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Drone remote sensing of individual trees. (A) Ultra-high-density drone lidar resolves individual tree structure in a temperate beech forest in the southern Czech Republic. Colors indicate elevation, and the tallest trees are about 40 m aboveground. Measurement density here is 4323 points per square meter. (B) High-spatial resolution optical remote sensing from a low-altitude drone in the Atlantic lowlands of Costa Rica. We used methods from computer vision to construct three-dimensional scene geometry from two-dimensional images. The image is a natural color composite. (C) Same area as B, but colored by surface elevation, where warmer colors indicate taller objects. A single Goethalsia meiantha crown is outlined in white. The area of this crown is 157.3 m 2 . At a pixel size of 1 cm, this crown contains 1.573 × 10 6 pixels, demonstrating the tremendous increase in measurement density at high-spatial resolution. Scale bar in B and C = 30 m.
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... less than a traditional airborne program, which makes it possible to acquire measurements frequently, on demand. Lidar sensors record the return-time of emitted laser pulses to produce a physically accurate three-dimensional point cloud (Disney, 2019). Measurement densities from drone lidar are easily in the thousands of points per square meter (Fig. 1). This important distinction has allowed highdensity point clouds from drones to resolve branch and stem structure within individual trees, and to associate individual plants with remote sensing data ( Brede et al., 2017;Trochta et al., ...
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... sensors on drones can produce pixels at the leaf level, resulting in many thousands of measurements within single trees from imaging spectrometers, multispectral cameras, and traditional digital cameras (Fig. 1). Increasing the density of measurements vastly increases the information content in remote sensing ...
Citations
... Phases Reduction days per decade SA Phases Reduction days per decade AM Phases Reduction days per decade SM productivity and C4 crops like maize showing a 5% to 10% increase in productivity(Kellner et al., 2019; Pastore et al., 2019).Elevated levels of carbon dioxide in wheat crops have been found to accelerate their growth and development, leading to earlier flowering and maturity(Padhan et al., 2019;Maphosa et al., 2019; Yang et al., 2007a). Wheat crops grown under elevated carbon dioxide conditions have been found to head 1.2 days earlier and reach flag leaf senescence 3.5 days earlier than those grown under ambient carbon dioxide levels(Maphosa et al., 2019). ...
The purpose of this systematic review was to investigate the impact of temperature and carbon dioxide changes on wheat crop phenology and nutritional content at different growth stages. The findings showed that rising temperatures can advance or delay phenological phases of the wheat crop, and increased carbon dioxide can result in earlier or later flowering, elongation, and maturity. Climate change and fluctuation can affect the balance of wheat carbon metabolism, mineral intake, and nutrient utilization, leading to a decline in key minerals such as iron, magnesium, manganese, phosphorus, sulfur, and zinc. As a result, crop yield and mineral content are both affected by climate variability, and it is recommended to grow wheat varieties with distinct adaptation strategies for different climatic conditions.
... Forecasting the future of tropical forests is challenging because little is known about the ways different species will respond to changing climate, or the resilience provided by that diversity (Fisher et al., 2018;Gallup et al., 2021;Koven et al., 2020;Restrepo-Coupe et al., 2021). To understand the likely responses of forests to further climate change, ecosystem models need to represent growth and mortality processes of individual trees more accurately than is currently the case (Kellner et al., 2019;Piponiot et al., 2022;Zuidema & van der Sleen, 2022). ...
