Saturday, April 20, 2024

SPACE

Mapping plant functional diversity from space: HKU ecologists revolutionize ecosystem monitoring with novel field-satellite integration



THE UNIVERSITY OF HONG KONG
Mapping Plant Functional Diversity from Space: HKU Ecologists Revolutionise Ecosystem Monitoring with Novel Field-Satellite Integration 

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HIGH-RESOLUTION SATELLITE IMAGES THAT CAPTURED MULTISPECTRAL DATA RECORDED THE REFLECTIONS OF LIGHT FROM PLANT LEAVES. THESE DATA ARE NOT ONLY OF GREAT RESEARCH IMPORTANCE, PROVIDING VALUABLE INSIGHTS INTO THE PHYSICAL AND BIOCHEMICAL PROPERTIES OF VEGETATION, BUT ALSO SHOWCASE STUNNING PATTERNS. 

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CREDIT: IMAGES ADAPTED FROM REMOTE SENSING OF ENVIRONMENT, 2024, DOI.ORG/10.1016/J.RSE.2024.114082.




An international team of researchers, led by Professor Jin WU from the School of Biological Sciences at The University of Hong Kong (HKU), has made a promising advancement in mapping plant functional traits from space using time-series satellite data. The study, published in Remote Sensing of Environment, showcases the innovative combination of the Sentinel-2 satellite mission and its dynamic time-series capabilities. This innovative approach not only unlocks a deeper understanding of essential foliar traits, providing crucial insights into the functional diversity and ecosystem functioning of terrestrial ecosystems, but it also equips us with powerful tools to address pressing environmental challenges effectively.

Leveraging the Satellites for In-depth Observations
Plant traits are vital in regulating key ecosystem processes such as carbon sequestration, air temperature regulation, and large-scale hydrological regulation. They also determine how ecosystems respond to various environmental stressors, ultimately determining their health, resilience, and vulnerability to climate change. However, large-scale mapping of these traits has been challenging due to limitations in existing methodologies, such as the difficulty in capturing traits across vast areas and issues such as data availability, trait complexity, and measurement techniques.

To overcome these challenges, Professor Wu’s team harnessed the power of satellite technology and introduced a pioneering approach that combines vegetation spectroscopy and phenology. Their approach utilised high-resolution imagery from the Sentinel-2 satellite, which captured multispectral data on a weekly interval with a 10-metre resolution. By analysing these satellite images, the team observed and recorded the reflections of light from plant leaves, providing valuable insights into the physical and biochemical properties of the vegetation. These observations were then compared to the timing of plant life cycle events, known as phenology. By integrating the data from satellite imagery and phenological observations, the team has been able to obtain comprehensive information about plant functional traits across high dimensions. This integration holds great potential for extending to other dimensions of plant characteristics, such as plant health, functioning, and resilience.

This method underwent thorough and rigorous testing to evaluate its efficacy, applicability across different scales, and potential for high-throughput monitoring. The test utilised benchmark data of 12 foliar traits collected from 14 geographically distant sites within the National Ecological Observatory Network (NEON) in the eastern United States.

Shuwen LIU, the first author and a PhD candidate from Professor Wu’s lab, stated: "Our approach effectively captures the diversity of plant traits at fine spatial scales while maintaining accuracy over large areas." Liu further explained that their method overcomes the limitations of other methods that rely solely on plant functional types or single image acquisitions.

The proposed approach outperformed traditional methods that rely on environmental variables or single Sentinel-2 images as predictors without requiring environmental variables to enhance predictive capabilities. This finding underscores the significance of phenological information in trait prediction and suggests that the ‘leaf economics spectrum’ theory may be the underlying mechanism driving their technical success. Given the model's proven effectiveness in 14 diverse ecosystem sites across the United States, it shows great promise for expansion to national and global scales, thereby enabling the monitoring of plant functional traits from ecosystem to regional and national levels.

Reflecting on the future potential of this research, Professor Wu said: "Future studies will focus on broader validation to fully exploit this technology’s potential in frontier basic science, such as understanding terrestrial ecosystems’ sensitivity response to climate change and identifying their respective tipping points. Additionally, there is great potential for applied science, particularly in exploring nature-based climate solutions."

