Search

Gibbons are often difficult to observe in dense forest habitats using traditional ground-based methods. This makes it challenging to estimate group sizes and, in turn, population sizes. This has proven to be a key constraint on accurate monitoring of the last remaining population of the Critically Endangered cao vit gibbon Nomascus nasutus. However, new technologies are beginning to circumvent the problems associated with traditional methods. We hypothesized that, by using an unoccupied aerial vehicle (UAV) equipped with thermal and standard (RGB) cameras, we could obtain more accurate group size counts than by using ground-based observations, as fewer gibbons would be missed. We tested this during a population survey of the cao vit gibbon, finding that the thermal video footage revealed additional individuals that were not counted by ground-based surveyors. Statistically, there was strong evidence (93% probability) that UAV-derived counts were higher (by 41%) than concurrent ground-based counts. We recorded six primate groups of three species (cao vit gibbon, rhesus macaque Macaca mulatta and Assamese macaque Macaca assamensis), including 24 gibbons across four groups (c. 20% of the global population). The RGB video footage also revealed seven female gibbons, two of which were carrying infants, providing vital group composition data. These data have contributed directly to a more accurate population survey of the species than would have been possible using direct observation only. We anticipate more widespread use of UAVs in the study of gibbons and other threatened species, leading to a more robust evidence base for their conservation.

Gibbons are often difficult to observe in dense forest habitats using traditional ground-based methods. This makes it challenging to estimate group sizes and, in turn, population sizes. This has proven to be a key constraint on accurate monitoring of the last remaining population of the Critically Endangered cao vit gibbon Nomascus nasutus. However, new technologies are beginning to circumvent the problems associated with traditional methods. We hypothesized that, by using an unoccupied aerial vehicle (UAV) equipped with thermal and standard (RGB) cameras, we could obtain more accurate group size counts than by using ground-based observations, as fewer gibbons would be missed. We tested this during a population survey of the cao vit gibbon, finding that the thermal video footage revealed additional individuals that were not counted by ground-based surveyors. Statistically, there was strong evidence (93% probability) that UAV-derived counts were higher (by 41%) than concurrent ground-based counts. We recorded six primate groups of three species (cao vit gibbon, rhesus macaque Macaca mulatta and Assamese macaque Macaca assamensis), including 24 gibbons across four groups (c. 20% of the global population). The RGB video footage also revealed seven female gibbons, two of which were carrying infants, providing vital group composition data. These data have contributed directly to a more accurate population survey of the species than would have been possible using direct observation only. We anticipate more widespread use of UAVs in the study of gibbons and other threatened species, leading to a more robust evidence base for their conservation. [ABSTRACT FROM AUTHOR]

This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. This study was carried out on the Cávado estuary sand spit (Portugal). The performance of three sets of multispectral images with different Ground Sample Distances (GSDs) were compared: 2.5 cm, 5 cm, and 10 cm. The images were classified using the supervised classification algorithm random forest and later improved by applying a sieve filter. Samples of C. edulis were also collected, dried, and weighed to estimate the AGB using the relationship between the dry weight (DW) and vegetation indices (VIs). The resulting regression models were evaluated based on their coefficient of determination (R2), Normalised Root Mean Square Error (NRMSE), p-value, Akaike information criterion (AIC), and the Bayesian information criterion (BIC). The results show that the three tested image resolutions allow for constructing reliable coverage maps of C. edulis, with overall accuracy values of 89%, 85%, and 88% for the classification of the 2.5 cm, 5 cm, and 10 cm GSD images, respectively. The best-performing VI-DW regression models achieved R2 = 0.87 and NRMSE = 0.09 for the 2.5 cm resolution; R2 = 0.77 and NRMSE = 0.12 for the 5 cm resolution; and R2 = 0.64 and NRMSE = 0.15 for the 10 cm resolution. The C. edulis area and total AGB were 3441.10 m2 and 28,327.1 kg (with an AGB relative error (RE) = 0.08) for the 2.5 cm resolution; 3070.04 m2 and 29,170.8 kg (AGB RE = 0.08) for the 5 cm resolution; and 2305.06 m2 and 22,135.7 kg (AGB RE = 0.11) for the 10 cm resolution. Spatial and model differences were analysed in detail to determine their causes. Final analyses suggest that multispectral imagery of up to 5 cm GSD is adequate for estimating C. edulis distribution and biomass. [ABSTRACT FROM AUTHOR]

