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Ecological Applications of Drone-Based Remote Sensing

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A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Ecology".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 57037

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Special Issue Editor

Dr. Adam Cross

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Guest Editor

School of Molecular and Life Sciences, Curtin University, Perth 6845, Australia
Interests: ecological restoration; seed biology; community ecology and phytosociology; freshwater aquatic ecosystems; conservation biology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last decade has seen an exponential increase in the application of remote sensing to ecological monitoring research, in a diverse range of fields. While cost and availability have traditionally constrained the use of remote sensing in research projects, recent technological development has seen drones (UAVs, UAS, RPAS) become increasingly popular sensing tools that have greatly expanded research capacity. Significant translation research has seen drones transition from tools of predominantly agricultural application to their being employed with increasing novelty to address complex ecological questions, particularly in the monitoring of biological communities and in assessing the trajectory of ecological recovery. Drones are becoming smaller, cheaper, and capable of mounting a wider variety of sensors to collect a greater diversity and volume of data. However, despite the trend of drone-mounted sensors being used in novel ways to monitor a wide variety of environmental factors, they remain often applied to highly specific aims or questions and do not consider the wide potential for capturing associated ecological data.
Activities directed at returning ecological functioning to degraded ecosystems are being undertaken at increasing scale around the world, as we must achieve a net gain in the extent and function of indigenous ecosystems in coming decades if ambitious global targets relating to sustainable development and biodiversity preservation are to be met. However, ecological restoration is a complex process requiring detailed subsequent monitoring over long time periods to ensure that predetermined goals are being met and to inform adaptive management in situations where trajectories are unsatisfactory. Given the increasing spatial and temporal scales of ecological recovery projects, the demand for more rapid and accurate methods of predicting restoration trajectory is growing.
This Special Issue aims to present a selection of studies experimentally applying drones to ecological research questions, particularly in the context of conservation, rehabilitation, and ecological restoration. Significantly more research is required to improve the potential of UAVs as ecological monitoring tools. Many areas of application remain predominantly unexplored, for example, examination of the capacity to monitor at very fine scales; accurate assessments of the health and performance of non-agricultural plants; monitoring and tracking of the development of individual plants; reliable classification of species from complex native plant communities; and assessments of fauna behaviour and ecology.

Dr. Adam T. Cross
Guest Editor

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Keywords

remote sensing
ecology
rehabilitation
ecological restoration
conservation
communities

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Published Papers (10 papers)

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Research

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14 pages, 2858 KiB

Open AccessEditor’s ChoiceArticle

Species-Specific Responses of Bird Song Output in the Presence of Drones

by Andrew M. Wilson, Kenneth S. Boyle, Jennifer L. Gilmore, Cody J. Kiefer and Matthew F. Walker

Drones 2022, 6(1), 1; https://doi.org/10.3390/drones6010001 - 21 Dec 2021

Cited by 6 | Viewed by 3620

Abstract

Drones are now widely used to study wildlife, but their application in the study of bioacoustics is limited. Drones can be used to collect data on bird vocalizations, but an ongoing concern is that noise from drones could change bird vocalization behavior. To test for behavioral impact, we conducted an experiment using 30 sound localization arrays to track the song output of 7 songbird species before, during, and after a 3 min flight of a small quadcopter drone hovering 48 m above ground level. We analyzed 8303 song bouts, of which 2285, from 184 individual birds were within 50 m of the array centers. We used linear mixed effect models to assess whether patterns in bird song output could be attributed to the drone’s presence. We found no evidence of any effect of the drone on five species: American Robin Turdus migratorius, Common Yellowthroat Geothlypis trichas, Field Sparrow Spizella pusilla, Song Sparrow Melospiza melodia, and Indigo Bunting Passerina cyanea. However, we found a substantial decrease in Yellow Warbler Setophaga petechia song detections during the 3 min drone hover; there was an 81% drop in detections in the third minute (Wald test, p < 0.001) compared with before the drone’s introduction. By contrast, the number of singing Northern Cardinal Cardinalis cardinalis increased when the drone was overhead and remained almost five-fold higher for 4 min after the drone departed (p < 0.001). Further, we found an increase in cardinal contact/alarm calls when the drone was overhead, with the elevated calling rate lasting for 2 min after the drone departed (p < 0.001). Our study suggests that the responses of songbirds to drones may be species-specific, an important consideration when proposing the use of drones in avian studies. We note that recent advances in drone technology have resulted in much quieter drones, which makes us hopeful that the impact that we detected could be greatly reduced. Full article