Tropical forests are a major component of the global carbon cycle and home to two‐thirds of terrestrial species. Upper‐canopy trees store the majority of forest carbon and can be vulnerable to drought events and storms. Monitoring their growth and mortality is essential to understanding forest resilience to climate change, but in the context of forest carbon storage, large trees are underrepresented in traditional field surveys, so estimates are poorly constrained. Aerial photographs provide spectral and textural information to discriminate between tree crowns in diverse, complex tropical canopies, potentially opening the door to landscape monitoring of large trees. Here we describe a new deep convolutional neural network method, Detectree2 , which builds on the Mask R‐CNN computer vision framework to recognize the irregular edges of individual tree crowns from airborne RGB imagery. We trained and evaluated this model with 3797 manually delineated tree crowns at three sites in Malaysian Borneo and one site in French Guiana. As an example application, we combined the delineations with repeat lidar surveys (taken between 3 and 6 years apart) of the four sites to estimate the growth and mortality of upper‐canopy trees. Detectree2 delineated 65 000 upper‐canopy trees across 14 km ² of aerial images. The skill of the automatic method in delineating unseen test trees was good ( F 1 score = 0.64) and for the tallest category of trees was excellent ( F 1 score = 0.74). As predicted from previous field studies, we found that growth rate declined with tree height and tall trees had higher mortality rates than intermediate‐size trees. Our approach demonstrates that deep learning methods can automatically segment trees in widely accessible RGB imagery. This tool (provided as an open‐source Python package) has many potential applications in forest ecology and conservation, from estimating carbon stocks to monitoring forest phenology and restoration.
Python package available to install at https://github.com/PatBall1/Detectree2 .
... Recently, the increasing number of SIF-capable satellite missions (Mohammed et al., 2019) has been accompanied with the rapid expansion of tower-based long-term SIF measurements (Magney et al., 2019a;Dechant et al., 2020;Martini et al., 2022), and progress in airborne and proximal SIF imaging systems (Rascher et al., 2015;Siegmann et al., 2019;Porcar-Castell et al., 2021). Such platforms provide unique sources of data to investigate and model the dynamics of photosynthesis at multiple spatial and temporal scales, moving from the broad brush of ecosystem scale satellite observations to the scale of individual plants and below (Kellner et al., 2019;Porcar-Castell et al., 2021). ...
... Characterizing the factors that drive the spatial and temporal variation of leaf-level spectral ChlF across an ecosystem is not only relevant to satellite SIF studies which produce spatially coarse, species and light environment aggregated, retrievals of ChlF and resultant photosynthetic phenology (Walther et al., 2016;Magney et al., 2019a); but will be particularly relevant to studies based on higher spatial resolution SIF data from emerging imaging systems which operate closer to the canopy and have the ability to resolve individual plants and their parts (Kellner et al., 2019;Porcar-Castell et al., 2021). In addition to the influence of canopy structure and measurement geometry (Liu et al., 2019;Dechant et al., 2020;Regaieg et al., 2021), dynamics in leaf-level traits beyond photosynthesis, can also influence the interpretation of spectral ChlF. ...
... The increase in yield is formed due to the accuracy of processing, the absence of overlap during the processing of plant protection chemicals. In addition, according to scientific research, 3-6% of the entire sown area of the field perishes under the wheels of wheeled vehicles [31][32][33][34]. ...
The article deals with the issue of economic efficiency of the use of drones in agricultural production. There is an opinion about their inefficiency, which is refuted by the study. The purpose of the study is to determine the effectiveness of the use of agricultural drones (using the example of U-30L-6 (BROUAV) in comparison with other technological options. The use of agricultural drones allows not only to reduce the cost of manufactured products, but also to increase crop yields by reducing losses during cultivation, as the number of passes of wheeled vehicles across the field during the growing season is reduced. Among the options considered (trailed sprayer, self-propelled sprayer, agrodrone), the use of copters took the second place in terms of production costs. But due to a decrease in the spraying rate and losses from trampling, the economic effect of using agricultural drones is the highest (3417.34 rubles/ha), which is more than twice as high as when using a self-propelled sprayer.
... For example, for trees with tall individuals, long growth spans, and complex genome sequences and habitats, large-scale phenotypic trait monitoring is difficult and expensive, which affects the rapid development of the forest breeding industry [22,23]. Unmanned aircraft remote sensing technology can gather high-resolution, multi-spectral images at a low altitude, and it can not only obtain extensive forest condition data in a timely and nondestructive manner, but it can also improve the fineness to the structure of the branches and leaves, and the reflective spectrum within an individual tree canopy, thereby increasing the accuracy of the information extraction and the efficiency of the data acquisition [24,25]. In recent years, with the development of artificial intelligence and computer vision algorithms, UAV remote sensing has been gradually applied to the inversion of the forest structure and physicochemical parameters as well as the forest biomass [26,27], the classification of dominant forest species [28,29] and the dynamic monitoring of forest resources [30]. ...
