Special Issue "Drones for Ecology and Conservation"

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

Deadline for manuscript submissions: 31 May 2022.

Special Issue Editors

Dr. Angelica Maria Almeyda Zambrano
E-Mail Website
Guest Editor
Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, USA
Interests: Sustainability science; Unmanned aerial vehicles (UAVs); GatorEye
Dr. Eben Broadbent
E-Mail Website
Guest Editor
School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32603, USA
Interests: unmanned aerial vehicles (UAVs); forest ecology; airborne sensors; GatorEye
Special Issues and Collections in MDPI journals
Dr. Ana Paula Dalla Corte
E-Mail Website
Guest Editor
Forest and Wood Science Center, Department of Forest Sciences, Federal University of Paraná (UFPR), Brazil
Interests: lidar remote sensing; digital aerial photogrammetry; tropical and forest plantations; forest inventory and spatial analysis
Dr. Carlos Alberto Silva
E-Mail Website
Guest Editor
1. University of Maryland College Park, Department of Geographical Sciences, USA;
2. School of Forest Resources and Conservation, University of Florida, USA
Interests: Lidar and hyperspectral remote sensing; tropical forest structure and ecology; industrial forest plantations, algorithms and tools development; data integration and change detection

Special Issue Information

Dear Colleagues,

Recent increases in the use of drone-borne sensors for ecological and conservation-related applications have been motivated by reduced costs, increased availability, new and enhanced passive and active sensors (e.g., hyperspectral and lidar), and the development of sophisticated fusion algorithms. Data have moved beyond mapping and monitoring ecosystem flora structure and composition, to directly mapping wildlife, and now to improve understanding of advanced community ecological, conservation biology, and forest ecology theory and application, and human dimensions of sustainability in varied landscape mosaics. In this Special Issue, we invite submissions from the broad ecological and applied conservation community, including but not limited to forest ecologists, wildlife biologists, conservation biologists, land-use and land-cover experts, and sustainability science researchers, who use drone-borne sensors ranging from small and low-cost systems (e.g., DJI Phantom) to complex multisensor fusion platforms (e.g., www.gatoreye.org).

We will be accepting review articles, technical notes, and research contributions. Specifically, innovative themes such as the following subtopics described below will be welcome:

  • Use of UAV-LiDAR for parameters attribute at landscape levels and individual trees;
  • Application of Unmanned aerial vehicles (UAV) and photogrammetry 3D for helping in the development of natural sciences;
  • Integration of remote sensors for the structural representation of forest and trees;
  • Integration of platforms for analysis of distribution and density of fauna and flora species;
  • Development of methodologies using UAV data approaches for applied conservation;
  • Use of drone-borne data for assessing sustainability as relates to human–environment interactions and land use and land cover change (LULCC).

Other themes linked to the title of the Special Edition “Drones for Ecology and Conservation” are also welcome. Please feel free to get in touch with us if you have any questions.

Dr. Angelica Maria Almeyda Zambrano
Dr. Eben North Broadbent
Dr. Ana Paula Dalla Corte
Dr. Carlos Alberto Silva
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 papers will be 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 2400 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.

Published Papers (8 papers)