(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)

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14 pages, 2865 KiB

Open AccessArticle

Incorporating Geographical Scale and Multiple Environmental Factors to Delineate the Breeding Distribution of Sea Turtles

by Liam C. Dickson, Kostas A. Katselidis, Christophe Eizaguirre and Gail Schofield

Drones 2021, 5(4), 142; https://doi.org/10.3390/drones5040142 - 26 Nov 2021

Cited by 6 | Viewed by 4453

Abstract

Temperature is often used to infer how climate influences wildlife distributions; yet, other parameters also contribute, separately and combined, with effects varying across geographical scales. Here, we used an unoccupied aircraft system to explore how environmental parameters affect the regional distribution of the terrestrial and marine breeding habitats of threatened loggerhead sea turtles (Caretta caretta). Surveys spanned four years and ~620 km coastline of western Greece, encompassing low (<10 nests/km) to high (100–500 nests/km) density nesting areas. We recorded 2395 tracks left by turtles on beaches and 1928 turtles occupying waters adjacent to these beaches. Variation in beach track and inwater turtle densities was explained by temperature, offshore prevailing wind, and physical marine and terrestrial factors combined. The highest beach-track densities (400 tracks/km) occurred on beaches with steep slopes and higher sand temperatures, sheltered from prevailing offshore winds. The highest inwater turtle densities (270 turtles/km) occurred over submerged sandbanks, with warmer sea temperatures associated with offshore wind. Most turtles (90%) occurred over nearshore submerged sandbanks within 10 km of beaches supporting the highest track densities, showing the strong linkage between optimal marine and terrestrial environments for breeding. Our findings demonstrate the utility of UASs in surveying marine megafauna and environmental data at large scales and the importance of integrating multiple factors in climate change models to predict species distributions. Full article

(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)

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15 pages, 6441 KiB

Open AccessArticle

Drones, Deep Learning, and Endangered Plants: A Method for Population-Level Census Using Image Analysis

by Kody R. Rominger and Susan E. Meyer

Drones 2021, 5(4), 126; https://doi.org/10.3390/drones5040126 - 28 Oct 2021

Cited by 5 | Viewed by 4074

Abstract

A census of endangered plant populations is critical to determining their size, spatial distribution, and geographical extent. Traditional, on-the-ground methods for collecting census data are labor-intensive, time-consuming, and expensive. Use of drone imagery coupled with application of rapidly advancing deep learning technology could greatly reduce the effort and cost of collecting and analyzing population-level data across relatively large areas. We used a customization of the YOLOv5 object detection model to identify and count individual dwarf bear poppy (Arctomecon humilis) plants in drone imagery obtained at 40 m altitude. We compared human-based and model-based detection at 40 m on n = 11 test plots for two areas that differed in image quality. The model out-performed human visual poppy detection for precision and recall, and was 1100× faster at inference/evaluation on the test plots. Model inference precision was 0.83, and recall was 0.74, while human evaluation resulted in precision of 0.67, and recall of 0.71. Both model and human performance were better in the area with higher-quality imagery, suggesting that image quality is a primary factor limiting model performance. Evaluation of drone-based census imagery from the 255 ha Webb Hill population with our customized YOLOv5 model was completed in <3 h and provided a reasonable estimate of population size (7414 poppies) with minimal investment of on-the-ground resources. Full article

(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)