Abstract At active volcanoes, the surface temperature and its spatial distribution can indicate changes in the underlying magmatic and hydrothermal system. Surface temperature monitoring has been widely performed using thermal remote sensing with thermal infrared (TIR) cameras. One of the drawbacks of this method is that the unknown viewing orientation of TIR images inhibits quantitative evaluations of the spatial extent and distributions of thermal anomalies. Therefore, many studies have performed 3D temperature-field reconstructions by processing TIR images photogrammetrically. However, these studies have not included the correction of TIR wave atmospheric attenuation and viewing-angle variation in emissivity in the reconstruction procedure, which can result in significant temperature misestimation. We propose a simple method that incorporates the correction into the reconstruction process, which can improve the estimation of surface temperature, especially in rugged terrains. We demonstrate our method for the active crater of Aso volcano in Japan. We create digital elevation models by applying the Structure from Motion–Multi-view Stereo algorithm to aerial visible images taken at the same time as the TIR images and then project the TIR images onto the DEM with the direct georeferencing method. The correction is carried out using the geometric parameters acquired in the process. We create 1-m resolution orthorectified thermal images of the crater on two separate dates (18 August 2020 and 16 March 2022) and calculate the heat discharge rates from steaming grounds and a volcanic lake. We find that the heat discharge rate from the fumarolic field on the south crater wall showed a sevenfold increase after the phreatic explosions that occurred on 14 and 20 October 2021. Graphical Abstract

Wetland vegetation biomass is an essential indicator of wetland health, and its estimation has become an active area of research. Zizania latifolia (Z. latifolia) is the dominant species of emergent vegetation in Honghu Wetland, and monitoring its aboveground biomass (AGB) can provide a scientific basis for the protection and restoration of this and other wetlands along the Yangtze River. This study aimed to develop a method for the AGB estimation of Z. latifolia in Honghu Wetland using high-resolution RGB imagery acquired from an unoccupied aerial vehicle (UAV). The spatial distribution of Z. latifolia was first extracted through an object-based classification method using the field survey data and UAV RGB imagery. Linear, quadratic, exponential and back propagation neural network (BPNN) models were constructed based on 17 vegetation indices calculated from RGB images to invert the AGB. The results showed that: (1) The visible vegetation indices were significantly correlated with the AGB of Z. latifolia. The absolute value of the correlation coefficient between the AGB and CIVE was 0.87, followed by ExG (0.866) and COM2 (0.837). (2) Among the linear, quadratic, and exponential models, the quadric model based on CIVE had the highest inversion accuracy, with a validation R2 of 0.37, RMSE and MAE of 853.76 g/m2 and 671.28 g/m2, respectively. (3) The BPNN model constructed with eight factors correlated with the AGB had the best inversion effect, with a validation R2 of 0.68, RMSE and MAE of 732.88 g/m2 and 583.18 g/m2, respectively. Compared to the quadratic model constructed by CIVE, the BPNN model achieved better results, with a reduction of 120.88 g/m2 in RMSE and 88.10 g/m2 in MAE. This study indicates that using UAV-based RGB images and the BPNN model provides an effective and accurate technique for the AGB estimation of dominant wetland species, making it possible to efficiently and dynamically monitor wetland vegetation cost-effectively. [ABSTRACT FROM AUTHOR]

Drones have become an important research tool for studies of cetaceans, providing valuable insights into their ecology and behavior. However, drones are also recognized as a potential source of disturbance to cetaceans, particularly when flown at low altitudes. In this study, we examined the impact of drones on endangered St. Lawrence belugas (Delphinapterus leucas), and reviewed drone studies of cetaceans to identify altitude thresholds linked to disturbance. We repurposed drone footage of free‐living belugas taken at various altitudes, speeds, and angles‐of‐approach, and noted the animals' reactions. Evasive reactions to the drone occurred during 4.3% (22/511) of focal group follows. Belugas were more likely to display sudden dives during low‐altitude flights, particularly flights below 23 m. Sudden dives were also more likely to occur in larger groups and were especially common when a drone first approached a group. We recommend that researchers maintain a lower altitude limit of 25 m in drone‐assisted studies of belugas and approach larger groups with caution. This recommendation is in line with our literature review, which indicates that drone flights above 30 m are unlikely to provoke disturbance among cetaceans. [ABSTRACT FROM AUTHOR]

