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FOREST PHENOTYPING WORKING GROUP
 

NEWSLETTER

(#2 2021)

EDITORIAL

Dear <<First Name>> <<Last Name>>,

Welcome to the second newsletter of the Forest Phenotyping Working Group. While the worldwide sanitary context still slows down most of our researches and brings uncertainties regarding future gatherings and international travels, the pandemic also has had a direct impact on forestry. Many sawmills and furniture factories have closed provoking an oversupply from the timber industry, already carrying at full inventories, and thus a collapse in lumber price, while a high demand for hygiene products has seen a rise in pulp wood demand. The combination of regional climate extremes (e.g., heat waves and drought) and these variations in wood demands affected by the Covid-19 will impact the future of forest biodiversity and management. Now, more than ever, new phenotyping tools and methods are needed to integrate this new dimension, secure the value of green spaces and forests, and understand the dynamic adaptability of our trees.


The Forest Phenotyping working group aims to:

  • Promote the concept of forest phenotyping to the international scientific community
  • Enable the sharing of knowledge and efforts with the international scientific community
  • Encourage the expansion of efforts to phenotype seedlings and trees
  • Build networks within the IPPN community to share and develop platforms of interest to other IPPN working groups



Context of the Newsletter:

  • The Forest Phenotyping Working Group Newsletter will be released three times a year, to avoid spam & redundancy with information presented in other IPPN media channels
  • Featured are news, new developments in forest phenotyping, publications, projects, etc. connected to tree and forest phenotyping which can be send to the working group Office from IPPN members & non-members, -from academia & industry
  • Dedicated sections with specific content (e.g. publications, symposia, workshops, jobs & expert interviews) will be added upon requests
  • Feedback & content input is welcome & can be directed to WGForestPhenotyping@fz-juelich.de (subject: Newsletter)


If you want to join force with us, share your research and follow the latest innovation and challenges of the Forest Phenotyping community, you can become a member and subscribe to the newsletter on the IPPN website or send an email to WGForestPhenotyping@fz-juelich.de. You will automatically receive the newsletters and also be informed of the future webinars.

I wish you good continuation and all the best for 2021!

Best regards,



Maxime Bombrun
(Chairman of the Forest Phenotyping Working Group)
 

NEWS

Presentation of the Forest Phenotyping Committee 


At the session of ‘Forest ecology and management - natural forest’ during 19th Ecological Society of China conference (Nov. 21, 2020 - Nov. 22, 2020), Dr. Lin (Tony) Cao gave an invited speech titled ‘Predictions of tree species distribution and forest structure in subtropical nature forests using aerial and near-field remote sensing’.

At the Symposium on Applications of 3D laser scanning technology (Nov. 17, 2020, organized by Jiangsu Society for Geodesy Photogrammetry and Cartography), Dr. Lin (Tony) Cao gave a keynote speech titled ‘Forest inventory and monitoring in Forests of Southern China using Airborne, UAV and Mobile LiDAR data.

At the sixth China LiDAR conference (Nov. 20, 2020-Nov. 22, 2020), Dr. Lin (Tony) Cao submitted a poster presentation titled ‘The Applications of LiDAR in Precision Silviculture.

The Forest Phenotyping Working Group has also sponsored Dr. Sudipto Chatterjee from the TERI School of Advanced Studies (India), who presented a ‘Phenological Studies to Assess Forest Health in Indo-gangetic Plains in India’ during the last North American Plant Phenotyping Network (NAPPN, February 16-19).

New channels of communication


One of the main mission of the Forest Phenotyping Working Group is to actively working on creating and improving our communication channels to grow our community, connect our members, and provide the latest development in forest phenotyping.

Therefore, we have started to create social and scientific networks platform to communicate between us: 

Feel free to follow and join to participate in the development of our community.
 


