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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: closed (31 May 2022) | Viewed by 47485

Special Issue Editors


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Guest Editor
Spatial Ecology and Conservation Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA
Interests: Sustainability science; Unmanned aerial vehicles (UAVs); GatorEye

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Guest Editor
Graduate Program in Forestry, Universidade Federal do Parana, Curitiba, Brazil
Interests: lidar remote sensing; digital aerial photogrammetry; tropical and forest plantations; forest inventory and spatial analysis
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Forest Biometrics and Remote Sensing Lab (Silva Lab), School of Forest, Fisheriers and Geomatics Science, University of Florida, P.O. Box 110410, Gainesville, FL 32611, USA
Interests: lidar remote sensing (ALS, TLS, UAV-lidar, GEDI); tropical forest structure and ecology; industrial forest plantations, algorithms and tools development; data integration and change detection
Special Issues, Collections and Topics in MDPI journals

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 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.

Published Papers (12 papers)

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16 pages, 2223 KiB  
Article
Estimation of Intertidal Oyster Reef Density Using Spectral and Structural Characteristics Derived from Unoccupied Aircraft Systems and Structure from Motion Photogrammetry
by Anna E. Windle, Brandon Puckett, Klaus B. Huebert, Zofia Knorek, David W. Johnston and Justin T. Ridge
Remote Sens. 2022, 14(9), 2163; https://doi.org/10.3390/rs14092163 - 30 Apr 2022
Cited by 7 | Viewed by 2809
Abstract
Eastern oysters (Crassostrea virginica) are an important component of the ecology and economy in coastal zones. Through the long-term consolidation of densely clustered shells, oyster reefs generate three-dimensional and complex structures that yield a suite of ecosystem services, such as nursery habitat, [...] Read more.
Eastern oysters (Crassostrea virginica) are an important component of the ecology and economy in coastal zones. Through the long-term consolidation of densely clustered shells, oyster reefs generate three-dimensional and complex structures that yield a suite of ecosystem services, such as nursery habitat, stabilizing shorelines, regulating nutrients, and increasing biological diversity. The decline of global oyster habitat has been well documented and can be attributed to factors, such as overharvesting, pollution, and disease. Monitoring oyster reefs is necessary to evaluate persistence and track changes in habitat conditions but can be time and labor intensive. In this present study, spectral and structural metrics of intertidal oyster reefs derived from Unoccupied Aircraft Systems (UAS) and Structure from Motion (SfM) outputs are used to estimate intertidal oyster density. This workflow provides a remote, rapid, nondestructive, and potentially standardizable method to assess large-scale intertidal oyster reef density that will significantly improve management strategies to protect this important coastal resource from habitat degradation. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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15 pages, 6957 KiB  
Article
Mapping Restoration Activities on Dirk Hartog Island Using Remotely Piloted Aircraft Imagery
by Lucy Wilson, Richard van Dongen, Saul Cowen and Todd P. Robinson
Remote Sens. 2022, 14(6), 1402; https://doi.org/10.3390/rs14061402 - 14 Mar 2022
Cited by 4 | Viewed by 2628
Abstract
Conservation practitioners require cost-effective and repeatable remotely sensed data for assistive monitoring. This paper tests the ability of standard remotely piloted aircraft (DJI Phantom 4 Pro) imagery to discriminate between plant species in a rangeland environment. Flights were performed over two 0.3–0.4 ha [...] Read more.
Conservation practitioners require cost-effective and repeatable remotely sensed data for assistive monitoring. This paper tests the ability of standard remotely piloted aircraft (DJI Phantom 4 Pro) imagery to discriminate between plant species in a rangeland environment. Flights were performed over two 0.3–0.4 ha exclusion plot sites, established as controls to protect vegetation from translocated animal disturbance on Dirk Hartog Island, Western Australia. Comparisons of discriminatory variables, classification potential, and optimal flight height were made between plot sites with different plant species diversity. We found reflectance bands and height variables to have high differentiation potential, whilst measures of texture were less useful for multisegmented plant canopies. Discrimination between species varied with omission errors ranging from 13 to 93%. Purposely resampling c. 5 mm imagery as captured at 20–25 m above terrain identified that a flight height of 120 m would improve capture efficiency in future surveys without hindering accuracy. Overall accuracy at a site with low species diversity (n = 4) was 70%, which is an encouraging result given the imagery is limited to visible spectral bands. With higher species diversity (n = 10), the accuracy reduced to 53%, although it is expected to improve with additional bands or grouping like species. Findings suggest that in rangeland environments with low species diversity, monitoring using a standard RPA is viable. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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21 pages, 6484 KiB  
Article
UAV LiDAR Survey for Archaeological Documentation in Chiapas, Mexico
by Whittaker Schroder, Timothy Murtha, Charles Golden, Andrew K. Scherer, Eben N. Broadbent, Angélica M. Almeyda Zambrano, Kelsey Herndon and Robert Griffin
Remote Sens. 2021, 13(23), 4731; https://doi.org/10.3390/rs13234731 - 23 Nov 2021
Cited by 14 | Viewed by 3553
Abstract
Airborne laser scanning has proven useful for rapid and extensive documentation of historic cultural landscapes after years of applications mapping natural landscapes and the built environment. The recent integration of unoccupied aerial vehicles (UAVs) with LiDAR systems is potentially transformative and offers complementary [...] Read more.
Airborne laser scanning has proven useful for rapid and extensive documentation of historic cultural landscapes after years of applications mapping natural landscapes and the built environment. The recent integration of unoccupied aerial vehicles (UAVs) with LiDAR systems is potentially transformative and offers complementary data for mapping targeted areas with high precision and systematic study of coupled natural and human systems. We report the results of data capture, analysis, and processing of UAV LiDAR data collected in the Maya Lowlands of Chiapas, Mexico in 2019 for a comparative landscape study. Six areas of archaeological settlement and long-term land-use reflecting a diversity of environments, land cover, and archaeological features were studied. These missions were characterized by areas that were variably forested, rugged, or flat, and included pre-Hispanic settlements and agrarian landscapes. Our study confirms that UAV LiDAR systems have great potential for broader application in high-precision archaeological mapping applications. We also conclude that these studies offer an important opportunity for multi-disciplinary collaboration. UAV LiDAR offers high-precision information that is not only useful for mapping archaeological features, but also provides critical information about long-term land use and landscape change in the context of archaeological resources. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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18 pages, 3150 KiB  
Article
Unmanned Aerial Vehicle (UAV)-Based Mapping of Acacia saligna Invasion in the Mediterranean Coast
by Flavio Marzialetti, Ludovico Frate, Walter De Simone, Anna Rita Frattaroli, Alicia Teresa Rosario Acosta and Maria Laura Carranza
Remote Sens. 2021, 13(17), 3361; https://doi.org/10.3390/rs13173361 - 25 Aug 2021
Cited by 24 | Viewed by 4074
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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26 pages, 14377 KiB  
Article
UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)
by Bojana Ivošević, Predrag Lugonja, Sanja Brdar, Mirjana Radulović, Ante Vujić and João Valente
Remote Sens. 2021, 13(16), 3272; https://doi.org/10.3390/rs13163272 - 18 Aug 2021
Cited by 3 | Viewed by 3306
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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15 pages, 4024 KiB  
Article
Differentiation of River Sediments Fractions in UAV Aerial Images by Convolution Neural Network
by Hitoshi Takechi, Shunsuke Aragaki and Mitsuteru Irie
Remote Sens. 2021, 13(16), 3188; https://doi.org/10.3390/rs13163188 - 12 Aug 2021
Cited by 9 | Viewed by 2457
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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23 pages, 5648 KiB  
Article
Monitoring the Structure of Regenerating Vegetation Using Drone-Based Digital Aerial Photogrammetry
by Rik J. G. Nuijten, Nicholas C. Coops, Catherine Watson and Dustin Theberge
Remote Sens. 2021, 13(10), 1942; https://doi.org/10.3390/rs13101942 - 16 May 2021
Cited by 8 | Viewed by 3980
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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19 pages, 11600 KiB  
Article