Field-based forest inventory plots are fundamental for many forest studies. These on-the-ground measurements of small samples of forested areas provide foresters with key information such as the size, abundance, health, and value of their forests. Recently, forest inventory plots have begun to be used as ground validation for tree features automatically extracted from remotely sensed data sets. Additionally, machine learning methods for feature extraction rely heavily on large quantities of training data and require these field forest inventory measurement datasets for algorithm training. Undermining the usefulness of forest inventory plot data as validation or training data is the positional uncertainty of plot location measurements. Because global navigation satellite systems (GNSS) cannot reliably measure plot center coordinates under thick tree canopy, plot center coordinates usually contain multiple meters of horizontal error. We present a method for reliably measuring plot center coordinates in which plot centers are individually marked with low-cost targets, allowing plot centers to be manually measured from orthoimagery captured during the leaf-off season. Our plot center measurements are shown to have less than 10 cm of horizontal error, an improvement of an order of magnitude over traditional GNSS methods. [ABSTRACT FROM AUTHOR]

At active volcanoes, the surface temperature and its spatial distribution can indicate changes in the underlying magmatic and hydrothermal system. Surface temperature monitoring has been widely performed using thermal remote sensing with thermal infrared (TIR) cameras. One of the drawbacks of this method is that the unknown viewing orientation of TIR images inhibits quantitative evaluations of the spatial extent and distributions of thermal anomalies. Therefore, many studies have performed 3D temperature-field reconstructions by processing TIR images photogrammetrically. However, these studies have not included the correction of TIR wave atmospheric attenuation and viewing-angle variation in emissivity in the reconstruction procedure, which can result in significant temperature misestimation. We propose a simple method that incorporates the correction into the reconstruction process, which can improve the estimation of surface temperature, especially in rugged terrains. We demonstrate our method for the active crater of Aso volcano in Japan. We create digital elevation models by applying the Structure from Motion–Multi-view Stereo algorithm to aerial visible images taken at the same time as the TIR images and then project the TIR images onto the DEM with the direct georeferencing method. The correction is carried out using the geometric parameters acquired in the process. We create 1-m resolution orthorectified thermal images of the crater on two separate dates (18 August 2020 and 16 March 2022) and calculate the heat discharge rates from steaming grounds and a volcanic lake. We find that the heat discharge rate from the fumarolic field on the south crater wall showed a sevenfold increase after the phreatic explosions that occurred on 14 and 20 October 2021. [ABSTRACT FROM AUTHOR]

This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. This study was carried out on the Cávado estuary sand spit (Portugal). The performance of three sets of multispectral images with different Ground Sample Distances (GSDs) were compared: 2.5 cm, 5 cm, and 10 cm. The images were classified using the supervised classification algorithm random forest and later improved by applying a sieve filter. Samples of C. edulis were also collected, dried, and weighed to estimate the AGB using the relationship between the dry weight (DW) and vegetation indices (VIs). The resulting regression models were evaluated based on their coefficient of determination (R2), Normalised Root Mean Square Error (NRMSE), p-value, Akaike information criterion (AIC), and the Bayesian information criterion (BIC). The results show that the three tested image resolutions allow for constructing reliable coverage maps of C. edulis, with overall accuracy values of 89%, 85%, and 88% for the classification of the 2.5 cm, 5 cm, and 10 cm GSD images, respectively. The best-performing VI-DW regression models achieved R2 = 0.87 and NRMSE = 0.09 for the 2.5 cm resolution; R2 = 0.77 and NRMSE = 0.12 for the 5 cm resolution; and R2 = 0.64 and NRMSE = 0.15 for the 10 cm resolution. The C. edulis area and total AGB were 3441.10 m2 and 28,327.1 kg (with an AGB relative error (RE) = 0.08) for the 2.5 cm resolution; 3070.04 m2 and 29,170.8 kg (AGB RE = 0.08) for the 5 cm resolution; and 2305.06 m2 and 22,135.7 kg (AGB RE = 0.11) for the 10 cm resolution. Spatial and model differences were analysed in detail to determine their causes. Final analyses suggest that multispectral imagery of up to 5 cm GSD is adequate for estimating C. edulis distribution and biomass.