DEVELOPMENT IN

FOREST PHENOTYPING


1. Building phenotyping platform in smart forest farm

The overall framework for building phenotyping platform in a smart forest farm

The overall framework consists of three main components:

(1) Application of forestry Internet of things (IOT): including basic hardware and software facilities and software application system, in which the software application mainly includes the forest farm operation command platform, video monitoring platform, data acquisition system, etc.

(2) The smart forest farm control platform includes four application systems: forest environment monitoring system (including four designated points), integrated intelligent control system of water and fertilizer, intelligent greenhouse monitoring system, and information management and scheduling system of forestry machinery.

(3) The smart forest farm monitoring platform includes video monitoring network in key areas.




The technical framework for building phenotyping platform in a smart forest farm

The platform adopts layered design idea, including perception layer, transport layer, presentation layer, and application layer as shown in figure above. This platform has flexible expansibility, portability and cross-platform, adopts B/S mode multi-layer architecture technology, supports single sign-on and unified security authentication, supports digital certificate and flexible access rights.

In terms of the IOT, communication and control devices supporting fault detection, low power consumption, and flexible expansibility are adopted to lay a foundation for the operation and maintenance of the entire platform in the future. In the aspect of database, the technology of database sub-database and cluster technology, such as application system cluster, database cluster, cache server cluster are adopted to ensure the stability and efficiency of the system operation.





2. Quantifying vertical profiles of biochemical traits for forest plantation species using advanced remote sensing approaches

Biochemical traits in forest vegetation are key indicators of leaf physiological processes, specifically photosynthetic and other photochemical light pathways, and are critical to the quantification of the terrestrial carbon cycle. Advances in remote sensing sensors and platforms are allowing multi-dimensional and continuous-spatial information to be acquired in a fast and non-destructive way to quantify forest biochemical traits at multiple spatial scales. Here we demonstrate the use of high spectral resolution, hyperspectral data combined with high density three-dimensional information from Light Detection and Ranging (LiDAR) both acquired from an unmanned aerial system (UAS) platform, to quantify and assess the three-dimensional distribution of biochemical pigments on individual tree canopy surfaces. To do so, a DSM based fusion method was developed to integrate the 3D LiDAR point cloud with hyperspectral reflectance data. Regression-based models were then developed to predict a number of biochemical traits (i.e., chlorophyll (Chl) a, b, total Chl and total carotenoids (Cars) content) from a suite of common spectral indices at three vertical canopy levels, and were evaluated using a leave-one-out cross-validation approach. One-way ANOVA and Duncan's multiple comparison post hoc tests were used to investigate the vertical distribution of biochemical pigments on individual tree canopy surfaces, and in response to age and species. Our results demonstrated that a number of vegetation indices, derived from the hyperspectral data, were strongly correlated with a number of biochemical traits (Adj-R2 = 0.85–0.91; rRMSE = 5.19–6.38%). This study indicates the potential of using fused 3D point cloud information with spectral data to monitor physiological activities of forest canopy for carbon accumulation estimation as well as precision forestry applications such as nutrition diagnosis, water regulation and subsequent productivity enhancement of these planted forest systems.





3. Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach

Accurate segmentation of individual tree crowns (ITCs) from airborne light detection and ranging (LiDAR) data remains a challenge for forest inventories. Although many ITC segmentation methods have been developed to derive tree crown information from airborne LiDAR data, these algorithms contain uncertainty in processing false treetops because of foliage clumps and lateral branches, overlapping canopies without clear valley-shape areas, and sub-canopy crowns with neighbouring trees that obscure their shapes from an aerial perspective. Here, we propose an approach to crown segmentation using computer vision theories applied in different forest types.