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20 pages, 7847 KiB

Open AccessEditor’s ChoiceArticle

Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection

by William Reckling, Helena Mitasova, Karl Wegmann, Gary Kauffman and Rebekah Reid

Drones 2021, 5(4), 110; https://doi.org/10.3390/drones5040110 - 2 Oct 2021

Cited by 17 | Viewed by 8193

Abstract

Monitoring rare plant species is used to confirm presence, assess health, and verify population trends. Unmanned aerial systems (UAS) are ideal tools for monitoring rare plants because they can efficiently collect data without impacting the plant or endangering personnel. However, UAS flight planning can be subjective, resulting in ineffective use of flight time and overcollection of imagery. This study used a Maxent machine-learning predictive model to create targeted flight areas to monitor Geum radiatum, an endangered plant endemic to the Blue Ridge Mountains in North Carolina. The Maxent model was developed with ten environmental layers as predictors and known plant locations as training data. UAS flight areas were derived from the resulting probability raster as isolines delineated from a probability threshold based on flight parameters. Visual analysis of UAS imagery verified the locations of 33 known plants and discovered four previously undocumented occurrences. Semi-automated detection of plant species was explored using a neural network object detector. Although the approach was successful in detecting plants in on-ground images, no plants were identified in the UAS aerial imagery, indicating that further improvements are needed in both data acquisition and computer vision techniques. Despite this limitation, the presented research provides a data-driven approach to plan targeted UAS flight areas from predictive modeling, improving UAS data collection for rare plant monitoring. Full article

(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)

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16 pages, 24121 KiB

Open AccessArticle

Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets

by Galen Richardson, Sylvain G. Leblanc, Julie Lovitt, Krishan Rajaratnam and Wenjun Chen

Drones 2021, 5(3), 99; https://doi.org/10.3390/drones5030099 - 17 Sep 2021

Cited by 7 | Viewed by 4731

Abstract

Relating ground photographs to UAV orthomosaics is a key linkage required for accurate multi-scaled lichen mapping. Conventional methods of multi-scaled lichen mapping, such as random forest models and convolutional neural networks, heavily rely on pixel DN values for classification. However, the limited spectral range of ground photos requires additional characteristics to differentiate lichen from spectrally similar objects, such as bright logs. By applying a neural network to tiles of a UAV orthomosaics, additional characteristics, such as surface texture and spatial patterns, can be used for inferences. Our methodology used a neural network (UAV LiCNN) trained on ground photo mosaics to predict lichen in UAV orthomosaic tiles. The UAV LiCNN achieved mean user and producer accuracies of 85.84% and 92.93%, respectively, in the high lichen class across eight different orthomosaics. We compared the known lichen percentages found in 77 vegetation microplots with the predicted lichen percentage calculated from the UAV LiCNN, resulting in a R² relationship of 0.6910. This research shows that AI models trained on ground photographs effectively classify lichen in UAV orthomosaics. Limiting factors include the misclassification of spectrally similar objects to lichen in the RGB bands and dark shadows cast by vegetation. Full article

(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)

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18 pages, 8018 KiB

Open AccessArticle

Effect of the Solar Zenith Angles at Different Latitudes on Estimated Crop Vegetation Indices

by Milton Valencia-Ortiz, Worasit Sangjan, Michael Gomez Selvaraj, Rebecca J. McGee and Sindhuja Sankaran