The determination of key phenological growth stages of banana plantations, such as flower emergence and plant establishment, is difficult due to the asynchronous growth habit of banana plants. Identifying phenological events assists growers in determining plant maturity, and harvest timing and guides the application of time-specific crop inputs. Currently, phenological monitoring requires repeated manual observations of individual plants' growth stages, which is highly laborious, time-inefficient, and requires the handling and integration of large field-based data sets. The ability of growers to accurately forecast yield is also compounded by the asynchronous growth of banana plants. Satellite remote sensing has proved effective in monitoring spatial and temporal crop phenology in many broadacre crops. However, for banana crops, very high spatial and temporal resolution imagery is required to enable individual plant level monitoring. Unoccupied aerial vehicle (UAV)-based sensing technologies provide a cost-effective solution, with the potential to derive information on health, yield, and growth in a timely, consistent, and quantifiable manner. Our research explores the ability of UAV-derived data to track temporal phenological changes of individual banana plants from follower establishment to harvest. Individual plant crowns were delineated using object-based image analysis, with calculations of canopy height and canopy area producing strong correlations against corresponding ground-based measures of these parameters (R2 of 0.77 and 0.69 respectively). A temporal profile of canopy reflectance and plant morphology for 15 selected banana plants were derived from UAV-captured multispectral data over 21 UAV campaigns. The temporal profile was validated against ground-based determinations of key phenological growth stages. Derived measures of minimum plant height provided the strongest correlations to plant establishment and harvest, whilst interpolated maxima of normalised difference vegetation index (NDVI) best indicated flower emergence. For pre-harvest yield forecasting, the Enhanced Vegetation Index 2 provided the strongest relationship (R2 = 0.77) from imagery captured near flower emergence. These findings demonstrate that UAV-based multitemporal crop monitoring of individual banana plants can be used to determine key growing stages of banana plants and offer pre-harvest yield forecasts. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it requires large amounts of human‐labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine‐tuning this model with only 1000 local annotations increases these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed. [ABSTRACT FROM AUTHOR]

Remote sensing of forests has become increasingly accessible with the use of unoccupied aerial vehicles (UAV), along with deep learning, allowing for repeated high-resolution imagery and the capturing of phenological changes at larger spatial and temporal scales. In temperate forests during autumn, leaf senescence occurs when leaves change colour and drop. However, the influence of leaf senescence in temperate forests on tree species segmentation using a Convolutional Neural Network (CNN) has not yet been evaluated. Here, we acquired high-resolution UAV imagery over a temperate forest in Quebec, Canada on seven occasions between May and October 2021. We segmented and labelled 23,000 tree crowns from 14 different classes to train and validate a CNN for each imagery acquisition. The CNN-based segmentation showed the highest F1-score (0.72) at the start of leaf colouring in early September and the lowest F1-score (0.61) at peak fall colouring in early October. The timing of the events occurring during senescence, such as leaf colouring and leaf fall, varied substantially between and within species and according to environmental conditions, leading to higher variability in the remotely sensed signal. Deciduous and evergreen tree species that presented distinctive and less temporally-variable traits between individuals were better classified. While tree segmentation in a heterogenous forest remains challenging, UAV imagery and deep learning show high potential in mapping tree species. Our results from a temperate forest with strong leaf colour changes during autumn senescence show that the best performance for tree species segmentation occurs at the onset of this colour change. • Effect of leaf phenology on tree segmentation from drone imagery is not well known. • U-Net semantic segmentation yieled good tree-cover segmentation for most species. • The best performance was found at the onset of autumn colours. • Species with less heterogenous crowns between individuals were better segmented. • A dataset of 23,000 tree crown annotations over a growing season was generated. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Field Ornithology is the property of Resilience Alliance and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Kelp forests are commonly classified within remote sensing imagery by contrasting the high reflectance in the near-infrared spectral region of kelp canopy floating at the surface with the low reflectance in the same spectral region of water. However, kelp canopy is often submerged below the surface of the water, making it important to understand the effects of kelp submersion on the above-water reflectance of kelp, and the depth to which kelp can be detected, in order to reduce uncertainties around the kelp canopy area when mapping kelp. Here, we characterized changes to the above-water spectra of Nereocystis luetkeana (Bull kelp) as different canopy structures (bulb and blades) were submerged in water from the surface to 100 cm in 10 cm increments, while collecting above-water hyperspectral measurements with a spectroradiometer (325–1075 nm). The hyperspectral data were simulated into the multispectral bandwidths of the WorldView-3 satellite and the Micasense RedEdge-MX unoccupied aerial vehicle sensors and vegetation indices were calculated to compare detection limits of kelp with a focus on differences between red edge and near infrared indices. For kelp on the surface, near-infrared reflectance was higher than red-edge reflectance. Once submerged, the kelp spectra showed two narrow reflectance peaks in the red-edge and near-infrared wavelength ranges, and the red-edge peak was consistently higher than the near-infrared peak. As a result, kelp was detected deeper with vegetation indices calculated with a red-edge band versus those calculated with a near infrared band. Our results show that using red-edge bands increased detection of submerged kelp canopy, which may be beneficial for estimating kelp surface-canopy area and biomass. [ABSTRACT FROM AUTHOR]

