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(#1 2020)


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

This newsletter is the first communication since the creation of the Forest Phenotyping Working Group in October 2019 during the The 6th International Plant Phenotyping Symposium (IPPS) in Nanjing, China, organised by the International Plant Phenotyping Network (IPPN). The IPPN is the association gathering the world's leading experts and institutions in the discipline of plant phenotyping on a global level.

The goals of  the Forest Phenotyping Working Group is to improve the understanding of multiple scale forest dynamics through its phenotypes. The development of tools, platforms, methodologies and an international community will bring together experts from silvilculture, forest management, genetics, computer & data science, experimental design, and forest biometrics and geomatics, to tackle the emerging challenges related to climate changes, sustainable forest management, the need for healthier forests and the manufacture of sustainable and cleaner products.

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 (subject: Newsletter)

As the chairman of the Forest Phenotyping Working Group, I am proud by the work accomplished by the members throughout this sensitive year. I am glad to be surrounded by motivated and talented characters and I hope that more members will join us in this journey.

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

Best regards,

Maxime Bombrun
(Chairman of the Forest Phenotyping Working Group)


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Presentation of the Forest Phenotyping Committee 

At the end of October 2019, The 6th International Plant Phenotyping Symposium (IPPS) took place in Nanjing, China. It was co-organized by IPPN member Agricultural University of Nanjing, one of China's most rapidly developing AgTech hot-spots.

The IPPS gathered 300 participants from all over the world, for 5 days to give or listen to over 45 talks & over 70 posters, sorted into 9 different session topics in reference to IPPNs Expert Working Groups (yield improvement, abiotic stress tolerance, image analysis, forest- and root phenotyping).

This event was the occasion for the Forest Phenotyping Working Group to kick-off with a workshop on the state-of-the-art of forest phenotyping starting with An introduction to forest phenotyping, presented by Heidi Dungey. Then, Nicholas Coops demonstrated the Use of Advanced Remote Sensing Tools for Phenotyping Evaluation. Finally, the workshop was concluded with a presentation on Forest Phenotyping assisted by machine learning from Maxime Bombrun.

The workshop was followed by the election of the Forest Phenotyping Working Group Commitee during which

In the current global circumstances, the committee hope to met and welcome many new members during the next IPPS planned for March 2021, in the Netherlands.

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.



From David Pont, Geomatics Scientist at Scion, New Zealand Forest Research Institute Limited

Phenotyping is widely applied in the crop sector but is its infancy for forestry. The size and longevity of forest trees demand in-forest phenotyping and creates distinct challenges for researchers. Scion is combining remote sensing, spatial information systems, and genomics to evaluate forest and tree productivity. Two recent projects have looked at managing forest-scale productivity and evaluating individual trees in a forest stand.

Using phenotyping to manage forest and estate productivity
The goal was to develop a practical way to optimise productivity using genetics, environment, management and remote sensing. We combined forest stand records for genetics and forest management data, environmental layers including climate, soils, and measures of yield derived from Airborne Laser Scanning (ALS). The complete data set comprised a 25 x 25 m grid across a 39,000 hectare forest estate, containing over 178 million data points. Machine learning was applied to identify which factors had the greatest influence on forest productivity.
We were able to predict productivity across the estate with an R2 > 0.90. The key drivers of productivity included genetics, soil type and tree density, showing that genetic, environmental and management (G x E x M) factors all influenced productivity.
Forest managers are now using the productivity models to predict the best genetics for different sites and are exploring the importance of uniform site occupancy for productivity.

Evaluating individual trees

Airborne Laser Scanning and photogrammetric point clouds collected from a UAV platform were used to detect individual trees with a 98% accuracy and to estimate their height with and R2=0.86. In our work, we have found that UAV-mounted lidar is more accurate and requires less data processing than photogrammetry, but both are viable methods.
We have been utilising crown sizes and tree locations to quantify fine-scale environmental effects and competition indices, showing that they are critical to accurately describe tree growth. Models that combine both competition and environmental effects have allowed us to explore the G x E x M relationships and more accurately quantify the genetic signal. Additional work also showed that tree competition interacts with tree disease dynamics and survival and further research is planned in this area.
Being able to extract a clearer indication of the effects of genetics on the productivity and health of individual trees will improve the results from breeding trials and future genetic selection. Further, models for competition end environmental effects will enable the development of next generation of individual tree growth models for forest management.

Good management records for planted forests, geographical information systems, data surfaces that describe climate and other abiotic factors, combined with remote sensing technologies, are allowing us to phenotype individual trees in situ in planted forests, increasing our understanding of the influence of genetics and the environment on phenotype.
Ultimately, accurate, quantitative phenotyping in forestry to will allow us to identify and quantify the key drivers of forest productivity, and inform and optimise future breeding, deployment and management programmes.





The Forest Phenotyping Working Group has been contacted by one of our new 2020 member to organise a webinar on Assessing Flowering and Tree Health through Remote Sensing. Some of our members have already expressed their interest to participate. The webinar will be supported by the IPPN and be held around April-May. If your research fits the topic and you want to participate contact us at (subject: webinar) and follow our channels of communication for more details soon.

Upcoming conference: 2021 NAPPN Annual Conference February 16-19.