Drones 2021, 5(3), 80; https://doi.org/10.3390/drones5030080 - 18 Aug 2021

Cited by 14 | Viewed by 4934

Abstract

Normalization of anisotropic solar reflectance is an essential factor that needs to be considered for field-based phenotyping applications to ensure reliability, consistency, and interpretability of time-series multispectral data acquired using an unmanned aerial vehicle (UAV). Different models have been developed to characterize the bidirectional reflectance distribution function. However, the substantial variation in crop breeding trials, in terms of vegetation structure configuration, creates challenges to such modeling approaches. This study evaluated the variation in standard vegetation indices and its relationship with ground-reference data (measured crop traits such as seed/grain yield) in multiple crop breeding trials as a function of solar zenith angles (SZA). UAV-based multispectral images were acquired and utilized to extract vegetation indices at SZA across two different latitudes. The pea and chickpea breeding materials were evaluated in a high latitude (46°36′39.92″ N) zone, whereas the rice lines were assessed in a low latitude (3°29′42.43″ N) zone. In general, several of the vegetation index data were affected by SZA (e.g., normalized difference vegetation index, green normalized difference vegetation index, normalized difference red-edge index, etc.) in both latitudes. Nevertheless, the simple ratio index (SR) showed less variability across SZA in both latitude zones amongst these indices. In addition, it was interesting to note that the correlation between vegetation indices and ground-reference data remained stable across SZA in both latitude zones. In summary, SR was found to have a minimum anisotropic reflectance effect in both zones, and the other vegetation indices can be utilized to evaluate relative differences in crop performances, although the absolute data would be affected by SZA. Full article

(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)

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14 pages, 6090 KiB

Open AccessFeature PaperArticle

UASea: A Data Acquisition Toolbox for Improving Marine Habitat Mapping

by Michaela Doukari, Marios Batsaris and Konstantinos Topouzelis

Drones 2021, 5(3), 73; https://doi.org/10.3390/drones5030073 - 3 Aug 2021

Cited by 4 | Viewed by 3474

Abstract

Unmanned aerial systems (UAS) are widely used in the acquisition of high-resolution information in the marine environment. Although the potential applications of UAS in marine habitat mapping are constantly increasing, many limitations need to be overcome—most of which are related to the prevalent environmental conditions—to reach efficient UAS surveys. The knowledge of the UAS limitations in marine data acquisition and the examination of the optimal flight conditions led to the development of the UASea toolbox. This study presents the UASea, a data acquisition toolbox that is developed for efficient UAS surveys in the marine environment. The UASea uses weather forecast data (i.e., wind speed, cloud cover, precipitation probability, etc.) and adaptive thresholds in a ruleset that calculates the optimal flight times in a day for the acquisition of reliable marine imagery using UAS in a given day. The toolbox provides hourly positive and negative suggestions, based on optimal or non-optimal survey conditions in a day, calculated according to the ruleset calculations. We acquired UAS images in optimal and non-optimal conditions and estimated their quality using an image quality equation. The image quality estimates are based on the criteria of sunglint presence, sea surface texture, water turbidity, and image naturalness. The overall image quality estimates were highly correlated with the suggestions of the toolbox, with a correlation coefficient of −0.84. The validation showed that 40% of the toolbox suggestions were a positive match to the images with higher quality. Therefore, we propose the optimal flight times to acquire reliable and accurate UAS imagery in the coastal environment through the UASea. The UASea contributes to proper flight planning and efficient UAS surveys by providing valuable information for mapping, monitoring, and management of the marine environment, which can be used globally in research and marine applications. Full article

(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)

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15 pages, 11848 KiB

Open AccessArticle

Measuring Height Characteristics of Sagebrush (Artemisia sp.) Using Imagery Derived from Small Unmanned Aerial Systems (sUAS)