The determination of key phenological growth stages of banana plantations, such as flower emergence and plant establishment, is difficult due to the asynchronous growth habit of banana plants. Identifying phenological events assists growers in determining plant maturity, and harvest timing and guides the application of time-specific crop inputs. Currently, phenological monitoring requires repeated manual observations of individual plants’ growth stages, which is highly laborious, time-inefficient, and requires the handling and integration of large field-based data sets. The ability of growers to accurately forecast yield is also compounded by the asynchronous growth of banana plants. Satellite remote sensing has proved effective in monitoring spatial and temporal crop phenology in many broadacre crops. However, for banana crops, very high spatial and temporal resolution imagery is required to enable individual plant level monitoring. Unoccupied aerial vehicle (UAV)-based sensing technologies provide a cost-effective solution, with the potential to derive information on health, yield, and growth in a timely, consistent, and quantifiable manner. Our research explores the ability of UAV-derived data to track temporal phenological changes of individual banana plants from follower establishment to harvest. Individual plant crowns were delineated using object-based image analysis, with calculations of canopy height and canopy area producing strong correlations against corresponding ground-based measures of these parameters (R2 of 0.77 and 0.69 respectively). A temporal profile of canopy reflectance and plant morphology for 15 selected banana plants were derived from UAV-captured multispectral data over 21 UAV campaigns. The temporal profile was validated against ground-based determinations of key phenological growth stages. Derived measures of minimum plant height provided the strongest correlations to plant establishment and harvest, whilst interpolated maxima of normalised difference vegetation index (NDVI) best indicated flower emergence. For pre-harvest yield forecasting, the Enhanced Vegetation Index 2 provided the strongest relationship (R2 = 0.77) from imagery captured near flower emergence. These findings demonstrate that UAV-based multitemporal crop monitoring of individual banana plants can be used to determine key growing stages of banana plants and offer pre-harvest yield forecasts.

The southern Beaufort coastline in Canada experiences significant storm surge events that are thought to play an important role in coastal erosion and influence permafrost dynamics. Unfortunately, many of these events have not been documented with tide gauge records. In this paper, we evaluate coastal driftwood accumulations as a proxy for estimating maximum storm surge heights and the history of these events. We use historical air photos and data derived from Unoccupied Aerial Vehicle (UAV) imagery to resurvey four coastal stranded driftwood study sites that were first appraised in 1985–86 and assess two new regional sites in the Mackenzie Delta. Maximum storm surge heights were found to be similar to observations carried out in the 1980s, however, we refine the elevations with more accuracy and reference these to a vertical datum appropriate for incorporating into sea level hazard assessments. Detailed mapping, historical air photo comparisons and the UAV acquired imagery at a site close to Tuktoyaktuk demonstrate that the highest storm surge at this site (1.98 m CGVD2013) occurred in association with a severe storm in 1970. This event shifted driftwood and floated material slightly upslope from an older event thought to occur in 1944 that reached 1.85 m (CGVD2013) elevation. The quality and accuracy of the high-resolution Digital Surface Model (DSM) and orthophoto derived from Structure from Motion (SfM) processing of the UAV photographs allowed mapping of four distinct stratigraphic units within the driftwood piles. Based on variations in anthropogenic debris composition, weathering characteristics and history of movement on aerial photographs, we conclude that no storm surge events at Tuktoyaktuk have exceeded ∼1.3 m (CGVD2013) since 1970. While there has been some speculation that ongoing climate change may lead to more frequent large magnitude storm surges along the Beaufort coast, our study and available tide gauge measurements, suggest that while moderate elevation storm surges may be more frequent in the past several decades, they have not approached the magnitude of the 1970 event.

