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A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (31 October 2018) | Viewed by 151898

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Special Issue Editors

Dr. Ricardo Díaz-Delgado

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Guest Editor

CSIC—Estación Biológica de Doñana (EBD), Sevilla, Spain
Interests: multi and hyperspectral re-mote sensing for monitoring vegetation, wetlands and landscape changes; multitemporal analysis of time series of remote sensing images; predictive mapping of species habitat distribution; landscape dynamics and interactions with wildfire regimes; plant regeneration monitoring under different disturbance regimes; fire scar mapping and fire regime reconstruction; socio-ecological assessment and ecosystem services mapping
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Dr. C.A. Mücher

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Guest Editor

Wageningen Environmental Research (Alterra) Wageningen Campus, Building 101, Droevendaalsesteeg 3 P.O Box 47, 6700 AA Wageningen, The Netherlands
Interests: biodiversity; geographical information systems; landscape ecology; remote sensing; monitoring; land use; geoinformation; habitats

Special Issue Information

Dear Colleagues,

This Special Issue aims at collecting UAV applications contributing to a better understanding of biodiversity and ecosystem status, threats, changes and trends. We welcome submissions from purely scientific missions to operational management missions, evidencing the enhancement of knowledge in:

Essential Biodiversity Variables and Ecosystem Services mapping
Ecological Integrity parameters mapping
Long-term ecological monitoring based on UAVs
Mapping of alien species spread and distribution
Upscaling ecological variables from drone to satellite images: methods and approaches
Rapid risk and disturbance assessment using drones
Ecosystem structure and processes assessment by using UAVs
Mapping threats, vulnerability and conservation issues of biological communities and species
Mapping of phenological and temporal trends
Habitat mapping, monitoring and reporting of conservation status

Dr. Ricardo Díaz-Delgado
Dr. C.A. Mücher

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. Drones is an international peer-reviewed open access monthly 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 2600 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.

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Published Papers (11 papers)

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Editorial

Jump to: Research, Review

4 pages, 194 KiB

Open AccessEditorial

Editorial of Special Issue “Drones for Biodiversity Conservation and Ecological Monitoring”

by Ricardo Díaz-Delgado and Sander Mücher

Drones 2019, 3(2), 47; https://doi.org/10.3390/drones3020047 - 7 Jun 2019

Cited by 10 | Viewed by 10831

Abstract

Unmanned Aerial Vehicles (UAV) have already become an affordable and cost-efficient tool to quickly map a targeted area for many emerging applications in the arena of Ecological Monitoring and Biodiversity Conservation. Managers, owners, companies and scientists are using professional drones equipped with high-resolution visible, multispectral or thermal cameras to assess the state of ecosystems, the effect of disturbances, or the dynamics and changes of biological communities inter alia. It is now a defining time to assess the use of drones for these types of applications over natural areas and protected areas. UAV missions are increasing but most of them are just testing its applicability. It is time now to move to frequent revisiting missions, aiding in the retrieval of important biophysical parameters in ecosystems or mapping species distributions. This Special Issue is aimed at collecting UAV applications contributing to a better understanding of biodiversity and ecosystem status, threats, changes and trends. Submissions were welcomed from purely scientific missions to operational management missions, evidencing the enhancement of knowledge in: Essential biodiversity variables and ecosystem services mapping; ecological integrity parameters mapping; long-term ecological monitoring based on UAVs; mapping of alien species spread and distribution; upscaling ecological variables from drone to satellite images: methods and approaches; rapid risk and disturbance assessment using drones, ecosystem structure and processes assessment by using UAVs, mapping threats, vulnerability and conservation issues of biological communities and species; mapping of phenological and temporal trends and habitat mapping; monitoring and reporting of conservation status. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)

Research

Jump to: Editorial, Review

17 pages, 5100 KiB

Open AccessArticle

Calibrating Sentinel-2 Imagery with Multispectral UAV Derived Information to Quantify Damages in Mediterranean Rice Crops Caused by Western Swamphen (Porphyrio porphyrio)

by Magda Pla, Gerard Bota, Andrea Duane, Jaume Balagué, Antoni Curcó, Ricard Gutiérrez and Lluís Brotons

