue

Published Papers (9 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

18 pages, 10118 KiB  
Article
A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands
by Maciej Bartold and Marcin Kluczek
Remote Sens. 2023, 15(9), 2392; https://doi.org/10.3390/rs15092392 - 3 May 2023
Cited by 21 | Viewed by 3812
Abstract
Wetlands are a critical component of the landscape for climate mitigation, adaptation, biodiversity, and human health and prosperity. Keeping an eye on wetland vegetation is crucial due to it playing a major role in the planet’s carbon cycle and ecosystem management. By measuring [...] Read more.
Wetlands are a critical component of the landscape for climate mitigation, adaptation, biodiversity, and human health and prosperity. Keeping an eye on wetland vegetation is crucial due to it playing a major role in the planet’s carbon cycle and ecosystem management. By measuring the chlorophyll fluorescence (ChF) emitted by plants, we can get a precise understanding of the current state and photosynthetic activity. In this study, we applied the Extreme Gradient Boost (XGBoost) algorithm to map ChF in the Biebrza Valley, which has a unique ecosystem in Europe for peatlands, as well as highly diversified flora and fauna. Our results revealed the advantages of using a set of classifiers derived from EO Sentinel-2 (S-2) satellite image mosaics to accurately map the spatio-temporal distribution of ChF in a terrestrial landscape. The validation proved that the XGBoost algorithm is quite accurate in estimating ChF with a good determination of 0.71 and least bias of 0.012. The precision of chlorophyll fluorescence measurements is reliant upon determining the optimal S-2 satellite overpass time, which is influenced by the developmental stage of the plants at various points during the growing season. Finally, the model performance results indicated that biophysical factors are characterized by greenness- and leaf-pigment-related spectral indices. However, utilizing vegetation indices based on extended periods of remote sensing data that better capture land phenology features can improve the accuracy of mapping chlorophyll fluorescence. Full article
Show Figures

Graphical abstract

13 pages, 5499 KiB  
Communication
Monitoring Ash Dieback in Europe—An Unrevealed Perspective for Remote Sensing?
by Mateo Gašparović, Ivan Pilaš, Damir Klobučar and Iva Gašparović
Remote Sens. 2023, 15(5), 1178; https://doi.org/10.3390/rs15051178 - 21 Feb 2023
Cited by 1 | Viewed by 2027
Abstract
The ash dieback pandemic, caused by the invasive fungus Hymenoscyphus fraxineus, represents one of Europe’s biggest threats to preserving natural biodiversity. To ensure the suppression of forest damage caused by fungi, timely recognition of the symptoms of ash dieback and further continuous monitoring [...] Read more.
The ash dieback pandemic, caused by the invasive fungus Hymenoscyphus fraxineus, represents one of Europe’s biggest threats to preserving natural biodiversity. To ensure the suppression of forest damage caused by fungi, timely recognition of the symptoms of ash dieback and further continuous monitoring on an adequate spatial scale are essential. Visual crown damage assessment is currently the most common method used for identifying ash dieback, but it lacks the spatial and temporal coverage required for effective disease suppression. Remote sensing technologies, with the capabilities of fast and repetitive retrieval of information over a large spatial scale, could present efficient supplementary methods for ash damage detection and disease monitoring. In this study, we provided a synthesis of the existing remote sensing methods and applications that considers ash dieback disease, and we described the lifecycle of the disease using the major symptoms that remote sensing technologies can identify. Unfortunately, although effective methods of monitoring biotic damage through remote sensing have been developed, ash dieback has only been addressed in two research studies in the United Kingdom and Germany. These studies were based on single-date hyperspectral and very-high-resolution imagery in combination with machine learning, using previously specified ground-truth information regarding crown damage status. However, no study exists using high-resolution imagery such as Sentinel-2 or radar Sentinel-1, although some preliminary project results show that these coarser sources of information could be applicable for ash dieback detection and monitoring in cases of Fraxinus angustifolia, which forms pure, more homogenous stands in Southern Europe. Full article
Show Figures