Soil and water salinization has global impact on the sustainability of agricultural production, affecting the health and condition of staple crops and reducing potential yields. Identifying or developing salt-tolerant varieties of commercial crops is a potential pathway to enhance food and water security and deliver on the global demand for an increase in food supplies. Our study focuses on a phenotyping experiment that was designed to establish the influence of salinity stress on a diversity panel of the wild tomato species, Solanum pimpinellifolium. Here, we explore how unoccupied aerial vehicles (UAVs) equipped with both an optical and thermal infrared camera can be used to map and monitor plant temperature (Tp) changes in response to applied salinity stress. An object-based image analysis approach was developed to delineate individual tomato plants, while a green–red vegetation index derived from calibrated red, green, and blue (RGB) optical data allowed the discrimination of vegetation from the soil background. Tp was retrieved simultaneously from the co-mounted thermal camera, with Tp deviation from the ambient temperature and its change across time used as a potential indication of stress. Results showed that Tp differences between salt-treated and control plants were detectable across the five separate UAV campaigns undertaken during the field experiment. Using a simple statistical approach, we show that crop water stress index values greater than 0.36 indicated conditions of plant stress. The optimum period to collect UAV-based Tp for identifying plant stress was found between fruit formation and ripening. Preliminary results also indicate that UAV-based Tp may be used to detect plant stress before it is visually apparent, although further research with more frequent image collections and field observations is required. Our findings provide a tool to accelerate field phenotyping to identify salt-resistant germplasm and may allow farmers to alleviate yield losses through early detection of plant stress via management interventions. [ABSTRACT FROM AUTHOR]

Soil and water salinization has global impact on the sustainability of agricultural production, affecting the health and condition of staple crops and reducing potential yields. Identifying or developing salt-tolerant varieties of commercial crops is a potential pathway to enhance food and water security and deliver on the global demand for an increase in food supplies. Our study focuses on a phenotyping experiment that was designed to establish the influence of salinity stress on a diversity panel of the wild tomato species, Solanum pimpinellifolium. Here, we explore how unoccupied aerial vehicles (UAVs) equipped with both an optical and thermal infrared camera can be used to map and monitor plant temperature (Tp) changes in response to applied salinity stress. An object-based image analysis approach was developed to delineate individual tomato plants, while a green–red vegetation index derived from calibrated red, green, and blue (RGB) optical data allowed the discrimination of vegetation from the soil background. Tp was retrieved simultaneously from the co-mounted thermal camera, with Tp deviation from the ambient temperature and its change across time used as a potential indication of stress. Results showed that Tp differences between salt-treated and control plants were detectable across the five separate UAV campaigns undertaken during the field experiment. Using a simple statistical approach, we show that crop water stress index values greater than 0.36 indicated conditions of plant stress. The optimum period to collect UAV-based Tp for identifying plant stress was found between fruit formation and ripening. Preliminary results also indicate that UAV-based Tp may be used to detect plant stress before it is visually apparent, although further research with more frequent image collections and field observations is required. Our findings provide a tool to accelerate field phenotyping to identify salt-resistant germplasm and may allow farmers to alleviate yield losses through early detection of plant stress via management interventions.

Many remote islands present barriers to effective wildlife monitoring in terms of challenging terrain and frequency of visits. The sub-Antarctic islands of South Georgia and the South Sandwich Islands are home to globally significant populations of seabirds and marine mammals. South Georgia hosts the largest breeding populations of Antarctic fur seals, southern elephant seals and king penguins as well as significant populations of wandering, black-browed and grey-headed albatross. The island also holds important populations of macaroni and gentoo penguins. The South Sandwich Islands host the world’s largest colony of chinstrap penguins in addition to major populations of Adélie and macaroni penguins. A marine protected area was created around these islands in 2012 but monitoring populations of marine predators remains a challenge, particularly as these species breed over large areas in remote and often inaccessible locations. During the 2019/20 austral summer, we trialled the use of an unoccupied aerial vehicle (UAV; drone) to monitor populations of seals, penguins and albatross and here we report our initial findings, including considerations about the advantages and limitations of the methodology. Three extensive southern elephant seal breeding sites were surveyed with complete counts made around the peak pupping date, two of these sites were last surveyed 24 years ago. A total of nine islands, historically recorded as breeding sites for wandering albatross, were surveyed with 144 fledglings and 48 adults identified from the aerial imagery. The UAV was effective at surveying populations of penguins that nest on flat, open terrain, such as Adélie and chinstrap penguin colonies at the South Sandwich Islands, and an extensive king penguin colony on South Georgia, but proved ineffective for monitoring macaroni penguins nesting in tussock habitat on South Georgia as individuals were obscured or hidden by vegetation. Overall, we show that UAV surveys can allow regular and accurate monitoring of these important wildlife populations.