First, a dual Gaussian filter was designed with automated adaptive parameter assignment and a screening strategy for false treetops. This preserved the geometric characteristics of sub-canopy trees while eliminating false treetops. Second, anisotropic water expansion controlled by the energy function was applied for accurate crown segmentation. This utilized gradient information from the digital surface model and explored the morphological structures of tree crown boundaries as analogous to the maximal valley height difference from surrounding treetops. We demonstrate the generality of our approach in the subtropical forests within China. Our approach enhanced the detection rate of treetops and ITC segmentation relative to the marker-controlled watershed method, especially in complicated intersections of multiple crowns. A high performance was demonstrated for three pure Eucalyptus plots and three plots dominated by Chinese fir. Finally, in a relatively complex forest park containing a wide range of tree species and sizes, a high performance was also achieved. Our method demonstrates that methods inspired by the computer vision theory can improve on existing approaches, providing the potential for accurate crown segmentation even in mixed forests with complex structures.






4. Phenotyping experiments in Gaofeng Forest Farm


The figure shows the visual comparison of Airborne Laser Scanning (ALS) point clouds and Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) point clouds within one strip (width: 5m) of planted forest in Southern China. We acquired ALS data in 2018 and UAV-LiDAR data in 2020. The first line of figure showed ALS data in 2018, the second showed UAV-LiDAR data in 2020, the third showed the comparison results by overlapping display in two colors. By comparing two different stage point clouds, the felling and growth condition of planted forest can be characterized visually. It is meaningful for forest managers to intuitively understand the process of forest dynamic change and provide useful data basis for sustainable management.


 

UPCOMING

EVENT

Forest Phenotyping IPPN Working Group 2021 Goals


The goals of the Working Group (WG) will be achieved through a set of milestones:
Establishment of the WG:

  • Consolidate the working group; First re-election of the chair and co-chairs during IPPS 2021
  • Organize collaborative workshop with other working groups during IPPS 2021, presenting the challenges and solutions of forest phenotyping by international sponsored speakers.
Call for members
  • Increase presence on social networks, employ tools to contact and gather collaborators and idea, while promoting the existence of the WG.
  • Sponsor speaker for the NAPPN Conference (online), representative of the WG.

Web portal:
Continue and improve the concept of newsletter to inform the members of the next events that the working group will organize, and the last discoveries in the field of tree and forest phenotyping.

Upcoming conferences:


1. SilviLaser 2021. Vienna, Austria, 29.9.–1.10.2021.

                                
                                        https://silvilaser2021.at/
 

We (TU Wien together with the company Umweltdata) as the organizing team of the silviLaser 2021 have decided to organize and to carry out the SilviLaser 2021. In any case, the SilviLaser 2021 will take place in Vienna, Austria.

From the current perspective, we expect to hold SilviLaser 2021 as a hybrid conference. We will comply with all applicable safety regulations on site and also planning an on-site health check, if needed. You can be sure, that all precautionary measures will be provided to keep our guests safe and sane during the conference and the excursions.

We will make the final decision on the format of SilviLaser 2021 until the end of April 2021. Apart from that, we are of course very happy to welcome you physically here in Vienna.

In addition to the conference program (live and/or video presentations, exhibition stands), an attractive benchmark will be organized in several field-sites near Vienna where companies and institutions can demonstrate their measurement equipment. The recorded data will be made available to the community as open-access data. In the context of this benchmark you will have the unique opportunity to get in contact with the companies and experience the data acquisition live and thus get a deep understanding of the characteristics of the individual LiDAR systems.






2. 9th edition of the ForestSAT (12-16 September 2022)

                                     

                                 http://forestsat2020.forestsat.com/

The 9th edition of the ForestSAT conference will be organized in Krakow (Poland) by: University of Agriculture in Krakow (Faculty of Forestry), Jagiellonian University in Krakow (Institute of Geography and Spatial Management), Forest Research Institute in Warsaw (Poland) and University of Washington (College of the Environment; Seattle, WA) on 12-16 September 2022.

ForestSAT 2022 will hosted by the Faculty of Forestry, University of Agriculture in Krakow, at the northern university campus (29 Listopada Ave.)