The quantitative, accurate and efficient acquisition of tree phenotypes is the basis for forest “gene-phenotype-environment” studies. It also offers significant support for clarifying the genetic control mechanisms of tree traits. The application of unmanned aerial vehicle (UAV) remote sensing technology to the collection of phenotypic traits at an individual tree level quantitatively analyses tree phenology and directionally evaluates tree growth, as well as accelerating the process of forest genetics and breeding. In this study, with the help of high-resolution, high-overlap, multispectral images obtained by an UAV, combined with digital elevation models (DEMs) extracted from point clouds acquired by a backpack LiDAR, a high-throughput tree structure and spectral phenotypic traits extraction and a genetic selection were conducted in a trial of Eucalyptus clones in the State-owned Dongmen Forest Farm in the Guangxi Zhuang Autonomous Region. Firstly, we validated the accuracy of extracting the phenotypic parameters of individual tree growth based on aerial stereo photogrammetry point clouds. Secondly, on this basis, the repeatability of the tree growth traits and vegetation indices (VIs), the genetic correlation coefficients between the traits were calculated. Finally, the eucalypt clones were ranked by integrating a selection index of traits, and the superior genotypes were selected and their genetic gain predicted. The results showed a high accuracy of the tree height (H) extracted from the digital aerial photogrammetry (DAP) point cloud based on UAV images (R2 = 0.91, and RMSE = 0.56 m), and the accuracy of estimating the diameter at breast height (DBH) was R2 = 0.71, and RMSE = 0.75 cm. All the extracted traits were significantly different within the tree species and among the clones. Except for the crown width (CW), the clonal repeatability ( of the traits were all above 0.9, and the individual repeatability values ( were all above 0.5. The genetic correlation coefficient between the tree growth traits and VIs fluctuated from 0.3 to 0.5, while the best clones were EA14-15, EA14-09, EC184, and EC183 when the selection proportion was 10%. The purpose of this study was to construct a technical framework for phenotypic traits extraction and genetic analysis of trees based on unmanned aerial stereo photography point clouds and high-resolution multispectral images, while also exploring the application potential of this approach in the selective breeding of eucalypt clones.
... Even in the comparatively well-studied system of temperate broadleaf deciduous forests, this scarcity of data continues to hinder our ability to test models and theories of coordinated leaf-and crown-level responses to environmental conditions (Hikosaka et al. 2016). Nevertheless, recent advances in embedded sensors and imaging capabilities, particularly from unoccupied aerial vehicles (UAVs) (McNeil et al. 2016) and lidar (Kellner et al. 2019), now provide rich data sources that can begin to approach the ideal of creating a dynamic, high-fidelity digital twin of a tree's architecture and functioning. By extending and enhancing crown architectural observations made across the globe for decades (and indeed centuries, in some cases) by natural historians and ecologists (see eg Horn 1971;Pastor 2016), recent advances in technology can help to identify a more formal, functional trait-based theory of crown economics. ...
Trees respond to global change in myriad ways, many of which may be linked to adaptations relating to tree crown architecture. However, there is a paucity of theory capable of predicting the adaptive importance and dynamics of crown architecture, most likely because of the difficulties involved in measuring the three‐dimensional arrangement and orientation of tree leaves within individual crowns. Here, we describe a theory of tree crown economics, and use measurements from new lidar (light detection and ranging) instruments, UAVs (unoccupied aerial vehicles), and time‐lapse camera imagery to identify support for two predictions of the theory, that (1) a light competition versus water use economic trade‐off drives covariance among three tree crown functional traits (mean leaf angle, crown density, and crown rugosity), and (2) crown traits can drive spatial and temporal variability in near‐infrared spectral reflectance and related ecosystem functions. Tree crown economic theory can complement leaf economic theory in helping ecologists map and model forest ecosystem responses to global change.
