Forest fragmentation is changing the shape of Amazonian trees
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POINT CLOUD OF TREES GROWING DEEP IN THE FOREST (ABOVE) AND IN FRAGMENTED FORESTS IN CENTRAL AMAZONIA, OBTAINED USING HIGHRESOLUTION TERRESTRIAL LASER SCANNING (TLS). THE GREEN AREA REPRESENTS THE AMOUNT OF BIOMASS.
view moreCREDIT: JANI NAERHI
Using laser scanning, researchers at the University of Helsinki have mapped out how the fragmentation of forests affects tree shape in the rainforests of Brazil. The results are surprising, as they shed light on the impact of human activity on the tropical environment and, consequently, on climate change in a new way.
In tropical rainforests, trees and vegetation have adapted for millennia to obtain light, survive and grow as effectively as possible. However, the conditions have changed.
Because of clear-cutting, the area of undisturbed rainforests is decreasing. At the edges of deforested areas, temperatures rise, and there is more light. Trees are able to adapt to changes in their living conditions and environment, but how does environmental change affect the shape of trees in the tropical rainforest? There has been no overall understanding of this so far.
Associate Professor Eduardo Maeda from the University of Helsinki coordinated an international project investigating tree shapes on the edges of the tropical rainforest. Matheus Nunes, who previously worked at the University of Helsinki and is now active at the University of Maryland, headed a study where data were collected through terrestrial laser scanning to model Amazonian trees.
Edge-effects change the growth pattern of trees
The findings were recently published in the prestigious Nature Communications journal. The study clearly demonstrated that trees growing on forest edges are shaped differently from those growing deep in the forest.
“Edge effects are evidenced in the thickness of tree trunks and symmetry of canopies. By adjusting these characteristics, trees can adapt to get as much light as possible and increase their chances of survival. In spite of increasing wood production, the amount of biomass that binds carbon dioxide in this 40-year-old forest is reduced by as much as twenty percent,” says Eduardo Maeda.
It was already known that there is less biomass in fragmented forests, as tall trees are more likely to fall over on the edges.
Running carbon sink calculations anew
Tropical rainforests continue to cover large areas and constitute a carbon sink significant for Earth as a whole. The changes now observed in individual trees pertain to large areas, making the findings globally relevant.
“The effect of human activity on climate change will need to be re-evaluated. This study provides new information on the adaptation of the rainforest to environmental change, as well as tools for researchers and decision-makers who are considering how to mitigate climate change,” Maeda notes.
The researchers used remote sensing to collect data in Central Amazonia, Brazil, creating a 3D tree representation for modelling. Various tree properties, such as their ability to use water and light as well as trunk size, were used in the calculation.
The study was funded by the Research Council of Finland (decision numbers 318252, 319905 and 345472).
Further information:
Eduardo Maeda, Associate Professor, eduardo.maeda@helsinki.fi, +358 50 476 4677
https://www.helsinki.fi/en/about-us/people/people-finder/eduardo-maeda-9109012
The Terrestrial Ecosystem Dynamics research group (Tree-D Lab)
Original article: Edge effects on tree architecture exacerbate biomass loss of fragmented Amazonian forests, Nunes et al. 2023. Nature Communications
JOURNAL
Nature Communications
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Edge effects on tree architecture exacerbate biomass loss of fragmented Amazonian forests
ARTICLE PUBLICATION DATE
14-Dec-2023
From forest gaps to landscapes: new insights into ecosystem functions
Ecosystems fulfil a number of vital tasks: They store carbon, clean polluted water, pollinate plants and so on. How well an ecosystem can fulfil these tasks depends largely on its biodiversity, i.e. the variety of plants, animals and microorganisms that live in it. Until now, scientists have only been able to understand the exact nature of this relationship at a local level, for example in relation to individual forest areas, meadows and ponds. The DFG (German Research Foundation) research group BETA-FOR, led by the University of Würzburg (JMU), has now succeeded in developing a statistical method that for the first time can also analyse the contributions of biodiversity between local ecosystems to the multifunctionality of entire landscapes.
"This statistical tool was urgently needed," explains Prof. Jörg Müller, spokesperson of the research group and holder of the Chair of Animal Ecology with a focus on ecological field research in our latitudes at the Department of Zoology III. "Human use is increasingly leading to the homogenisation of entire landscapes worldwide. This has consequences that are as far-reaching as they are unknown. With the help of our new method, we can analyse for the first time how the loss of heterogeneous landscapes affects not only biodiversity, but also their multifunctionality." Measures to promote biodiversity can also be evaluated in relation to the functions of the landscape - such as renaturalisation projects, the establishment of protected areas or the promotion of sustainable agriculture."
From local ecosystems to entire landscapes
And this is how it works: the new statistical method relates the different biodiversity between individual ecosystems in a landscape to the overall multifunctionality. The term "multifunctionality" refers to the bundle of all functions that an ecosystem performs simultaneously. It breaks down the multifunctionality of a landscape into two components - the functions at the local level and those between different ecosystems in a landscape. This way, multifunctionality can be related to local biodiversity and to the biodiversity created by the diversity of habitats.
The new tool, a R Package called MF.beta4, was developed by the DFG research group BETA-FOR in cooperation with the renowned statistician and mathematician Anne Chao from the National Tsing Hua University in Taiwan. With this development, the group has achieved one of its central scientific goals.