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Article
Unmanned Aerial Vehicle (UAV)-Based Mapping of Acacia saligna Invasion in the Mediterranean Coast
Remote Sens. 2021, 13(17), 3361; https://doi.org/10.3390/rs13173361 - 25 Aug 2021
Viewed by 248
Abstract
Remote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on dynamic and heterogeneous landscapes, using satellite RS data, is not always feasible. Unmanned aerial vehicles (UAV) with ultra-high spatial resolution data represent a promising tool [...] Read more.
Remote Sensing (RS) is a useful tool for detecting and mapping Invasive Alien Plants (IAPs). IAPs mapping on dynamic and heterogeneous landscapes, using satellite RS data, is not always feasible. Unmanned aerial vehicles (UAV) with ultra-high spatial resolution data represent a promising tool for IAPs detection and mapping. This work develops an operational workflow for detecting and mapping Acacia saligna invasion along Mediterranean coastal dunes. In particular, it explores and tests the potential of RGB (Red, Green, Blue) and multispectral (Green, Red, Red Edge, Near Infra—Red) UAV images collected in pre-flowering and flowering phenological stages for detecting and mapping A. saligna. After ortho—mosaics generation, we derived from RGB images the DSM (Digital Surface Model) and HIS (Hue, Intensity, Saturation) variables, and we calculated the NDVI (Normalized Difference Vegetation Index). For classifying images of the two phenological stages we built a set of raster stacks which include different combination of variables. For image classification, we used the Geographic Object-Based Image Analysis techniques (GEOBIA) in combination with Random Forest (RF) classifier. All classifications derived from RS information (collected on pre-flowering and flowering stages and using different combinations of variables) produced A. saligna maps with acceptable accuracy values, with higher performances on classification derived from flowering period images, especially using DSM + HIS combination. The adopted approach resulted an efficient method for mapping and early detection of IAPs, also in complex environments offering a sound support to the prioritization of conservation and management actions claimed by the EU IAS Regulation 1143/2014. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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Article
UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)
Remote Sens. 2021, 13(16), 3272; https://doi.org/10.3390/rs13163272 - 18 Aug 2021
Viewed by 395
Abstract
Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land [...] Read more.
Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. Object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms—support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)—were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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Article
Differentiation of River Sediments Fractions in UAV Aerial Images by Convolution Neural Network
Remote Sens. 2021, 13(16), 3188; https://doi.org/10.3390/rs13163188 - 12 Aug 2021
Viewed by 286
Abstract
Riverbed material has multiple functions in river ecosystems, such as habitats, feeding grounds, spawning grounds, and shelters for aquatic organisms, and particle size of riverbed material reflects the tractive force of the channel flow. Therefore, regular surveys of riverbed material are conducted for [...] Read more.
Riverbed material has multiple functions in river ecosystems, such as habitats, feeding grounds, spawning grounds, and shelters for aquatic organisms, and particle size of riverbed material reflects the tractive force of the channel flow. Therefore, regular surveys of riverbed material are conducted for environmental protection and river flood control projects. The field method is the most conventional riverbed material survey. However, conventional surveys of particle size of riverbed material require much labor, time, and cost to collect material on site. Furthermore, its spatial representativeness is also a problem because of the limited survey area against a wide riverbank. As a further solution to these problems, in this study, we tried an automatic classification of riverbed conditions using aerial photography with an unmanned aerial vehicle (UAV) and image recognition with artificial intelligence (AI) to improve survey efficiency. Due to using AI for image processing, a large number of images can be handled regardless of whether they are of fine or coarse particles. We tried a classification of aerial riverbed images that have the difference of particle size characteristics with a convolutional neural network (CNN). GoogLeNet, Alexnet, VGG-16 and ResNet, the common pre-trained networks, were retrained to perform the new task with the 70 riverbed images using transfer learning. Among the networks tested, GoogleNet showed the best performance for this study. The overall accuracy of the image classification reached 95.4%. On the other hand, it was supposed that shadows of the gravels caused the error of the classification. The network retrained with the images taken in the uniform temporal period gives higher accuracy for classifying the images taken in the same period as the training data. The results suggest the potential of evaluating riverbed materials using aerial photography with UAV and image recognition with CNN. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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Article
Monitoring the Structure of Regenerating Vegetation Using Drone-Based Digital Aerial Photogrammetry
Remote Sens. 2021, 13(10), 1942; https://doi.org/10.3390/rs13101942 - 16 May 2021
Viewed by 808
Abstract