by Ryan G. Howell, Ryan R. Jensen, Steven L. Petersen and Randy T. Larsen

Drones 2020, 4(1), 6; https://doi.org/10.3390/drones4010006 - 19 Feb 2020

Cited by 10 | Viewed by 5145

Abstract

In situ measurements of sagebrush have traditionally been expensive and time consuming. Currently, improvements in small Unmanned Aerial Systems (sUAS) technology can be used to quantify sagebrush morphology and community structure with high resolution imagery on western rangelands, especially in sensitive habitat of the Greater sage-grouse (Centrocercus urophasianus). The emergence of photogrammetry algorithms to generate 3D point clouds from true color imagery can potentially increase the efficiency and accuracy of measuring shrub height in sage-grouse habitat. Our objective was to determine optimal parameters for measuring sagebrush height including flight altitude, single- vs. double- pass, and continuous vs. pause features. We acquired imagery using a DJI Mavic Pro 2 multi-rotor Unmanned Aerial Vehicle (UAV) equipped with an RGB camera, flown at 30.5, 45, 75, and 120 m and implementing single-pass and double-pass methods, using continuous flight and paused flight for each photo method. We generated a Digital Surface Model (DSM) from which we derived plant height, and then performed an accuracy assessment using on the ground measurements taken at the time of flight. We found high correlation between field measured heights and estimated heights, with a mean difference of approximately 10 cm (SE = 0.4 cm) and little variability in accuracy between flights with different heights and other parameters after statistical correction using linear regression. We conclude that higher altitude flights using a single-pass method are optimal to measure sagebrush height due to lower requirements in data storage and processing time. Full article

(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)

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20 pages, 5375 KiB

Open AccessFeature PaperArticle

Multi-Sensor UAV Tracking of Individual Seedlings and Seedling Communities at Millimetre Accuracy

by Todd M. Buters, David Belton and Adam T. Cross

Drones 2019, 3(4), 81; https://doi.org/10.3390/drones3040081 - 30 Oct 2019

Cited by 18 | Viewed by 5698

Abstract

The increasing spatial and temporal scales of ecological recovery projects demand more rapid and accurate methods of predicting restoration trajectory. Unmanned aerial vehicles (UAVs) offer greatly improved rapidity and efficiency compared to traditional biodiversity monitoring surveys and are increasingly employed in the monitoring of ecological restoration. However, the applicability of UAV-based remote sensing in the identification of small features of interest from captured imagery (e.g., small individual plants, <100 cm²) remains untested and the potential of UAVs to track the performance of individual plants or the development of seedlings remains unexplored. This study utilised low-altitude UAV imagery from multi-sensor flights (Red-Green-Blue and multispectral sensors) and an automated object-based image analysis software to detect target seedlings from among a matrix of non-target grasses in order to track the performance of individual target seedlings and the seedling community over a 14-week period. Object-based Image Analysis (OBIA) classification effectively and accurately discriminated among target and non-target seedling objects and these groups exhibited distinct spectral signatures (six different visible-spectrum and multispectral indices) that responded differently over a 24-day drying period. OBIA classification from captured imagery also allowed for the accurate tracking of individual target seedling objects through time, clearly illustrating the capacity of UAV-based monitoring to undertake plant performance monitoring of individual plants at very fine spatial scales. Full article

(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)

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Review

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29 pages, 4622 KiB

Open AccessFeature PaperReview

A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data

by Iryna Dronova, Chippie Kislik, Zack Dinh and Maggi Kelly

Drones 2021, 5(2), 45; https://doi.org/10.3390/drones5020045 - 25 May 2021

Cited by 36 | Viewed by 8747

Abstract

Recent developments in technology and data processing for Unoccupied Aerial Vehicles (UAVs) have revolutionized the scope of ecosystem monitoring, providing novel pathways to fill the critical gap between limited-scope field surveys and limited-customization satellite and piloted aerial platforms. These advances are especially ground-breaking for supporting management, restoration, and conservation of landscapes with limited field access and vulnerable ecological systems, particularly wetlands. This study presents a scoping review of the current status and emerging opportunities in wetland UAV applications, with particular emphasis on ecosystem management goals and remaining research, technology, and data needs to even better support these goals in the future. Using 122 case studies from 29 countries, we discuss which wetland monitoring and management objectives are most served by this rapidly developing technology, and what workflows were employed to analyze these data. This review showcases many ways in which UAVs may help reduce or replace logistically demanding field surveys and can help improve the efficiency of UAV-based workflows to support longer-term monitoring in the face of wetland environmental challenges and management constraints. We also highlight several emerging trends in applications, technology, and data and offer insights into future needs. Full article

(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing)

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