Drones 2019, 3(2), 45; https://doi.org/10.3390/drones3020045 - 21 May 2019

Cited by 20 | Viewed by 7115

Abstract

Making agricultural production compatible with the conservation of biological diversity is a priority in areas in which human–wildlife conflicts arise. The threatened Western Swamphen (Porphyrio porphyrio) feeds on rice, inducing crop damage and leading to decreases in rice production. Due to the Swamphen protection status, economic compensation policies have been put in place to compensate farmers for these damages, thus requiring an accurate, quantitative, and cost-effective evaluation of rice crop losses over large territories. We used information captured from a UAV (Unmanned Aerial Vehicle) equipped with a multispectral Parrot SEQUOIA camera as ground-truth information to calibrate Sentinel-2 imagery to quantify damages in the region of Ebro Delta, western Mediterranean. UAV vegetation index NDVI (Normalized Difference Vegetation Index) allowed estimation of damages in rice crops at 10 cm pixel resolution by discriminating no-green vegetation pixels. Once co-registered with Sentinel grid, we predicted the UAV damage proportion at a 10 m resolution as a function of Sentinel-2 NDVI, and then we extrapolated the fitted model to the whole Sentinel-2 Ebro Delta image. Finally, the damage predicted with Sentinel-2 data was quantified at the agricultural plot level and validated with field information compiled on the ground by Rangers Service. We found that Sentinel2-NDVI data explained up to 57% of damage reported with UAV. The final validation with Rangers Service data pointed out some limitations in our procedure that leads the way to improving future development. Sentinel2 imagery calibrated with UAV information proved to be a viable and cost-efficient alternative to quantify damages in rice crops at large scales. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)

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20 pages, 5816 KiB

Open AccessArticle

A UAV-Based Sensor System for Measuring Land Surface Albedo: Tested over a Boreal Peatland Ecosystem

by Francis Canisius, Shusen Wang, Holly Croft, Sylvain G. Leblanc, Hazen A. J. Russell, Jing Chen and Rong Wang

Drones 2019, 3(1), 27; https://doi.org/10.3390/drones3010027 - 16 Mar 2019

Cited by 22 | Viewed by 13400

Abstract

A multiple sensor payload for a multi-rotor based UAV platform was developed and tested for measuring land surface albedo and spectral measurements at user-defined spatial, temporal, and spectral resolutions. The system includes a Matrice 600 UAV with an RGB camera and a set of four downward pointing radiation sensors including a pyranometer, quantum sensor, and VIS and NIR spectrometers, measuring surface reflected radiation. A companion ground unit consisting of a second set of identical sensors simultaneously measure downwelling radiation. The reflected and downwelling radiation measured by the four sensors are used for calculating albedo for the total shortwave broadband, visible band and any narrowband at a 1.5 nm spectral resolution within the range of 350–1100 nm. The UAV-derived albedo was compared with those derived from Landsat 8 and Sentinel-2 satellite observations. Results show the agreement between total shortwave albedo from UAV pyranometer and Landsat 8 (R² = 0.73) and Sentinel-2 (R² = 0.68). Further, total shortwave albedo was estimated from spectral measurements and compared with the satellite-derived albedo. This UAV-based sensor system promises to provide high-resolution multi-sensors data acquisition. It also provides maximal flexibility for data collection at low cost with minimal atmosphere influence, minimal site disturbance, flexibility in measurement planning, and ease of access to study sites (e.g., wetlands) in contrast with traditional data collection methods. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)

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15 pages, 4677 KiB

Open AccessArticle

Enhancement of Ecological Field Experimental Research by Means of UAV Multispectral Sensing