Measures of vegetation structure are often key within ecological restoration monitoring programs because a change in structure is rapidly identifiable, measurements are straightforward, and structure is often a good surrogate for species composition. This paper investigates the use of drone-based digital aerial photogrammetry [...] Read more.
Measures of vegetation structure are often key within ecological restoration monitoring programs because a change in structure is rapidly identifiable, measurements are straightforward, and structure is often a good surrogate for species composition. This paper investigates the use of drone-based digital aerial photogrammetry (DAP) for the characterization of the structure of regenerating vegetation as well as the ability to inform restoration programs through spatial arrangement assessment. We used cluster analysis on five DAP-derived metrics to classify vegetation structure into seven classes across three sites of ongoing restoration since linear disturbances in 2005, 2009, and 2014 in temperate and boreal coniferous forests in Alberta, Canada. The spatial arrangement of structure classes was assessed using land cover maps, mean patch size, and measures of local spatial association. We observed DAP heights of short-stature vegetation were consistently underestimated, but strong correlations (rs > 0.75) with field height were found for juvenile trees, shrubs, and perennials. Metrics of height and canopy complexity allowed for the extraction of relatively tall and complex vegetation structures, whereas canopy cover and height variability metrics enabled the classification of the shortest vegetation structures. We found that the boreal site disturbed in 2009 had the highest cover of classes associated with complex vegetation structures. This included early regenerative (22%) and taller (13.2%) wood-like structures as well as structures representative of tall graminoid and perennial vegetation (15.3%), which also showed the highest patchiness. The developed tools provide large-scale maps of the structure, enabling the identification and assessment of vegetational patterns, which is challenging based on traditional field sampling that requires pre-defined location-based hypotheses. The approach can serve as a basis for the evaluation of specialized restoration objectives as well as objectives tailored towards processes of ecological succession, and support prioritization of future inspections and mitigation measures. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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Article
Characterising Termite Mounds in a Tropical Savanna with UAV Laser Scanning
Remote Sens. 2021, 13(3), 476; https://doi.org/10.3390/rs13030476 - 29 Jan 2021
Cited by 1 | Viewed by 1106
Abstract
Termite mounds are found over vast areas in northern Australia, delivering essential ecosystem services, such as enhancing nutrient cycling and promoting biodiversity. Currently, the detection of termite mounds over large areas requires airborne laser scanning (ALS) or high-resolution satellite data, which lack precise [...] Read more.
Termite mounds are found over vast areas in northern Australia, delivering essential ecosystem services, such as enhancing nutrient cycling and promoting biodiversity. Currently, the detection of termite mounds over large areas requires airborne laser scanning (ALS) or high-resolution satellite data, which lack precise information on termite mound shape and size. For detailed structural measurements, we generally rely on time-consuming field assessments that can only cover a limited area. In this study, we explore if unmanned aerial vehicle (UAV)-based observations can serve as a precise and scalable tool for termite mound detection and morphological characterisation. We collected a unique data set of terrestrial laser scanning (TLS) and UAV laser scanning (UAV-LS) point clouds of a woodland savanna site in Litchfield National Park (Australia). We developed an algorithm that uses several empirical parameters for the semi-automated detection of termite mounds from UAV-LS and used the TLS data set (1 ha) for benchmarking. We detected 81% and 72% of the termite mounds in the high resolution (1800 points m2) and low resolution (680 points m2) UAV-LS data, respectively, resulting in an average detection of eight mounds per hectare. Additionally, we successfully extracted information about mound height and volume from the UAV-LS data. The high resolution data set resulted in more accurate estimates; however, there is a trade-off between area and detectability when choosing the required resolution for termite mound detection Our results indicate that UAV-LS data can be rapidly acquired and used to monitor and map termite mounds over relatively large areas with higher spatial detail compared to airborne and spaceborne remote sensing. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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Article
Single-Pass UAV-Borne GatorEye LiDAR Sampling as a Rapid Assessment Method for Surveying Forest Structure
Remote Sens. 2020, 12(24), 4111; https://doi.org/10.3390/rs12244111 - 16 Dec 2020
Cited by 3 | Viewed by 1234
Abstract
Unmanned aerial vehicles (UAV) allow efficient acquisition of forest data at very high resolution at relatively low cost, making it useful for multi-temporal assessment of detailed tree crowns and forest structure. Single-pass flight plans provide rapid surveys for key selected high-priority areas, but [...] Read more.