About the DFG research group BETA-FOR
The DFG project "Enhancing the structural diversity between patches for improving multidiversity and multifunctionality in production forests" focusses on the relationship between biodiversity, ecosystem services and their stability. In BETA-FOR, the influence of different forest management practices on biodiversity is being investigated experimentally. A transdisciplinary consortium of researchers from the fields of biology, ecology, forestry, remote sensing and statistics is recording and analysing around 20 ecosystem functions and species groups in 11 forests in Germany.
Original publication
Hill-Chao numbers allow decomposing gamma multifunctionality into alpha and beta components. Anne Chao, Chun-Huo Chiu, Kai-Hsiang Hu, Fons van der Plas, Marc W. Cadotte, Oliver Mitesser, Simon Thorn, Akira S. Mori, Michael Scherer-Lorenzen, Nico Eisenhauer, Claus Bässler, Benjamin M. Delory, Heike Feldhaar, Andreas Fichtner, Torsten Hothorn, Marcell K. Peters, Kerstin Pierick, Goddert von Oheimb, Jörg Müller. Ecology Letters. 2023 Dec 10. DOI: 10.1111/ele.14336
JOURNAL
Ecology Letters
METHOD OF RESEARCH
Data/statistical analysis
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Hill–Chao numbers allow decomposing gamma multifunctionality into alpha and beta components
ARTICLE PUBLICATION DATE
10-Dec-2023
Revolutionizing forestry: 'CountShoots' unveils advanced UAV and AI techniques for precise slash pine shoot counting
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THE OVERALL RESEARCH ROADMAP.
view moreCREDIT: PLANT PHENOMICS
In southern China, the genetically improved slash pine (Pinus elliottii) plays a crucial role in timber and resin production, with new shoot density being a key growth trait. Current manual counting methods are inefficient and inaccurate. Emerging technologies such as UAV-based RGB imaging and deep learning (DL) offer promising solutions. However, DL methods face challenges in global feature capture, necessitating additional mechanisms. Innovations like the Vision Transformer and its derivatives (e.g., TransCrowd, CCTrans) show potential in plant trait counting, offering simplified and more effective approaches for large-scale and accurate data processing. This technological evolution presents an opportunity for research in automated new shoot detection in slash pines, utilizing these advanced DL methodologies.
In July 2023, Plant Phenomics published a research article entitled “CountShoots: Automatic Detection and Counting of Slash Pine New Shoots Using UAV Imagery”. This study introduces the Slash Pine Shoot Counting Network (SPSC-net), a model based on CCTrans, designed for counting new shoots of slash pine. It incorporates a feature pyramid module for accurate counting.
In the detection of slash pine trees, models like YOLOv5, Efficientnet, and YOLOX were compared, using a 0.5 threshold for tree identification. YOLOX demonstrated superior precision, recall, and average precision(AP), especially at a higher 0.75 threshold. In contrast, Faster-RCNN showed the lowest performance. Manual counting of 26 test images revealed that YOLOX had a lower false detection rate and EfficientNet had minimal missed targets. YOLOX excelled in complex and overlapping target scenarios. For the detection of new shoots, the study compared balanced and unbalanced OT frameworks, while assessing different transposition cost matrices. The perspective-guided model displayed the best performance, validating the efficacy of nonequilibrium OT for density regression. SPSC-net achieved the lowest MSE and MAE among all models, outperforming DM-Count, CSR-net, and MCNN. Scatter plots and density maps demonstrated the high prediction accuracy of the SPSC-net. On this basis the study developed CountShoots, a system of extracting and counting slash pine. Implemented on the Flask framework, it features modules for user interaction, model loading, plant extraction, and shoot counting. The process involves uploading images, extracting plant data, counting shoots, and providing feedback on the results, all streamlined for user convenience. The study confirmed the effectiveness of the SPSC-net in multiscale image processing of slash pine. YOLOX and SPSC-net were compared with other models, demonstrating superior detection and counting accuracy. SPSC-net's self-attention mechanism and feature pyramid fusion enable detailed and semantically rich feature extraction. Despite its success, there are limitations to consider, such as potential obstruction from the canopy layer and restriction on UAV flight height.
In conclusion, the research developed a comprehensive pipeline using SPSC-net and YOLOX for accurate slash pine shoot counting and crown detection, offering a robust tool for forestry research and genetic breeding of slash pine.
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References
Authors
Xia Hao1, Yue Cao1, Zhaoxu Zhang1, Federico Tomasetto2, Weiqi Yan3, Cong Xu4, Qifu Luan5, and Yanjie Li5*
Affiliations
1College of Information Science and Engineering, Shandong Agricultural University, No. 61, Daizong Road, Taian 271018, Shandong Province, China.
2AgResearch Ltd., Christchurch 8140, New Zealand.
3Department of Computer Science, Auckland University of Technology, Auckland 1010, New Zealand.
4School of Forestry, University of Canterbury, Private Bag 4800, 8041 Christchurch, New Zealand.
5Research Institute of Subtropical Forestry, Chinese Academy of Forestry, No. 73, Daqiao Road, Fuyang, Hangzhou 311400, Zhejiang Province, China.
About Yanjie Li
He is an associate professor at the Research Institute of Subtropical Forestry, Chinese Academy of Forestry. His research interests include genetic breeding and germplasm resource evaluation, mainly focusing on the rapid estimation and evaluation of high-throughput forest germplasm resource phenotypes in important timber species in subtropical areas such as Pinus wetland, Pinus torch pine and Sassafras.
JOURNAL
Plant Phenomics
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
CountShoots: Automatic detection and counting of slash pine new shoots using UAV imagery
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