ForestSAT 2022 will continue this long tradition of bringing together international scientists, stake holders, and policy makers from research organizations, universities, public agencies, NGOs and the private sector and marks the return of the conference to Europe.
Themes proposed for ForestSAT 2022 by organizers:
 
  • Forest Big Data
  • Global Forest Observation
  • The Revolution in Remote Sensing Technologies & Fusion
  • Automation of Data Processing using Cloud Processing
  • Forest Resources Inventory
  • Mapping forest decline/degradation/disasters
  • Applications of Hyperspectral Sensors
  • LiDAR application in Forestry
  • Application of UAS in Forestry
  • Forest Changes – 2D/3D/4D
  • Forest Management and Policy
  • Urban Forestry & Ecosystem Services
  • Precision Forestry (TLS, MLS, HLS, ULS)
  • Modeling Application on Forest Biomass
  • New Approaches to Forest Ecosystem Modeling
  • Monitoring of Protected Forests, Biodiversity and Forest Services



 

3. 2022 NAPPN Annual Conference
 
https://nappn.plant-phenotyping.org/
 
The NAPPN Annual Conference will be held in Athens, Georgia, and highlight the most recent advances in the rapidly developing field of Plant Phenomics.

The NAPPN General Assembly meeting, hands on workshops, virtual tours, and more!





4. WUR organizes 7th International Plant Phenotyping Symposium (postpone IPPS7 to 2022)
https://www.wur.nl/en/show/WUR-organises-7th-International-Plant-Phenotyping-Symposium.htm
 
The IPPN Board has announced that Wageningen University & Research has been elected to organize the 7th International Plant Phenotyping Symposium in October 2021. Since we cannot predict the course of the pandemic and the progress of vaccinations globally, especially with respect to international travel, the IPPN Board together with the local organizing team have jointly decided to postpone IPPS7 to 2022. We still follow the plan to combine the IPPS7 with the inauguration of the Netherlands Plant Eco-Phenotyping Center in Wageningen! Both events would provide visitors an excellent showcase of the most advanced methods, technologies, and applications in plant phenotyping worldwide.




5. Webinar - sharing AI models and data on forest damage (In Swedish)

The Swedish Forest Agency has started several projects for the use of artificial intelligence (AI), most of them around forest damage. Some projects use AI to identify damaged trees in drone images. In the webinar, Halil Radogoshi and Alice Högström, both of the Swedish Forest Agency, and Fredrik Walter, Dianthus will go present:
  • The AI ​​infrastructure that the Swedish Forest Agency has built up to identify damaged trees in drone images.
  • What geodata about forest damage The Swedish Forest Agency works with and how they are shared.
  • Examples of the use of shared AI training data outside the Swedish Forest Agency.
More information and registration here

 
 

PUBLICATIONS

Research Spotlight: The status and prospects of remote sensing applications in precision silviculture


With the rapid development of the society and economy as well as the growth of population, the contradiction between our country's timber supply and demand is still prominent, and its dependence on foreign countries is high. Faced with limited land resources, there is an urgent need to cultivate forest resources more efficiently and with high quality, and to apply precision silviculture technologies in various links such as directive breeding and intensive management. The multi-platform, multi-angle, multi-mode three-dimensional observation system and quantitative analysis method constructed by modern remote sensing technology are the key technologies for precision silviculture. The integrated and accurate new precision silviculture system, built with remote sensing technology as the core, from soil type analysis, land adaptability evaluation, ecological environment simulation to tree breeding, irrigation and fertilization, forest growth monitoring, pest control, etc., will fully support the overall quality and efficiency improvement of modern forestry as well as the precise improvement of forest quality. This review article first introduces the application status of RGB cameras, multispectral, hyperspectral, LiDAR, thermal infrared and fluorescence sensors in precision silviculture, and makes a comprehensive comparison of their application characteristics and measurement indicators; then, focuses on the use of remote sensing in the three key application directions i. e., high quality species selection, monitoring and diagnosis of nutrient stress, accurate water and fertilizer sprinkler irrigation, as well as the analysis of the common needs of each application direction; finally, from three aspects, i.e., multi-source remote sensing information fusion, artificial intelligence, Internet of Things and 3S technology integration, and the integrated application of remote sensing data with physiological and ecological models and radiation transmission models, the development trend and application prospects of future remote sensing technology in precision silviculture are analyzed.