Characterising Termite Mounds in a Tropical Savanna with UAV Laser Scanning
by Barbara D’hont, Kim Calders, Harm Bartholomeus, Tim Whiteside, Renee Bartolo, Shaun Levick, Sruthi M. Krishna Moorthy, Louise Terryn and Hans Verbeeck
Remote Sens. 2021, 13(3), 476; https://doi.org/10.3390/rs13030476 - 29 Jan 2021
Cited by 11 | Viewed by 3888
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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17 pages, 6662 KiB  
Article
Single-Pass UAV-Borne GatorEye LiDAR Sampling as a Rapid Assessment Method for Surveying Forest Structure
by Gabriel Atticciati Prata, Eben North Broadbent, Danilo Roberti Alves de Almeida, Joseph St. Peter, Jason Drake, Paul Medley, Ana Paula Dalla Corte, Jason Vogel, Ajay Sharma, Carlos Alberto Silva, Angelica Maria Almeyda Zambrano, Ruben Valbuena and Ben Wilkinson
Remote Sens. 2020, 12(24), 4111; https://doi.org/10.3390/rs12244111 - 16 Dec 2020
Cited by 11 | Viewed by 3827
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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19 pages, 8070 KiB  
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
by Marcus V. N. d’Oliveira, Eben N. Broadbent, Luis C. Oliveira, Danilo R. A. Almeida, Daniel A. Papa, Manuel E. Ferreira, Angelica M. Almeyda Zambrano, Carlos A. Silva, Felipe S. Avino, Gabriel A. Prata, Ricardo A. Mello, Evandro O. Figueiredo, Lúcio A. de Castro Jorge, Leomar Junior, Rafael W. Albuquerque, Pedro H. S. Brancalion, Ben Wilkinson and Marcelo Oliveira-da-Costa
Remote Sens. 2020, 12(11), 1754; https://doi.org/10.3390/rs12111754 - 29 May 2020
Cited by 28 | Viewed by 6749
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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11 pages, 2044 KiB  
Technical Note
Assessment of Canopy Health with Drone-Based Orthoimagery in a Southern Appalachian Red Spruce Forest
by Ryley C. Harris, Lisa M. Kennedy, Thomas J. Pingel and Valerie A. Thomas
Remote Sens. 2022, 14(6), 1341; https://doi.org/10.3390/rs14061341 - 10 Mar 2022
Cited by 9 | Viewed by 2947
Abstract
Consumer-grade drone-produced digital orthoimagery is a valuable tool for conservation management and enables the low-cost monitoring of remote ecosystems. This study demonstrates the applicability of RGB orthoimagery for the assessment of forest health at the scale of individual trees in a 46-hectare plot [...] Read more.
Consumer-grade drone-produced digital orthoimagery is a valuable tool for conservation management and enables the low-cost monitoring of remote ecosystems. This study demonstrates the applicability of RGB orthoimagery for the assessment of forest health at the scale of individual trees in a 46-hectare plot of rare southern Appalachian red spruce forest on Whitetop Mountain, Virginia. We used photogrammetric Structure from Motion software Pix4Dmapper with drone-collected imagery to generate a mosaic for point cloud reconstruction and orthoimagery of the plot. Using 3-band RBG digital orthoimagery, we visually classified 9402 red spruce individuals, finding 8700 healthy (92.5%), 251 declining/dying (2.6%), and 451 dead (4.8%). We mapped individual spruce trees in each class and produced kernel density maps of health classes (live, dead, and dying). Our approach provided a nearly gap-free assessment of the red spruce canopy in our study site, versus a much more time-intensive field survey. Our maps provided useful information on stand mortality patterns and canopy gaps that could be used by managers to identify optimal locations for selective thinning to facilitate understory sapling regeneration. This approach, dependent mainly on an off-the-shelf drone system and visual interpretation of orthoimagery, could be applied by land managers to measure forest health in other spruce, or possibly spruce-fir, communities in the Appalachians. Our study highlights the usefulness of drone-produced orthoimagery for conservation monitoring, presenting a valid and accessible protocol for the monitoring and assessment of forest health in remote spruce, and possibly other conifer, populations. Adoption of drone-based monitoring may be especially useful in light of climate change and the possible displacement of southern Appalachian red spruce (and spruce-fir) ecosystems by the upslope migration of deciduous trees. Full article
(This article belongs to the Special Issue Drones for Ecology and Conservation)
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10 pages, 1407 KiB  
Technical Note
Evaluating Alternative Flight Plans in Thermal Drone Wildlife Surveys—Simulation Study
by Julia Witczuk and Stanisław Pagacz
Remote Sens. 2021, 13(6), 1102; https://doi.org/10.3390/rs13061102 - 14 Mar 2021
Cited by 6 | Viewed by 3410
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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