Figure 1

20 pages, 5802 KiB  
Article
Using Satellite Imagery and Aerial Orthophotos for the Multi-Decade Monitoring of Subalpine Norway Spruce Stands Changes in Gorce National Park, Poland
by Wojciech Krawczyk and Piotr Wężyk
Remote Sens. 2023, 15(4), 951; https://doi.org/10.3390/rs15040951 - 9 Feb 2023
Viewed by 1680
Abstract
In the last 50 years, forest disturbances, caused mainly by insect outbreaks and windstorms, had a significant impact on the subalpine Norway spruce (Picea abies (L.) H. Karst.) stands across Europe. The high intensity of these factors often led to complete dieback [...] Read more.
In the last 50 years, forest disturbances, caused mainly by insect outbreaks and windstorms, had a significant impact on the subalpine Norway spruce (Picea abies (L.) H. Karst.) stands across Europe. The high intensity of these factors often led to complete dieback of existing forest stands, as in Gorce National Park (Southern Poland). The aim of this study was to monitor land cover changes in subalpine Norway spruce stands and their dynamics in Gorce NP in the years 1977–2020 (43 years), with the use of archival remote sensing data. The study area was divided into two subareas: A—the Kudłoń and B—the Jaworzyna range. Changes were tracked in six defined land cover classes, based on available aerial orthophotos and Landsat (NASA) imagery, with the help of the authors’ photointerpretation key. The results showed that almost 50% of old-growth Norway spruce stands died in the analyzed time period (50.9% in subarea A; 48.8% in subarea B). However, young forests appeared in almost 17% of the study area (20.7% and 14.2% in subarea A and B, respectively). The dynamics of land cover changes were different for the analyzed subareas; in subarea A Norway spruce dieback processes weakened at the end of the analyzed time period, whereas in subarea B they maintained high intensity. The process of old-growth Norway spruce stands dieback is still occurring in Gorce NP, but it does not result in the disappearance of the whole subalpine spruce forest ecosystem but is rather a generational change, due to emerging young forests. Full article
Show Figures

Figure 1

18 pages, 10429 KiB  
Article
Evaluating Threatened Bird Occurrence in the Tropics by Using L-Band SAR Remote Sensing Data
by Shoko Kobayashi, Motoko S. Fujita, Yoshiharu Omura, Dendy S. Haryadi, Ahmad Muhammad, Mohammad Irham and Satomi Shiodera
Remote Sens. 2023, 15(4), 947; https://doi.org/10.3390/rs15040947 - 9 Feb 2023
Viewed by 1547
Abstract
The biodiversity loss in Southeast Asia indicates an urgent need for long-term monitoring, which is lacking. Much attention is being directed toward bird diversity monitoring using remote sensing, based on relation to forest structure. However, few studies have utilized space-borne active microwave remote [...] Read more.
The biodiversity loss in Southeast Asia indicates an urgent need for long-term monitoring, which is lacking. Much attention is being directed toward bird diversity monitoring using remote sensing, based on relation to forest structure. However, few studies have utilized space-borne active microwave remote sensing, which has considerable advantages in terms of repetitive observations over tropical areas. Here, we evaluate threatened bird occurrence from L-band satellite data explaining forest structure in Sumatra, Indonesia. First, we identified L-band parameters with strong correlations with the forest layer structure, defined as forest floor, understory, and canopy layers. Then, we analyzed the correlation between threatened bird occurrence and L-band parameters identified as explaining forest structure. The results reveal that several parameters can represent the layers of forest floor, understory, and canopy. Subsequent statistical analysis elucidated that forest-dependent and threatened bird species exhibit significant positive correlations with the selected L-band parameters explaining forest floor and understory. Our results highlight the potential of applying microwave satellite remote sensing to evaluate bird diversity through forest structure estimation, although a more comprehensive study is needed to strengthen our findings. Full article
Show Figures

Figure 1

15 pages, 17572 KiB  
Article
Tree Crown Detection and Delineation in a Temperate Deciduous Forest from UAV RGB Imagery Using Deep Learning Approaches: Effects of Spatial Resolution and Species Characteristics
by Yi Gan, Quan Wang and Atsuhiro Iio
Remote Sens. 2023, 15(3), 778; https://doi.org/10.3390/rs15030778 - 29 Jan 2023
Cited by 13 | Viewed by 4638
Abstract
The automatic detection of tree crowns and estimation of crown areas from remotely sensed information offer a quick approach for grasping the dynamics of forest ecosystems and are of great significance for both biodiversity and ecosystem conservation. Among various types of remote sensing [...] Read more.
The automatic detection of tree crowns and estimation of crown areas from remotely sensed information offer a quick approach for grasping the dynamics of forest ecosystems and are of great significance for both biodiversity and ecosystem conservation. Among various types of remote sensing data, unmanned aerial vehicle (UAV)-acquired RGB imagery has been increasingly used for tree crown detection and crown area estimation; the method has efficient advantages and relies heavily on deep learning models. However, the approach has not been thoroughly investigated in deciduous forests with complex crown structures. In this study, we evaluated two widely used, deep-learning-based tree crown detection and delineation approaches (DeepForest and Detectree2) to assess their potential for detecting tree crowns from UAV-acquired RGB imagery in an alpine, temperate deciduous forest with a complicated species composition. A total of 499 digitized crowns, including four dominant species, with corresponding, accurate inventory data in a 1.5 ha study plot were treated as training and validation datasets. We attempted to identify an effective model to delineate tree crowns and to explore the effects of the spatial resolution on the detection performance, as well as the extracted tree crown areas, with a detailed field inventory. The results show that the two deep-learning-based models, of which Detectree2 (F1 score: 0.57) outperformed DeepForest (F1 score: 0.52), could both be transferred to predict tree crowns successfully. However, the spatial resolution had an obvious effect on the estimation accuracy of tree crown detection, especially when the resolution was greater than 0.1 m. Furthermore, Dectree2 could estimate tree crown areas accurately, highlighting its potential and robustness for tree detection and delineation. In addition, the performance of tree crown detection varied among different species. These results indicate that the evaluated approaches could efficiently delineate individual tree crowns in high-resolution optical images, while demonstrating the applicability of Detectree2, and, thus, have the potential to offer transferable strategies that can be applied to other forest ecosystems. Full article
Show Figures