by Ricardo Díaz-Delgado, Gábor Ónodi, György Kröel-Dulay and Miklós Kertész

Drones 2019, 3(1), 7; https://doi.org/10.3390/drones3010007 - 7 Jan 2019

Cited by 14 | Viewed by 6309

Abstract

Although many climate research experiments are providing valuable data, long-term measurements are not always affordable. In the last decades, several facilities have secured long-term experiments, but few studies have incorporated spatial and scale effects. Most of them have been implemented in experimental agricultural fields but none for ecological studies. Scale effects can be assessed using remote sensing images from space or airborne platforms. Unmanned aerial vehicles (UAVs) are contributing to an increased spatial resolution, as well as becoming the intermediate scale between ground measurements and satellite/airborne image data. In this paper we assess the applicability of UAV-borne multispectral images to provide complementary experimental data collected at point scale (field sampling) in a long-term rain manipulation experiment located at the Kiskun Long-Term Socio-Ecological Research (LTSER) site named ExDRain to assess the effects on grassland vegetation. Two multispectral sensors were compared at different scales, the Parrot Sequoia camera on board a UAV and the portable Cropscan spectroradiometer. The NDVI values were used to assess the effect of plastic roofs and a proportional reduction effect was found for Sequoia-derived NDVI values. Acceptable and significant positive relationships were found between both sensors at different scales, being stronger at Cropscan measurement scale. Differences found at plot scale might be due to heterogeneous responses to treatments. Spatial variability analysis pointed out a more homogeneous response for plots submitted to severe and moderate drought. More investigation is needed to address the possible effect of species abundance on NDVI at plot scale contributing to a more consistent representation of ground measurements. The feasibility of carrying out systematic UAV flights coincident or close to ground campaigns will certainly reveal the consistency of the observed spatial patterns in the long run. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)

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16 pages, 6596 KiB

Open AccessEditor’s ChoiceArticle

Greenness Indices from a Low-Cost UAV Imagery as Tools for Monitoring Post-Fire Forest Recovery

by Asier R. Larrinaga and Lluis Brotons

Drones 2019, 3(1), 6; https://doi.org/10.3390/drones3010006 - 6 Jan 2019

Cited by 61 | Viewed by 9406

Abstract

During recent years unmanned aerial vehicles (UAVs) have been increasingly used for research and application in both agriculture and forestry. Nevertheless, most of this work has been devoted to improving accuracy and explanatory power, often at the cost of usability and affordability. We tested a low-cost UAV and a simple workflow to apply four different greenness indices to the monitoring of pine (Pinus sylvestris and P. nigra) post-fire regeneration in a Mediterranean forest. We selected two sites and measured all pines within a pre-selected plot. Winter flights were carried out at each of the sites, at two flight heights (50 and 120 m). Automatically normalized images entered an structure from motion (SfM) based photogrammetric software for restitution, and the obtained point cloud and orthomosaic processed to get a canopy height model and four different greenness indices. The sum of pine diameter at breast height (DBH) was regressed on summary statistics of greenness indices and the canopy height model. Excess green index (ExGI) and green chromatic coordinate (GCC) index outperformed the visible atmospherically resistant index (VARI) and green red vegetation index (GRVI) in estimating pine DBH, while canopy height slightly improved the models. Flight height did not severely affect model performance. Our results show that low cost UAVs may improve forest monitoring after disturbance, even in those habitats and situations where resource limitation is an issue. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)

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12 pages, 3630 KiB

Open AccessEditor’s ChoiceArticle

Classification of Lowland Native Grassland Communities Using Hyperspectral Unmanned Aircraft System (UAS) Imagery in the Tasmanian Midlands

by Bethany Melville, Arko Lucieer and Jagannath Aryal

Drones 2019, 3(1), 5; https://doi.org/10.3390/drones3010005 - 5 Jan 2019

Cited by 32 | Viewed by 5581

Abstract

This paper presents the results of a study undertaken to classify lowland native grassland communities in the Tasmanian Midlands region. Data was collected using the 20 band hyperspectral snapshot PhotonFocus sensor mounted on an unmanned aerial vehicle. The spectral range of the sensor is 600 to 875 nm. Four vegetation classes were identified for analysis including Themeda triandra grassland, Wilsonia rotundifolia, Danthonia/Poa grassland, and Acacia dealbata. In addition to the hyperspectral UAS dataset, a Digital Surface Model (DSM) was derived using a structure-from-motion (SfM). Classification was undertaken using an object-based Random Forest (RF) classification model. Variable importance measures from the training model indicated that the DSM was the most significant variable. Key spectral variables included bands two (620.9 nm), four (651.1 nm), and 11 (763.2 nm) from the hyperspectral UAS imagery. Classification validation was performed using both the reference segments and the two transects. For the reference object validation, mean accuracies were between 70% and 72%. Classification accuracies based on the validation transects achieved a maximum overall classification accuracy of 93. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)