... The advancement in remote sensing platforms and sensors (Box 1) over the last decade has provided unprecedented insight into our natural world. An increasing number of studies are beginning to link remote sensing data to ecological processes, including detecting changes in species boundaries (Ma et al. 2019), the decline in ecosystem health (Sun et al. 2016), the development of new global models of plant growth and productivity (Coops 2015) and the estimation of functional traits at the individual tree level (Kellner et al. 2019). The use of remote sensing in ecological restoration is emerging (Cordell et al. 2017;Buters et al. 2019). ...
The benefits of using remote sensing technologies for informing and monitoring ecological restoration of forests from the community to the individual are presented. At the community level, we link remotely sensed measures of structural complexity with animal behaviour. At the plot level, we monitor the return of vegetation structure and ecosystem services (e.g. carbon sequestration) using data-rich three-dimensional point clouds. At the individual-level, we use high-resolution images to accurately classify plants to species and provenance and show genetic-based variation in canopy structural traits. To facilitate the wider use of remote sensing in restoration, we discuss the challenges that remain to be resolved.
... For a method to be adopted in precision agriculture workflows, it needs to be farmer-friendly and as straightforward as possible (Cohen et al., 2017). Based on the reviewed literature, there is currently a significant knowledge gap and disconnect between obtaining and extracting UAV-based TIR information and then ensuring this information can be translated into meaningful biological understanding at the individual plant scale (Kellner et al., 2019). Our research presents an approach for retrieving T p from UAV-based TIR and RGB imagery, with an experimental focus on a diversity panel of tomato plants undergoing dripirrigation in both control and salt water conditions. ...
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.
... This dataset of Handroanthus guayacan was later used to test whether adult recruitment of H. guayacan was negatively density dependent 19 . In the Peruvian and Colombian Amazon, thousands of flowering individuals of yellow and pink crowns with synchronous flowering, that could be potentially for the studies of population dynamics, have been already observed in high-resolution images from the Planet Labs constellation of cube-sats and remained to be mapped 20 . ...
Mapping the spatial distribution of a plant is a current challenge in ecology. Here, a convolutional neural network (CNN) and 33,798 Sentinel-2 satellite images were used to detect and map forest stands dominated by trees of the genus Pleroma by their magenta-to-deep-purple blossoms in the entire Brazilian Atlantic Forest domain, from June 2016 to July 2020. The Pleroma genus, known for its pioneer behaviour, was detected in an area representing 10.8% of the Atlantic Forest, associated negatively with temperature and positively with elevation, slope, tree cover and precipitation. The detection of another genus by the model, 18% of all the detections contained only pink blooming Handroanthus trees, highlighted that botanical identification from space must be taken with caution, particularly outside the known distribution range of the species. The Pleroma blossom seasonality occurred over a period of ~5–6 months centered on the March equinox and populations with distinct blossom timings were found. Our results indicate that in the Atlantic Forest, the remaining natural forest is less diverse than expected but is at least recovering from degradation. Our study suggests a method to produce ecological-domain scale maps of tree genera and species based on their blossoms that could be used for tree studies and biodiversity assessments.
... However, because of a lack of available data or as a result of concerns that such a level of detail will be neither generalizable nor scalable, very few studies attempted to derive a comprehensive metric for species trait variation or to discern such variation from uncertainties (Cavender-Bares et al., 2016;Č epl et al., 2018;Czyż et al., 2020;García-Verdugo et al., 2010;Madritch et al., 2014;Pettorelli et al., 2016;Santiso and Retuerto, 2015;Singh et al., 2015). And yet, remote sensing of individuals made possible in recent years by the combination of highresolution imaging spectroscopy and LiDAR illustrates the potential of remote sensing to capture species traits (Kellner et al., 2019). The individual-level mapping of morphological and physiological traits (Ali et al., 2017;Zheng et al., 2021), or even the monitoring of phenological events as forest flowering from space (Dixon et al., 2021) resulting from this approach are great examples. ...
The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica. We first use fabrics as standard material to quantify measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over one growing season (2.7 ± 1.7%), or between individuals sampled over one week (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual at the time of the measurement. However, measurements of variation within individuals at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches toward a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance.