Figure 1

16 pages, 3651 KiB  
Article
Expansion of Eucalyptus Plantation on Fertile Cultivated Lands in the North-Western Highlands of Ethiopia
by Gashaw Molla, Meseret B. Addisie and Gebiaw T. Ayele
Remote Sens. 2023, 15(3), 661; https://doi.org/10.3390/rs15030661 - 22 Jan 2023
Cited by 2 | Viewed by 2250
Abstract
Converting fertile, cultivated land into Eucalyptus plantations has become a common practice in Ethiopia. Integrating geospatial techniques with socio-economic data analysis can be a useful method to evaluate the expansion of Eucalyptus and its underlying factors. The objective of this study is to [...] Read more.
Converting fertile, cultivated land into Eucalyptus plantations has become a common practice in Ethiopia. Integrating geospatial techniques with socio-economic data analysis can be a useful method to evaluate the expansion of Eucalyptus and its underlying factors. The objective of this study is to detect the spatio-temporal patterns and main factors contributing to Eucalyptus expansion in the Mecha district of Ethiopia. To quantify the spatial extents of Eucalyptus plantations, the study employed Landsat images from 1991 to 2021 with supervised image classification in ERDAS Imagine 2015. In addition, 120 households were chosen using random sampling technique to incorporate socioeconomic factors related to Eucalyptus expansion. The result shows that, Eucalyptus plantations expanded significantly across the study area during the last three decades. Eucalyptus plantation covered 908.87 ha, 3719.05 ha, and 26261.9 ha in 1991, 2006, and 2021, respectively. The increment was mostly at the expense of fertile cultivated land use. The main reasons for its expansion are linked with farmer’s expectations of a better source of income, apprehension about the detrimental effects on nearby cropland, and its affordable production cost. In conclusion, the study area faces challenges from the uncontrolled expansion of Eucalyptus plantations on productive lands. Therefore, careful management and intervention strategies should be established to manage its rapid expansion. Full article
Show Figures

Graphical abstract

19 pages, 9897 KiB  
Article
The Use of an Airborne Laser Scanner for Rapid Identification of Invasive Tree Species Acer negundo in Riparian Forests
by Dominik Mielczarek, Piotr Sikorski, Piotr Archiciński, Wojciech Ciężkowski, Ewa Zaniewska and Jarosław Chormański
Remote Sens. 2023, 15(1), 212; https://doi.org/10.3390/rs15010212 - 30 Dec 2022
Cited by 6 | Viewed by 1890
Abstract
Invasive species significantly impact ecosystems, which is fostered by global warming. Their removal generates high costs to the greenery managers; therefore, quick and accurate identification methods can allow action to be taken with minimal impact on ecosystems. Remote sensing techniques such as Airborne [...] Read more.
Invasive species significantly impact ecosystems, which is fostered by global warming. Their removal generates high costs to the greenery managers; therefore, quick and accurate identification methods can allow action to be taken with minimal impact on ecosystems. Remote sensing techniques such as Airborne Laser Scanning (ALS) have been widely applied for this purpose. However, many species of invasive plants, such as Acer negundo L., penetrate the forests under the leaves and thus make recognition difficult. The strongly contaminated riverside forests in the Vistula valley were examined in the gradient of the center of Warsaw and beyond its limits within a Natura 2000 priority habitat (91E0), namely, alluvial and willow forests and poplars. This work aimed to assess the potentiality of a dual-wavelength ALS in identifying the stage of the A. negundo invasion. The research was carried out using over 500 test areas of 4 m diameter within the riparian forests, where the habitats did not show any significant traces of transformation. LiDAR bi-spectral data with a density of 6 points/m2 in both channels were acquired with a Riegl VQ-1560i-DW scanner. The implemented approach is based on crown parameters obtained from point cloud segmentation. The Adaptive Mean Shift 3D algorithm was used to separate individual crowns. This method allows for the delineation of individual dominant trees both in the canopy (horizontal segmentation) and undergrowth (vertical segmentation), taking into account the diversified structure of tree stands. The geometrical features and distribution characteristics of the GNDVI (Green Normalized Vegetation Index) were calculated for all crown segments. These features were found to be essential to distinguish A. negundo from other tree species. The classification was based on the sequential additive modeling algorithm using a multi-class loss function. Results with a high accuracy, exceeding 80%, allowed for identifying and localizing tree crowns belonging to the invasive species. With the presented method, we could determine dendrometric traits such as the age of the tree, its height, and the height of the covering leaves of the trees. Full article
Show Figures