The North American Plant Phenotyping Network (NAPPN) will be holding the first annual conference (virtual) from February 16-19, 2021. This conference highlights the most recent advances in the rapidly developing field of Plant Phenomics.

Congratulations to Dr. Sudipto Chatterjee from TERI School of Advanced Studies, New Delhi, India. His abstract has been selected to represent the Forest Phenotyping Working Group during the NAPPN conference. Dr. Chatterjee will present his work on the importance of monitoring forest health through Proposed Phenological Studies to Assess Forest Health in Indo Gangetic Plains in India.
The study goes alongside the objectives of the working group to protect the forests and provide healthy and sustainable trees to preserve both the biodiversity and our environment.



Research Spotlight: Use of advanced modelling methods to estimate radiata pine productivity indices

Watt et al., (2020) have developed productivity index maps of New Zealand. Using a national database of 3,676 plots with predictors extracted from geospatial surfaces, they predict Site Index and 300 Index for Pinus Radiata. They developed an ensemble model from statistical and machine learning based methods to optimise the accuracy of the predictions.

Alves, F. C., Balmant, K. M., Resende Jr, M. F., Kirst, M., & de los Campos, G. (2020). Accelerating forest tree breeding by integrating genomic selection and greenhouse phenotyping. The Plant Genome, e20048. doi: 10.1002/tpg2.20048

Bendig, J., Malenovský, Z., Gautam, D., & Lucieer, A. (2019). Solar-Induced Chlorophyll Fluorescence Measured From an Unmanned Aircraft System: Sensor Etaloning and Platform Motion Correction. IEEE Transactions on Geoscience and Remote Sensing58(5), 3437-3444. doi: 10.1109/TGRS.2019.2956194.

Bombrun, M., Dash, J. P., Pont, D., Watt, M. S., Pearse, G. D., & Dungey, H. S. (2020). Forest-Scale Phenotyping: Productivity Characterisation Through Machine Learning. Frontiers in Plant Science11. doi: 

Camarretta, N., A Harrison, P., Lucieer, A., M Potts, B., Davidson, N., & Hunt, M. (2020). From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure. Remote Sensing12(19), 3184. doi: 10.3390/rs12193184

Cortés, A. J., Restrepo-Montoya, M., & Bedoya-Canas, L. E. (2020). Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate. Frontiers in Plant Science11, 1606. doi: 10.1007/s13595-014-0446-5

Fady, B., Aravanopoulos, F., Benavides, R., González-Martínez, S., Grivet, D., Lascoux, M., ... & Vinceti, B. (2020). Genetics to the rescue: managing forests sustainably in a changing world. Tree Genetics & Genomes16(6), 1-11. doi: 10.1007/s11295-020-01474-8

Grubinger, S., Coops, N. C., Stoehr, M., El-Kassaby, Y. A., Lucieer, A., & Turner, D. (2020). Modeling realized gains in Douglas-fir (Pseudotsuga menziesii) using laser scanning data from unmanned aircraft systems (UAS). Forest Ecology and Management473, 118284.
doi: 10.1016/j.foreco.2020.118284

Huang, Y., Ren, Z., Li, D., & Liu, X. (2020). Phenotypic techniques and applications in fruit trees: a review. Plant Methods16(1), 1-22. doi: 10.1186/s13007-020-00649-7

Isabel, N., Holliday, J. A., & Aitken, S. N. (2020). Forest genomics: Advancing climate adaptation, forest health, productivity, and conservation. Evolutionary Applications13(1), 3-10. doi: 10.1111/eva.12902

Lebedev, V. G., Lebedeva, T. N., Chernodubov, A. I., & Shestibratov, K. A. (2020). Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives. Forests11(11), 1190. doi: 10.3390/f11111190

Martins, G. S., Yuliarto, M., Antes, R., Prasetyo, A., Unda, F., Mansfield, S. D., ... & Acosta, J. J. (2020). Wood and Pulping Properties Variation of Acacia crassicarpa A. Cunn. ex Benth. and Sampling Strategies for Accurate Phenotyping. Forests11(10), 1043. doi: 10.3390/f11101043

Mazis, A., Choudhury, S. D., Morgan, P. B., Stoerger, V., Hiller, J., Ge, Y., & Awada, T. (2020). Application of high-throughput plant phenotyping for assessing biophysical traits and drought response in two oak species under controlled environment. Forest Ecology and Management465, 118101. doi: 10.1016/j.foreco.2020.118101

Salmela, M. J., Velmala, S. M., & Pennanen, T. (2020). Seedling traits from root to shoot exhibit genetic diversity and distinct responses to environmental heterogeneity within a tree population. Oikos129(4), 544-558. doi: 10.1111/oik.06797

Solvin, T. M., Puliti, S., & Steffenrem, A. (2020). Use of UAV photogrammetric data in forest genetic trials: Measuring tree height, growth, and phenology in Norway spruce (Picea abies L. Karst.). Scandinavian Journal of Forest Research35(7), 322-333. doi: 10.1080/02827581.2020.1806350

Yáñez, M. A., Zamudio, F., Espinoza, S., Ponce, M., Gajardo, J., & Espinosa, C. (2020). Assessing wood properties on hybrid poplars using rapid phenotyping tools. New Forests, 1-14. doi: 10.1007/s11056-020-09799-x
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