About the research team
The Global Ecology and Remote Sensing (GEARS) lab at HKU aims to uncover the fundamental mechanisms that regulate vegetation-climate interactions across various scales, ranging from leaves to the global level. It employs a diverse range of tools, including cutting-edge geospatial techniques, field observations, eco-evolutionary and ecophysiological theories, earth system models, and high-performance computing. Its research goals are twofold: firstly, to advance fundamental science by exploring the mechanisms that link climate, species (functional) composition, and ecosystem processes, and secondly, to bridge the gap between scientific and technological advancements in order to address pressing environmental issues related to climate change, such as forest health monitoring, food security, climate change impact assessments, and nature-based climate change mitigation. About GEARS: https://wu-jin.weebly.com/

About Professor Jin Wu
Jin Wu is an Assistant Professor at HKU School of Biological Sciences and a recipient of the NSFC-Excellent Young Scholar (Hong Kong & Macau) award in 2019. Prior to this, he held a Goldhaber Distinguished Fellow position at Brookhaven National Laboratory and earned his PhD from the University of Arizona. With a wide range of interests in biodiversity, conservation, global change, and sustainability sciences, he utilises an integrated approach (combining remote sensing, AI, and domain knowledge) to study these topics and aims to enhance how people experience, understand, and appreciate our living habitats and inspire actions to sustain our natural ecosystems. He has published over 100 peer-reviewed papers, including in prestigious journals such as Science, Nature, Global Change Biology, and Remote Sensing of Environment. Currently, he serves as an Associate Editor for Remote Sensing in Ecology and Conservation.

Link to the paper and key figure:
The journal paper, entitled ‘Spectra-phenology integration for high-resolution, accurate, and scalable mapping of foliar functional traits using time-series Sentinel-2 data’, can be found at the following link: https://doi.org/10.1016/j.rse.2024.114082

For media enquiries, please contact Ms Casey To, External Relations Officer (tel: 3917 4948; email: caseyto@hku.hk / Ms Cindy Chan, Assistant Director of Communications of HKU Faculty of Science (tel: 3917 5286; email: cindycst@hku.hk).


Land cover (a) and functional trait maps produced from Satellite images. The team used four traits - LMA (b), nitrogen (c), potassium (d) and chlorophyll a+b (e) - as examples for demonstration. 

CREDIT

Figures adapted from Remote Sensing of Environment, 2024, doi.org/10.1016/j.rse.2024.114082.


Technical Trials for Easing the (Cosmological) Tension



A new study sorts through models attempting to solve one of the major challenges of contemporary cosmic science, the measurement of its expansion


SISSA MEDIALAB

The CMB at different resolutions 

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COMPARISON BETWEEN CMB DATA RESOLUTION COLLECTED BY PLANCK  AND SPT-3G

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CREDIT: THE SOUTH POLE TELESCOPE: HTTPS://POLE.UCHICAGO.EDU/PUBLIC/HOME.HTML




Thanks to the dizzying growth of cosmic observations and measurement tools and some new advancements (primarily the “discovery” of what we call dark matter and dark energy) all against the backdrop of General Relativity, the early 2000s were a time when nothing seemed capable of challenging the advancement of our knowledge about the cosmos, its origins, and its future evolution.
Even though we were aware there was still much to uncover, the apparent agreement between our observations, calculations, and theoretical framework was indicating that our knowledge of the universe was set to grow significantly and without interruption.

However, thanks to increasingly sophisticated observations and calculations, the emergence of an apparently small “glitch” in our understanding of the Universe proved capable of jamming seemingly perfectly oiled gears. At first, it was thought it could be resolved it with even more precise calculations and measurements, but this was not the case. The "cosmological tension" (or Hubble Tension), is a discrepancy between the two ways in which we calculate the so-called Hubble parameter, H0, which describes the universe's expansion. 

The Hubble parameter can be calculated following two paths: 

  • The astrophysical observations of celestial bodies defined as local, i.e., not very far from us: it is possible to calculate the speed at which bodies at different distances are moving away. The expansion and H0 in this case is calculated by comparing speeds and distances.
  • The calculations based on data from the cosmic microwave background CMB, a faint and extremely distant radiation dating back to the very early Universe. The information we gather at that distance allows us to calculate the Universe's expansion rate and the Hubble parameter.

These two sources provided not exactly equal, but very close and consistent values of H0, and at the time it seemed that the two methods were showing good agreement. Bingo. 

It was around 2013 when we realized that the "numbers didn't add up". “The discrepancy that emerged might seem small, but given that the error bars on both sides are becoming much smaller, this separation between the two measurements is becoming large”, Khalife explains. The initial two values of H0, in fact, were not too precise, and as the “error bars” were large enough to overlap, there was hope that future finer measurements would finally coincide. “Then the Planck experiment came along, giving very small error bars compared to the previous experiments” but still maintaining the discrepancy, dashing hopes for an easy resolution. 