Reference:
Zhou, K., Cao, L., 2021. The status and prospects of remote sensing applications in precision silviculture. Natl. Remote Sens. Bull. 25, 423–438.

Research Spotlight: Competition between radiata pine can be used to identify trees with desirable traits


Competition between plantation grown radiata pine can be used to improve heritability estimates for desirable tree traits by accounting for phenotypic variation not explained by environmental and spatial effects.
 
Competition from large trees can reduce the growth of their smaller neighbours. The size of a tree (diameter at breast height), the size of neighbouring competitor trees and how close they are, can represent competition and growth potential.
 
Using a stand of young radiata pine of planted as part of a genetics trial in the Kaingaroa Forest, central North Island of New Zealand, Scion scientists have used competition metrics to account for environmental variation in tree traits. The trees’ height, diameter at breast height, stem volume, infection by Dothistroma needle blight and wood stiffness (wood quality) were assessed from the ground and the area was laser scanned from the air.
 
“Airborne laser scanning data can be used to distinguish between individual tree crowns and give us information about tree size and spatial relationships to neighbours. From this, we can calculate metrics to represent tree to tree competition,” says Dr David Pont, lead author of the study. “We characterised individual trees to generate tree-level phenotypes and tree-to-tree competition metrics”.
 
“We then used the competition metrics to improve our ability to account for environmental variation and its relative importance on heritable tree traits.”
 
Including competition substantially reduced residual variance and improved estimates of the traits being studied. The reductions in residuals ranged from -65.48% for tree height to -21.03% for wood stiffness, and improvements in heritabilities from 39% for tree height to 14% for wood stiffness.
 
“Including an explicit competition term and a generic spatial term is a robust and effective way to account for environmental variation in tree traits,” says Dr Pont.
 
The researchers also found that the relative amounts of variation due to genotype, competition, and site were distinct by trait. Tree diameter at breast height and volume were strongly influenced by competition, while height was influenced equally by competition and site effects.
 
The ability to phenotype individual trees to quantify the interactions between genotype, environment, and management practices is critical to improve tree genetics though breeding, and to the development of precision forestry approaches to increase the efficiency and sustainability of managed forests.



Reference:
Pont, D., Dungey, H. S., Suontama, M., & Stovold, G. T. (2020). Spatial models with inter-tree competition from airborne laser scanning improve estimates of genetic variance. Frontiers in Plant Science, 11, 2141. https://doi.org/10.3389/fpls.2020.596315

RECENT PUBLICATIONS:
Bayat, M., Bettinger, P., Hassani, M., & Heidari, S. (2021). Ten-year estimation of Oriental beech (Fagus orientalis Lipsky) volume increment in natural forests: a comparison of an artificial neural networks model, multiple linear regression and actual increment. Forestry: An International Journal of Forest Research.

Carpentier, S. C., Iyyakutty, R., Kissel, E., Van Wesemael, J., Chase, R., Tomekpé, K., & Roux, N. (2021). Phenotyping protocol for drought tolerance in banana.

du Toit, F., Coops, N. C., Goodbody, T. R., Stoehr, M., & El-Kassaby, Y. A. (2021). Deriving internal crown geometric features of Douglas-fir from airborne laser scanning in a realized-gain trial. Forestry: An International Journal of Forest Research.

Finch, J. P., Brown, N., Beckmann, M., Denman, S., & Draper, J. (2021). Index measures for oak decline severity using phenotypic descriptors. Forest Ecology and Management, 485, 118948.