Colonial nesting of both single and mixed-species assemblages is common across avian taxa. Coloniality may provide fitness advantages such as reduced nest predation through predator swamping, early warning of predators, or nest stability, or could simply arise through individuals selecting similar high-quality habitats. Traditionally, studies of nest dispersion in marsh-nesting colonial birds have required the use of on-the-ground methods due to the difficulty of accessing nesting areas and the cryptic nature of nests. Here, I make use of drone-borne thermal and visible sensors to capture imagery of a large sample of nests (~1,700) at single and mixed-species colonies of two grebe species and one gull species nesting at four marshes in Saskatchewan, Canada. As predicted, species exhibited significant intraspecific nest clustering, with individuals nesting closer together than expected by chance more often than not (in nine of 13 species-subcolony comparisons, where subcolonies were delineated based on aggregations of nests within marshes). In contrast, grebe species rarely nested closer to Franklin's Gulls (Leucophaeus pipixcan) than expected by chance, contrary to theoretical predictions based on presumed advantages of anti-predator defense or nest stability from gulls. As predicted, the larger (Western Grebe, Aechmophorus occidentalis) and more aggressive species (Franklin's Gull) had greater nearest neighbor distances than the smallest species (Eared Grebe, Podiceps nigricollis). Nearest neighbor distances tended to be smaller at subcolonies with greater emergent vegetation cover and at larger subcolonies, which may be due to reduced visibility allowing for smaller distances between neighbors, and social attraction at larger subcolonies, respectively. While valuable from a population monitoring perspective, this study demonstrates that high-resolution imagery from drones can also aid in answering ecological questions, and in combination with detailed nest success information, could help identify habitat needs and potential factors limiting reproductive success of declining and sensitive marsh species.

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.

Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial (

Seamless bathymetric and topographic digital elevation models (SBT-DEM) of coastal zones are important for modeling tsunami propagation and coastal inundation, monitoring coastal erosion and deposition, and modeling hydrodynamics and sediment transport. Fine-scale SBT-DEM construction has been elusive due to the difficulty of accurately measuring shallow water depths with conventional techniques. We attempt to perform coastal bathymetric and topographic surveys using an SBES-equipped unoccupied surface vehicle (USV) and a LiDAR-equipped unoccupied aerial vehicle (UAV), and construct a fine-scale SBT-DEM using GIS (Geographic Information System) techniques. The results show that the proposed spatiotemporal divisional measurement method can seamlessly collect coastal topographic and bathymetric data using tidal range. The improved Blend mosaic algorithm not only seamlessly and smoothly stitches the land and seabed DEMs, but also minimizes the loss of elevation accuracy. The fine-scale SBT-DEM constructed in this paper has the advantages of high resolution (Pixel size = 1 m) and high accuracy (RMSE = 6.2 cm). Fine-scale SBT-DEM can provide integrated topographic and bathymetric data for coastal dynamic monitoring and environmental management. • A novel method for fine-scale SBT-DEM construction was proposed. • The first combined UAV LiDAR and USV SBES for coastal SBT-DEM construction. • This method can quickly and accurately generate seamless and high-resolution SBT-DEM. • Fine-scale SBT-DEM provides new data support for coastal dynamics studies. [ABSTRACT FROM AUTHOR]