Planck was a satellite launched in space in 2007 to gather an image of the CMB as detailed as never before. Its results released a few years later confirmed the discrepancy was real and what was a moderate concern turned into a significant crisis. In short: the most recent and near sections of the universe we observe tell a different story, or rather seem to obey a different physics, than the oldest and most distant ones, a very unlikely possibility.

If it's not a problem of measurements then it could be a flaw in the theory, many thought. The current accepted theoretical model is called ΛCDM. ΛCDM is largely based on General Relativity - the most extraordinary, elegant, and repeatedly observationally confirmed theory about the universe formulated by Albert Einstein more than a century ago - and takes into account dark matter (interpreted as cold and slow-moving) and dark energy as a cosmological constant.

Over the last years, various alternative models or extensions to the ΛCDM model have been proposed, but so far, none have proven convincing (or sometimes even trivially testable) in significantly reducing the "tension". “It is important to test these various models, see what works and what can be excluded, so that we can narrow the path or find new directions to turn to”, explains Khalife. In their new paper, he and his colleagues on the basis of previous research lined up 11 of these models, bringing some order to the theoretical jungle that has been created. The models were tested with analytical and statistical methods on different sets of data, both from the near and distant universe, including the most recent results from the SH0ES (Supernova H0 for the Equation of State) collaboration and SPT-3G (the new upgraded camera of the South Pole Telescope, collecting the CMB). 

Three of the selected models that were shown in previous works to be viable solutions were ultimately excluded by the new data this research considers. On the other hand, other three models still seem capable of reducing the tension, but this doesn’t solve the problem. “We found that those could reduce the tension in a statistically significant way, but only because they have very large error bars and the predictions they make are too uncertain for the standards of cosmology research”, says Khalife. “There is a difference between solving and reducing: these models are reducing the tension from a statistical point of view, but they're not solving it”, meaning that none of them is predicting a large value of H0 from CMB data alone. More in general none of the models tested proved superior to the others studied in this work in reducing the tension.

“From our test we now know which are the models that we should not look at to solve the tension,” concludes Khalife, “and we also know the models that we might be looking at in the future”. This work could be a base for the models that will be developed in the future, and by constraining them with increasingly precise data, we could move closer to developing a new model for our Universe.

 

Scientists discover new way to extract cosmological information from galaxy surveys




CHINESE ACADEMY OF SCIENCES HEADQUARTERS
An illustration of the main idea and results 

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AN ILLUSTRATION OF THE MAIN IDEA AND RESULTS FROM WANG ET AL., COMMUN. PHYS. 7, 130 (2024)

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CREDIT: CREDIT TO NAOC




Scientists at the National Astronomical Observatories of the Chinese Academy of Sciences (NAOC) and their international collaborators have recently developed a new method for efficiently extracting information from galaxy surveys.

Their research results were published online in the latest issue of Communications Physics.

Massive galaxy redshift surveys are powerful tools for probing the Universe in this era of precision cosmology. By observing a great number of spectra from distant galaxies, astronomers are able to create density fields of galaxies at different epochs of the Universe. These density fields carry crucial information about the clustering of galaxies, which is quantified by two-point and N-point (N>2) correlation functions.

“The information content in the N-point functions is highly complementary to that in the two-point functions,” said ZHAO Gongbo, lead author of the study and a researcher at NAOC. “The N-point functions play an important role in studies of the nature of dark energy, dark matter and gravity.”

However, it is difficult to make use of the N-point functions in practice due to various complexities, including the measurement and modeling of these quantities.

After working on this challenging task for a few years, ZHAO and his collaborators have developed a new method for extracting information in the N-point functions from the two-point functions.

This new method, which is based on a technology called density reconstruction, makes it possible to extract the primary information in the three-point and four-point functions by a joint analysis of the two-point functions measured from the pre- and post-reconstructed density fields, respectively.

“This opens a new window for using the high-order information in galaxy surveys in an efficient way,” said ZHAO. “and that’s important for cosmological implications for forthcoming galaxy surveys including Dark Energy Spectroscopic Instrument (DESI), Prime Focus Spectrograph (PFS) and China Space Station Telescope (CSST).”

This work was funded by the Natural Science Foundation of China (NSFC), China’s Ministry of Science and Technology (MOST), and the Chinese Academy of Sciences (CAS).

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