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20 pages, 6029 KiB

Open AccessArticle

Rapid Assessment of Ecological Integrity for LTER Wetland Sites by Using UAV Multispectral Mapping

by Ricardo Díaz-Delgado, Constantin Cazacu and Mihai Adamescu

Drones 2019, 3(1), 3; https://doi.org/10.3390/drones3010003 - 23 Dec 2018

Cited by 35 | Viewed by 10366

Abstract

Long-term ecological research (LTER) sites need a periodic assessment of the state of their ecosystems and services in order to monitor trends and prevent irreversible changes. The ecological integrity (EI) framework opens the door to evaluate any ecosystem in a comparable way, by measuring indicators on ecosystem structure and processes. Such an approach also allows to gauge the sustainability of conservation management actions in the case of protected areas. Remote sensing (RS), provided by satellite, airborne, or drone-borne sensors becomes a very synoptic and valuable tool to quickly map isolated and inaccessible areas such as wetlands. However, few RS practical indicators have been proposed to relate to EI indicators for wetlands. In this work, we suggest several RS wetlands indicators to be used for EI assessment in wetlands and specially to be applied with unmanned aerial vehicles (UAVs). We also assess the applicability of multispectral images captured by UAVs over two long-term socio-ecological research (LTSER) wetland sites to provide detailed mapping of inundation levels, water turbidity and depth as well as aquatic plant cover. We followed an empirical approach to find linear relationships between UAVs spectral reflectance and the RS indicators over the Doñana LTSER platform in SW Spain. The method assessment was carried out using ground-truth data collected in transects. The resulting empirical models were implemented for Doñana marshes and can be applied for the Braila LTSER platform in Romania. The resulting maps are a very valuable input to assess habitat diversity, wetlands dynamics, and ecosystem productivity as frequently as desired by managers or scientists. Finally, we also examined the feasibility to upscale the information obtained from the collected ground-truth data to satellite images from Sentinel-2 MSI using segments from the UAV multispectral orthomosaic. We found a close multispectral relationship between Parrot Sequoia and Sentinel-2 bands which made it possible to extend ground-truth to map inundation in satellite images. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)

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Graphical abstract

9 pages, 2192 KiB

Open AccessArticle

Estimating Wildlife Tag Location Errors from a VHF Receiver Mounted on a Drone

by André Desrochers, Junior A. Tremblay, Yves Aubry, Dominique Chabot, Paul Pace and David M. Bird

Drones 2018, 2(4), 44; https://doi.org/10.3390/drones2040044 - 11 Dec 2018

Cited by 15 | Viewed by 14505

Abstract

Recent studies have demonstrated the high potential of drones as tools to facilitate wildlife radio-tracking in rugged, difficult-to-access terrain. Without estimates of accuracy, however, data obtained from receivers attached to drones will be of limited use. We estimated transmitter location errors from a drone-borne VHF (very high frequency) receiver in a hilly and dense boreal forest in southern Québec, Canada. Transmitters and the drone-borne receiver were part of the Motus radio-tracking system, a collaborative network designed to study animal movements at local to continental scales. We placed five transmitters at fixed locations, 1–2 m above ground, and flew a quadrotor drone over them along linear segments, at distances to transmitters ranging from 20 m to 534 m. Signal strength was highest with transmitters with antennae pointing upwards, and lowest with transmitters with horizontal antennae. Based on drone positions with maximum signal strength, mean location error was 134 m (range 44–278 m, n = 17). Estimating peak signal strength against drone GPS coordinates with quadratic, least-squares regressions led to lower location error (mean = 94 m, range 15–275 m, n = 10) but with frequent loss of data due to statistical estimation problems. We conclude that accuracy in this system was insufficient for high-precision purposes such as finding nests. However, in the absence of a dense array of fixed receivers, the use of drone-borne Motus receivers may be a cost-effective way to augment the quantity and quality of data, relative to deploying personnel in difficult-to-access terrain. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)