Fu, X., Zhang, Z., Cao, L., Coops, N.C., Goodbody, T.R.H., 2021. Remote Sensing of Environment Assessment of approaches for monitoring forest structure dynamics using bi-temporal digital aerial photogrammetry point clouds. Remote Sens. Environ. 255, 112300.
https://doi.org/10.1016/j.rse.2021.112300.

Larue, C., Barreneche, T., & Petit, R. J. (2021). Efficient monitoring of phenology in chestnuts. Scientia Horticulturae, 281, 109958.

Liu, H., Shen, X., Cao, L., Yun, T., Zhang, Z., Fu, X., Chen, X., Liu, F., 2021. Deep Learning in Forest Structural Parameter Estimation Using Airborne LiDAR Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 1603–1618. https://doi.org/10.1109/JSTARS.2020.3046053.

Lombardi, E., Ferrio, J. P., Rodríguez-Robles, U., de Dios, V. R., & Voltas, J. (2021). Ground-penetrating Radar as Phenotyping Tool for Characterizing Intraspecific Variability in Root Traits of Pinus Halepensis.

Mphahlele, M. M., Isik, F., Hodge, G. R., & Myburg, A. A. (2021). Genomic Breeding for Diameter Growth and Tolerance to Leptocybe Gall Wasp and Botryosphaeria/Teratosphaeria Fungal Disease Complex in Eucalyptus grandis. Frontiers in plant science, 12, 228.

Opgenoorth, L., Dauphin, B., Benavides, R., Heer, K., Alizoti, P., Martínez-Sancho, E., ... & Cavers, S. (2021). The GenTree Platform: growth traits and tree-level environmental data in 12 European forest tree species. GigaScience, 10(3), giab010.

Pavicic, M., Overmyer, K., Jones, P., Jacobson, D., & Himanen, K. (2021). Image-based methods to score fungal pathogen symptom progression and severity in excised Arabidopsis leaves. Plants, 10(1), 158.

Ramalho de Oliveira, L. F., Lassiter, H. A., Wilkinson, B., Whitley, T., Ifju, P., Logan, S. R., ... & Martin, T. A. (2021). Moving to Automated Tree Inventory: Comparison of UAS-Derived Lidar and Photogrammetric Data with Manual Ground Estimates. Remote Sensing, 13(1), 72.

Shen, X., Cao, L., Coops, N.C., Fan, H., Wu, X., Liu, H., Wang, G., Cao, F., 2020. Remote Sensing of Environment Quantifying vertical pro fi les of biochemical traits for forest plantation species using advanced remote sensing approaches. Remote Sens. Environ. 250, 112041. https://doi.org/10.1016/j.rse.2020.112041.

Takashima, Y., Hiraoka, Y., Matsushita, M., & Takahashi, M. (2021). Evaluation of Responsivity to Drought Stress Using Infrared Thermography and Chlorophyll Fluorescence in Potted Clones of Cryptomeria japonica. Forests 2021, 12, 55.

Watt, M. S., Palmer, D. J., Leonardo, E. M. C., & Bombrun, M. (2021). Use of advanced modelling methods to estimate radiata pine productivity indices. Forest Ecology and Management, 479, 118557.

Yun, T., Jiang, K., Li, G., Eichhorn, M.P., Fan, J., Liu, F., Chen, B., An, F., Cao, L., 2021. Remote Sensing of Environment Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach. Remote Sens. Environ. 256, 112307. https://doi.org/10.1016/j.rse.2021.112307.

Zhang, Z., Cao, L., Liu, H., Fu, X., Shen, X., 2020. Assessing the 3-D Structure of Bamboo Forests Using an Advanced Pseudo-Vertical Waveform Approach Based on Airborne Full-Waveform LiDAR Data. IEEE Trans. Geosci. Remote Sens. 1–24. https://doi.org/10.1109/TGRS.2020.3042790.


 

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