Accurately quantifying tree and forest structure is important for monitoring and understanding terrestrial ecosystem functioning in a changing climate. The emergence of laser scanning, such as Terrestrial Laser Scanning (TLS) and Unoccupied Aerial Vehicle Laser Scanning (UAV-LS), has advanced accurate and detailed forest structural measurements. TLS generally provides very accurate measurements on the plot-scale (a few ha), whereas UAV-LS provides comparable measurements on the landscape-scale (>10 ha). Despite the pivotal role dense tropical forests play in our climate, the strengths and limitations of TLS and UAV-LS to accurately measure structural metrics in these forests remain largely unexplored. Here, we propose to combine TLS and UAV-LS data from dense tropical forest plots to analyse how this fusion can further advance 3D structural mapping of structurally complex forests. We compared stand (vertical point distribution profiles) and tree level metrics from TLS, UAV-LS as well as their fused point cloud. The tree level metrics included the diameter at breast height (DBH), tree height (H), crown projection area (CPA), and crown volume (CV). Furthermore, we evaluated the impact of point density and number of returns for UAV-LS data acquisition. DBH measurements from TLS and UAV-LS were compared to census data. The TLS and UAV-LS based H, CPA and CV measurements were compared to those obtained from the fused point cloud. Our results for two tropical rainforest plots in Australia demonstrate that TLS can measure H, CPA and CV with an accuracy (RMSE) of 0.30 m (H average =27.32 m), 3.06 m2 (CPA average =66.74 m2), and 29.63 m3 (CV average =318.81 m3) respectively. UAV-LS measures H, CPA and CV with an accuracy (RMSE) of <0.40 m, <5.50 m2, and <30.33 m3 respectively. However, in dense tropical forests single flight UAV-LS is unable to sample the tree stems sufficiently for DBH measurement due to a limited penetration of the canopy. TLS can determine DBH with an accuracy (RMSE) of 5.04 cm, (DBH average =45.08 cm), whereas UAV-LS can not. We show that in dense tropical forests stand-alone TLS is able to measure macroscopic structural tree metrics on plot-scale. We also show that UAV-LS can be used to quickly measure H, CPA, and CV of canopy trees on the landscape-scale with comparable accuracy to TLS. Hence, the fusion of TLS and UAV-LS, which can be time consuming and expensive, is not required for these purposes. However, TLS and UAV-LS fusion opens up new avenues to improve stand-alone UAV-LS structural measurements at the landscape-scale by applying TLS as a local calibration tool. • TLS can measures the DBH, tree height and crown metrics in tropical rainforests. • UAV-LS has great potential for monitoring of tree height and crown metrics. • TLS and UAV-LS fusion opens up avenues for landscape-scale structure calibration. [ABSTRACT FROM AUTHOR]

• A new image-based method for classifying lodging severity of field plots proposed. • Texture and height features extracted from maps generated from UAV images. • Height feature outperformed texture feature in lodging severity classification. • "No" and "severe" lodging plots were better classified than "medium" lodging plots. • Height feature yielded constant accuracy with ground sample distance. Lodging is a major limiting factor for the yield, quality and harvesting efficiency of selected crops worldwide. This study presents an efficient, robust and non-destructive assessment of lodging severity for four different grasses for seed production, using images collected by an unoccupied aerial vehicle (UAV) in two field plot experiments across five growing seasons. Canopy texture and height related features were extracted from individual plot images and evaluated for estimating lodging severity. Histograms of oriented gradients (HOG) were used as texture features, and three canopy height distributions features (CHV1, CHV2 and CHV3) were proposed. Each canopy height distribution feature divides the plots into subplots and estimates the average height of each subplot. CHV1 concatenates average height of the subplots into its feature, while CHV2 concatenates the difference in average height between all subplots, and CHV3 concatenates the difference in average height between adjacent subplots. The plots were classified using support vector machines into three categories according to the lodging severity. The results showed that the HOG and height distribution features can be used for grading lodging severity in UAV images with high accuracy (71.9% and 79.1%, respectively). However, the HOG features showed a negative relationship to the ground sample distance (GSD), while the CHV1 had a constant accuracy across the GSDs. Combination of the two features did not significantly improve the classification accuracy. The present results have potential to generate lodging severity maps for application in precision farming and thereby to increase grass seed yield and harvest efficiency at farm scale. It should be noted that results and methods from the current study might not be transferred to other crops due to crop specific lodging characteristics and effect of yields. [ABSTRACT FROM AUTHOR]

Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial (<1 m) and temporal (on-demand) resolution data facilitates monitoring of individual plants over time and can provide essential information about health, yield, and growth in a timely and quantifiable manner. Such applications would be beneficial for cropped banana plants due to their distinctive growth characteristics. Limited studies have employed UAV data for mapping banana crops and to our knowledge only one other investigation features multi-temporal detection of banana crowns. The purpose of this study was to determine the suitability of multiple-date UAV-captured multi-spectral data for the automated detection of individual plants using convolutional neural network (CNN), template matching (TM), and local maximum filter (LMF) methods in a geographic object-based image analysis (GEOBIA) software framework coupled with basic classification refinement. The results indicate that CNN returns the highest plant detection accuracies, with the developed rule set and model providing greater transferability between dates (F-score ranging between 0.93 and 0.85) than TM (0.86–0.74) and LMF (0.86–0.73) approaches. The findings provide a foundation for UAV-based individual banana plant counting and crop monitoring, which may be used for precision agricultural applications to monitor health, estimate yield, and to inform on fertilizer, pesticide, and other input requirements for optimized farm management. [ABSTRACT FROM AUTHOR]