Graphical abstract

14 pages, 2862 KiB  
Article
Tree Species Classification Using Plant Functional Traits and Leaf Spectral Properties along the Vertical Canopy Position
by Yicen Zhang, Junjie Wang, Zhifeng Wu, Juyu Lian, Wanhui Ye and Fangyuan Yu
Remote Sens. 2022, 14(24), 6227; https://doi.org/10.3390/rs14246227 - 8 Dec 2022
Cited by 2 | Viewed by 1650
Abstract
Plant functional traits are rarely used in tree species classification, and the impact of vertical canopy positions on collecting samples for classification also remains unclear. We aim to explore the feasibility and effectiveness of leaf traits in classification, as well as to detect [...] Read more.
Plant functional traits are rarely used in tree species classification, and the impact of vertical canopy positions on collecting samples for classification also remains unclear. We aim to explore the feasibility and effectiveness of leaf traits in classification, as well as to detect the effect of vertical position on classification accuracy. This work will deepen our understanding of the ecological mechanism of natural forest structure and succession from new perspectives. In this study, we collected foliar samples from three canopy layers (upper, middle and lower) and measured their spectra, as well as eight well-known leaf traits. We used a leaf hyperspectral reflectance (LHR) dataset, leaf functional traits (LFT) dataset and LFT + LHR dataset to classify six dominant tree species in a subtropical evergreen broad-leaved forest. Our results showed that the LFT + LHR dataset achieved the highest classification results (overall accuracy (OA) = 77.65% and Kappa = 0.73), followed by the LFT dataset (OA = 74.26% and Kappa = 0.69) and the LHR dataset (OA = 69.06% and Kappa = 0.63). Along the vertical canopy, the OA and Kappa increased from the lower to the upper layers, and the combination data of the three canopy layers achieved the highest accuracy. For the individual tree species, the shade-tolerant species (including Machilus chinensis, Cryptocarya chinensis and Cryptocarya concinna) produced higher accuracies than the light-demanding species (including Schima superba and Castanopsis chinensis). Our results provide an approach for enhancing tree species recognition from the plant physiology and biochemistry perspective and emphasize the importance of vertical direction in forest community research. Full article
Show Figures

Graphical abstract

26 pages, 2496 KiB  
Article
Assessment of Iran’s Mangrove Forest Dynamics (1990–2020) Using Landsat Time Series
by Yousef Erfanifard, Mohsen Lotfi Nasirabad and Krzysztof Stereńczak
Remote Sens. 2022, 14(19), 4912; https://doi.org/10.3390/rs14194912 - 1 Oct 2022
Cited by 8 | Viewed by 2281 | Correction
Abstract
Mangrove forests distributed along the coast of southern Iran are an important resource and a vital habitat for species communities and the local people. In this study, accurate mapping and spatiotemporal change detection were conducted on Iran’s mangroves for three decades, using the [...] Read more.
Mangrove forests distributed along the coast of southern Iran are an important resource and a vital habitat for species communities and the local people. In this study, accurate mapping and spatiotemporal change detection were conducted on Iran’s mangroves for three decades, using the Landsat imagery available for the years 1990, 2000, 2010, and 2020. Four general vegetation indices and eight mangrove-specific indices were employed for mangrove mapping in three study sites. Additionally, six important landscape metrics were implemented to quantify the spatiotemporal alteration of the mangrove forests during the study period. Our results showed the robustness of the submerged mangrove recognition index (SMRI), validated as the most effective index (F1-score ≥ 0.89), which was used for mangrove identification within all nine sites. The mangrove area of southern Iran was estimated at approximately 13,000 ha in 2020, with an overall increase of 2313 ha over the whole period. A similar trend could be observed for both the landscape connectivity and complexity. Our results revealed that a stronger connectivity and higher complexity could be detected in most sites, while there was increased fragmentation and a weaker connection in some locations. This study provides an accurate map of Iran’s mangrove forests over time and space. Full article
Show Figures

Figure 1

Back to TopTop