Forest-Scale Phenotyping: Productivity Characterisation Through Machine Learning

Bombrun, Maxime; Dash, Jonathan P.; Pont, David; Watt, Michael S.; Pearse, Grant D.; Dungey, Heidi S.

doi:10.3389/fpls.2020.00099

Cited by 23 publications

(8 citation statements)

References 44 publications

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“…Our study represents one of the first applications of remote sensing in a quantitative genetic framework for forest trees, particularly tree architectural traits. Previous applications of remote sensing in a genetic framework have studied provenance differences in heat-sum requirements when grown in a common garden environment [34], and highlighted the use of LiDAR technology for whole forest phenotyping following an area-based approach [35].…”

Section: Discussionmentioning

confidence: 99%

From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure

et al. 2020

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The use of unmanned aerial vehicles (UAVs) for remote sensing of natural environments has increased over the last decade. However, applications of this technology for high-throughput individual tree phenotyping in a quantitative genetic framework are rare. We here demonstrate a two-phased analytical pipeline that rapidly phenotypes and filters for genetic signals in traditional and novel tree productivity and architectural traits derived from ultra-dense light detection and ranging (LiDAR) point clouds. The goal of this study was rapidly phenotype individual trees to understand the genetic basis of ecologically and economically significant traits important for guiding the management of natural resources. Individual tree point clouds were acquired using UAV-LiDAR captured over a multi-provenance common-garden restoration field trial located in Tasmania, Australia, established using two eucalypt species (Eucalyptus pauciflora and Eucalyptus tenuiramis). Twenty-five tree productivity and architectural traits were calculated for each individual tree point cloud. The first phase of the analytical pipeline found significant species differences in 13 of the 25 derived traits, revealing key structural differences in productivity and crown architecture between species. The second phase investigated the within species variation in the same 25 structural traits. Significant provenance variation was detected for 20 structural traits in E. pauciflora and 10 in E. tenuiramis, with signals of divergent selection found for 11 and 7 traits, respectively, putatively driven by the home-site environment shaping the observed variation. Our results highlight the genetic-based diversity within and between species for traits important for forest structure, such as crown density and structural complexity. As species and provenances are being increasingly translocated across the landscape to mitigate the effects of rapid climate change, our results that were achieved through rapid phenotyping using UAV-LiDAR, raise the need to understand the functional value of productivity and architectural traits reflecting species and provenance differences in crown structure and the interplay they have on the dependent biotic communities.

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Section: Discussionmentioning

confidence: 99%

From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure

et al. 2020

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“…The use of thermal imagery provides an efficient means of phenotyping this variation in gs, and, for instance, canopy temperature was used to provisionally identify droughttolerant black poplar genotypes within a genetics trial comprising 503 genotypes [25]. Further research should utilise thermal imagery to identify drought-tolerant radiata pine genotypes within genetic trials and from well-characterised plantations using landscapelevel phenotyping methods [53,54]. The identification of these genotypes will allow growers to match genotypes to drought-prone sites to optimise growth and mitigate the impacts of climate change in more arid growing regions.…”

Section: Discussionmentioning

confidence: 99%

Detecting the Short-Term Effects of Water Stress on Radiata Pine Physiology Using Thermal Imagery

Watt,

de Silva,

Estarija

et al. 2023

Forests

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Despite the utility of thermal imagery for characterising the impacts of water stress on plant physiology, few studies have been undertaken on plantation-grown conifers, including the most widely planted exotic species, radiata pine. Using data collected from a pot trial, where water was withheld from radiata pine over a nine-day period, the objectives of this study were to (i) determine how rapidly key physiological traits change in response to water stress and (ii) assess the utility of normalised canopy temperature, defined as canopy temperature–air temperature (Tc–Ta), for detecting these physiological changes. Volumetric water content remained high in the well-watered control treatment over the course of the experiment (0.47–0.48 m3 m−3) but declined rapidly in the water stress treatment from 0.47 m3 m−3 at 0 days after treatment (DAT) to 0.04 m3 m−3 at 9 DAT. There were no significant treatment differences in measurements taken at 0 DAT for Tc–Ta, stomatal conductance (gs), transpiration rate (E) or assimilation rate (A). However, by 1 DAT, differences between treatments in tree physiological traits were highly significant, and these differences continued diverging with values in the control treatment exceeding those of trees in the water stress treatment at 9 DAT by 42, 43 and 61%, respectively, for gs, E and A. The relationships between Tc–Ta and the three physiological traits were not significant at 0 DAT, but all three relationships were highly significant from as early as 1 DAT onwards. The strength of the relationships between Tc–Ta and the three physiological traits increased markedly over the duration of the water stress treatment, reaching a maximum coefficient of determination (R2) at 7 DAT when values were, respectively, 0.87, 0.86 and 0.67 for gs, E and A. The early detection of changes in tree physiology from 1 DAT onwards suggests that thermal imagery may be useful for a range of applications in field-grown radiata pine.

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“…Bombrun used a machine learning approach to screen indicators that have a strong influence on forest productivity from multiple observations of forests. The results revealed that topology is one of the important drivers of forest productivity [18]. However, the study did not go further in exploring which specific topological indicators influenced forest productivity.…”

Section: Introductionmentioning

confidence: 88%

Study on the Relationship between Topological Characteristics of Vegetation Ecospatial Network and Carbon Sequestration Capacity in the Yellow River Basin, China

Fang

et al. 2021

Remote Sensing

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Achieving carbon neutrality is a necessary effort to rid humanity of a catastrophic climate and is a goal for China in the future. Ecological space plays an important role in the realization of carbon neutrality, but the relationship between the structure of vegetation ecological space and vegetation carbon sequestration capacity has been the focus of research. In this study, we extracted the base data from MODIS products and other remote sensing products, and then combined them with the MCR model to construct a vegetation ecospatial network in the Yellow River Basin in 2018. Afterward, we calculated the topological indicators of ecological nodes in the network and analyzed the relationship between the carbon sequestration capacity (net biome productivity) of ecological nodes and these topological indicators in combination with the Biome-BGC model. The results showed that there was a negative linear correlation between the betweenness centrality of forest nodes and their carbon sequestration capacity in the Yellow River Basin (p < 0.05, R2 = 0.59). On the other hand, there was a positive linear correlation between the clustering coefficient of grassland nodes and their carbon sequestration capacity (p < 0.01, R2 = 0.49). In addition, we briefly evaluated the vegetation ecospatial network in the Yellow River BASIN and suggested its optimization direction under the background of carbon neutrality in the future. Increasing the carbon sequestration capacity of vegetation through the construction of national ecological projects is one of the ways to achieve carbon neutrality, and this study provides a reference for the planning of future national ecological projects in the Yellow River Basin. Furthermore, this is also a case study of the application of remote sensing in vegetation carbon budgeting.

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Forest-Scale Phenotyping: Productivity Characterisation Through Machine Learning

Cited by 23 publications

References 44 publications

From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure

From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure

Detecting the Short-Term Effects of Water Stress on Radiata Pine Physiology Using Thermal Imagery

Study on the Relationship between Topological Characteristics of Vegetation Ecospatial Network and Carbon Sequestration Capacity in the Yellow River Basin, China

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