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Graphical abstract

13 pages, 6839 KiB

Open AccessArticle

Drone Monitoring of Breeding Waterbird Populations: The Case of the Glossy Ibis

by Isabel Afán, Manuel Máñez and Ricardo Díaz-Delgado

Drones 2018, 2(4), 42; https://doi.org/10.3390/drones2040042 - 1 Dec 2018

Cited by 40 | Viewed by 8479

Abstract

Waterbird communities are potential indicators of ecological changes in threatened wetland ecosystems and consequently, a potential object of ecological monitoring programs. Waterbirds often breed in largely inaccessible colonies in flooded habitats, so unmanned aerial vehicle (UAV) surveys provide a robust method for estimating their breeding population size. Counts of breeding pairs might be carried out by manual and automated detection routines. In this study we surveyed the main breeding colony of Glossy ibis (Plegadis falcinellus) at the Doñana National Park. We obtained a high resolution image, in which the number and location of nests were determined manually through visual interpretation by an expert. We also suggest a standardized methodology for nest counts that would be repeatable across time for long-term monitoring censuses, through a supervised classification based primarily on the spectral properties of the image and a subsequent automatic size and form based count. Although manual and automatic count were largely similar in the total number of nests, accuracy between both methodologies was only 46.37%, with higher variability in shallow areas free of emergent vegetation than in areas dominated by tall macrophytes. We discuss the potential challenges for automatic counts in highly complex images. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)

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17 pages, 5945 KiB

Open AccessArticle

Assessment of Chimpanzee Nest Detectability in Drone-Acquired Images

by Noémie Bonnin, Alexander C. Van Andel, Jeffrey T. Kerby, Alex K. Piel, Lilian Pintea and Serge A. Wich

Drones 2018, 2(2), 17; https://doi.org/10.3390/drones2020017 - 23 Apr 2018

Cited by 44 | Viewed by 10895

Abstract

As with other species of great apes, chimpanzee numbers have declined over the past decades. Proper conservation of the remaining chimpanzees requires accurate and frequent data on their distribution and density. In Tanzania, 75% of the chimpanzees live at low densities on land outside national parks and little is known about their distribution, density, behavior or ecology. Given the sheer scale of chimpanzee distribution across western Tanzania (>20,000 km²), we need new methods that are time and cost efficient while providing precise and accurate data across broad spatial scales. Scientists have recently demonstrated the usefulness of drones for detecting wildlife, including apes. Whilst direct observation of chimpanzees is unlikely given their elusiveness, we investigated the potential of drones to detect chimpanzee nests in the Issa valley, western Tanzania. Between 2015 and 2016, we tested and compared the capabilities of two fixed-wing drones. We surveyed twenty-two plots (50 × 500 m) in gallery forests and miombo woodlands to compare nest observations from the ground with those from the air. We performed mixed-effects logistic regression models to evaluate the impact of image resolution, seasonality, vegetation type, nest height and color on nest detectability. An average of 10% of the nests spotted from the ground were detected from the air. From the factors tested, only image resolution significantly influenced nest detectability in drone-acquired images. We discuss the potential, but also the limitations, of this technology for determining chimpanzee distribution and density and to provide guidance for future investigations on the use of drones for ape population surveys. Combining traditional and novel technological methods of surveying allows more accurate collection of data on animal distribution and habitat connectivity that has important implications for ape conservation in an increasingly anthropogenically-disturbed landscape. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)

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Review

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23 pages, 849 KiB

Open AccessEditor’s ChoiceReview

Drones for Conservation in Protected Areas: Present and Future

by Jesús Jiménez López and Margarita Mulero-Pázmány

Drones 2019, 3(1), 10; https://doi.org/10.3390/drones3010010 - 9 Jan 2019

Cited by 194 | Viewed by 46263

Abstract

Park managers call for cost-effective and innovative solutions to handle a wide variety of environmental problems that threaten biodiversity in protected areas. Recently, drones have been called upon to revolutionize conservation and hold great potential to evolve and raise better-informed decisions to assist management. Despite great expectations, the benefits that drones could bring to foster effectiveness remain fundamentally unexplored. To address this gap, we performed a literature review about the use of drones in conservation. We selected a total of 256 studies, of which 99 were carried out in protected areas. We classified the studies in five distinct areas of applications: “wildlife monitoring and management”; “ecosystem monitoring”; “law enforcement”; “ecotourism”; and “environmental management and disaster response”. We also identified specific gaps and challenges that would allow for the expansion of critical research or monitoring. Our results support the evidence that drones hold merits to serve conservation actions and reinforce effective management, but multidisciplinary research must resolve the operational and analytical shortcomings that undermine the prospects for drones integration in protected areas. Full article

(This article belongs to the Special Issue Drones for Biodiversity Conservation and Ecological Monitoring)

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