Unmanned aerial vehicles (UAV) allow efficient acquisition of forest data at very high resolution at relatively low cost, making it useful for multi-temporal assessment of detailed tree crowns and forest structure. Single-pass flight plans provide rapid surveys for key selected high-priority areas, but their accuracy is still unexplored. We compared aircraft-borne LiDAR with GatorEye UAV-borne LiDAR in the Apalachicola National Forest, USA. The single-pass approach produced digital terrain models (DTMs), with less than 1 m differences compared to the aircraft-derived DTM within a 145° field of view (FOV). Canopy height models (CHM) provided reliable information from the top layer of the forest, allowing reliable treetop detection up to wide angles; however, underestimations of tree heights were detected at 175 m from the flightline, with an error of 2.57 ± 1.57. Crown segmentation was reliable only within a 60° FOV, from which the shadowing effect made it unviable. Reasonable quality threshold values for LiDAR products were: 195 m (145° FOV) for DTMs, 95 m (110° FOV) for CHM, 160 to 180 m (~140° FOV) for ITD and tree heights, and 40 to 60 m (~60° FOV) for crown delineation. These findings also support the definition of mission parameters for standard grid-based flight plans under similar forest types and flight parameters. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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Article
Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft- and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil
Remote Sens. 2020, 12(11), 1754; https://doi.org/10.3390/rs12111754 - 29 May 2020
Cited by 9 | Viewed by 1768
Abstract
Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural [...] Read more.
Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural parameter estimates for monitoring in native and commercial forests. In this study, we compare products and aboveground biomass (AGB) estimations from LiDAR data acquired using an aircraft-borne system in 2015 and data collected by the unmanned aerial vehicle (UAV)-based GatorEye Unmanned Flying Laboratory in 2017 for ten forest inventory plots located in the Chico Mendes Extractive Reserve in Acre state, southwestern Brazilian Amazon. The LiDAR products were similar and comparable among the two platforms and sensors. Principal differences between derived products resulted from the GatorEye system flying lower and slower and having increased returns per second than the aircraft, resulting in a much higher point density overall (11.3 ± 1.8 vs. 381.2 ± 58 pts/m2). Differences in ground point density, however, were much smaller among the systems, due to the larger pulse area and increased number of returns per pulse of the aircraft system, with the GatorEye showing an approximately 50% higher ground point density (0.27 ± 0.09 vs. 0.42 ± 0.09). The LiDAR models produced by both sensors presented similar results for digital elevation models and estimated AGB. Our results validate the ability for UAV-borne LiDAR sensors to accurately quantify AGB in dense high-leaf-area tropical forests in the Amazon. We also highlight new possibilities using the dense point clouds of UAV-borne systems for analyses of detailed crown structure and leaf area density distribution of the forest interior. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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Technical Note
Evaluating Alternative Flight Plans in Thermal Drone Wildlife Surveys—Simulation Study
Remote Sens. 2021, 13(6), 1102; https://doi.org/10.3390/rs13061102 - 14 Mar 2021
Viewed by 655
Abstract
The rapidly developing technology of unmanned aerial vehicles (drones) extends to the availability of aerial surveys for wildlife research and management. However, regulations limiting drone operations to visual line of sight (VLOS) seriously affect the design of surveys, as flight paths must be [...] Read more.
The rapidly developing technology of unmanned aerial vehicles (drones) extends to the availability of aerial surveys for wildlife research and management. However, regulations limiting drone operations to visual line of sight (VLOS) seriously affect the design of surveys, as flight paths must be concentrated within small sampling blocks. Such a design is inferior to spatially unrestricted randomized designs available if operations beyond visual line of sight (BVLOS) are allowed. We used computer simulations to assess whether the VLOS rule affects the accuracy and precision of wildlife density estimates derived from drone collected data. We tested two alternative flight plans (VLOS vs. BVLOS) in simulated surveys of low-, medium- and high-density populations of a hypothetical ungulate species with three levels of effort (one to three repetitions). The population density was estimated using the ratio estimate and distance sampling method. The observed differences in the accuracy and precision of estimates from the VLOS and BVLOS surveys were relatively small and negligible. Only in the case of the low-density population (2 ind./100 ha) surveyed once was the VLOS design inferior to BVLOS, delivering biased and less precise estimates. These results show that while the VLOS regulations complicate survey logistics and interfere with random survey design, the quality of derived estimates does not have to be compromised. We advise testing alternative survey variants with the aid of computer simulations to achieve reliable estimates while minimizing survey costs. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Title: UAV-based mapping of dune plant invasions in the Mediterranean coast

Authors: MARZIALETTI Flavio, FRATE Ludovico, DE SIMONE Walter, ACOSTA Alicia T.R., CARRANZA Maria Laura

Keywords: Acacia saligna, Carpobrotus sp., coastal dunes, multi-temporal analysis, multispectral images, species flowering, unmanned aerial vehicles

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