Best practices, a computer package, and hands-on tutorials to guide the suggested application of a powerful emerging technique for prediction of plant traits from hyperspectral data. 

The Science
The estimation of leaf traits, such as leaf nitrogen, from hyperspectral reflectance data enables rapid, high-throughput, non-destructive characterization of leaf function and plant phenotyping with applications in ecosystem characterization and monitoring. Partial least squares regression (PLSR) is a popular method for the prediction of a wide range of nutritional and structural leaf traits using hyperspectral data. However, there has not been a standard approach for developing and reporting these models and results. This has led to challenges in the wider application of the technique. We developed a detailed description of use of PLSR to predict leaf traits with spectra and our recommended best practices across all steps of the process, from experimental design, data collection, PLSR model building, model application and reporting of results. With the article, we also provide hands-on tutorials to assist users to in understanding the PLSR modeling and application of our best practices with their own data.

The Impact
Plant scientists require detailed and extensive information on the concentration and distribution of physiological and structural leaf properties to study vegetation responses to environmental change, monitor plant health, and facilitate the rapid screening of different plant phenotypes. Traditional approaches to measure these traits directly are expensive and logistically challenging. Here we summarize and illustrate an alternative spectroscopic approach for the rapid, accurate and non-destructive estimation of traits using remote sensing data.

Summary
Plant physiologists and ecologists regularly measure leaf functional traits, including leaf nitrogen or photosynthetic rate, across a range of leaves, plants, species, or environments. These direct measurements, while very accurate for characterizing leaf structure and function, are typically slow, expensive, and can be logistically challenging.  In addition, many ecological or phenotyping studies require a large number of samples, which can be impractical with traditional methods. On the other hand, remote sensing methods have been shown to be effective for the rapid estimation of many of key leaf traits, however the inconsistent usage of the methods have led to challenges in the wider application across the plant sciences. To address this challenge, and to help standardize the approach across studies to facilitate wider adoption, we provide a detailed summary of the spectral method of leaf trait estimation. We also provide clear examples and tutorials to illustrate how to use the approach, and we discuss a range of suggested “best-practices”. Importantly, we also highlight how the same approach can be scaled up to estimate vegetation traits across landscapes using non-contact remote sensing data.

Contact (BER): Daniel Stover, SC-23.1 (Daniel.Stover@science.doe.gov)
Science Contact: Shawn P. Serbin, Brookhaven National Laboratory, sserbin@bnl.gov (+1 631-344-3165)

Funding
This work was supported by the Next-Generation Ecosystem Experiments (NGEE Arctic and NGEE Tropics) projects that are supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science, and through the United States Department of Energy contract No. DE-SC0012704 to Brookhaven National Laboratory.

Publications
Burnett, A. C., J. Anderson, K. J. Davidson, K. S. Ely, J. Lamour, Q. Li, B. D. Morrison, D. Yang, A. Rogers, and S. P. Serbin. 2021. “A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression”. Journal of Experimental Botany. [DOI: https://doi.org/10.1093/jxb/erab295]

Related Links
Article URL: https://doi.org/10.1093/jxb/erab295
Companion computer code: https://zenodo.org/record/4730995
Brookhaven National Laboratory “Of Leaves and Light” article on the use of spectroscopy to estimate leaf functional traits https://www.bnl.gov/newsroom/news.php?a=213279
Open-source book chapter, “Scaling Functional Traits from Leaves to Canopies”: https://link.springer.com/chapter/10.1007/978-3-030-33157-3_3

Congratulations to Charlie Koven, our NGEE-Tropics modeling lead and a staff scientist at Lawrence Berkeley National Laboratory (LBNL), for being awarded 2021’s Piers J. Sellers Global Environmental Change Mid-Career Award from the American Geophysical Union (AGU). AGU’s Piers J. Sellers Global Environmental Change Mid-Career Award is an annual award that “recognizes outstanding contributions in research, educational, or societal impacts in the area of global environmental change, especially through interdisciplinary approaches” (from AGU’s website). Please check out the full LBNL Earth and Environmental Sciences Area (EESA)’s press release on Charlie’s recent award here. The award will be bestowed during December’s upcoming AGU Fall Meeting — don’t forget to drop by and congratulate Charlie if you will be in attendance!

Image courtesy of Christina Procopiou’s EESA press release on Charlie Koven’s receipt of the Piers J. Sellers Global Environmental Change Mid-Career Award.

A team assessed how factors affect forest disturbance intensity across multiple regions and examined the difficulty to build a general cyclone impact model.

The Science
Cyclones have huge impact on our planet earth. After cyclone passed, some trees were fallen while some trees just lost some leaves. Why is that? Scientists use satellite images and study what factors contribute to the different impact on forests brought by hurricanes. Scientists found a 40 m/s wind speed threshold that affect the cyclones impact, but little consistency can be found on other variables among the studies of different cyclones. Each cyclone interacted with the landscape in a unique way. In addition, they discussed the difficulties to build a model which can predict where the next damage region would be due to upcoming cyclone.

The Impact
Climate change enhances cyclone-related rainfall, which worse the impact of cyclone on forests. Therefore, it is important to understand how the forested in different regions were affected by past cyclones and get better insights to prepare for future cyclones. This study reveals the links between remote sensing images derived forest disturbance intensity and the factors, including wind and rainfall, forest structure, terrain features, and soil properties at the landscape scale. The authors are the first to discuss the possibility to build a machine learning model to predict the impact of an unseen hurricane on forests.

Summary
This study addressed the importance of climate variables, terrain features, and forest properties in predicting tree damage caused by cyclones. Wind, elevation, and pre-disturbance vegetation condition are strong predictors. Machine learning technologies were used to build cyclone impact models. As machine learning models become more popular in earth science, this study showed that them still had limitations in cyclone effects prediction. The models worked well on hold out test data, but they had weak predictability on unseen cyclones. The authors believed that more finer scale data can be helpful to build local models work with similar ecosystems and landscapes, but the complexities of cyclone effects coupled with landscapes, soils, states of affected systems, climate change leads us to question the existence of an omnipotent cyclone impact model that works for the globe.

Contact (BER): Daniel Stover, SC-23.1 (Daniel.Stover@science.doe.gov)
Science Contact: Yanlei Feng, University of California, Berkeley (ylfeng@berkeley.edu)
Jeffrey Q. Chambers, Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Berkeley, CA, USA (jchambers@lbl.gov)

Funding
This research was supported as part of the Next Generation Ecosystem Experiments-Tropics (NGEE), funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract number DE-AC02-05CH11231.

Publications
Feng, Y., Negrón-Juárez, R., Chambers, J. Q. (2021) Multi-cyclone analysis and machine learning model implications of cyclone effects on forests, International Journal of Applied Earth Observation and Geoinformation, Volume 103, https://doi.org/10.1016/j.jag.2021.102528.

Related Links
Disturbance intensity map of hurricane disturbance. Katrina, Rita, Yasi, María have been validated with field observations and high-resolution airborne remote sensing images. UI Interface: https://ylfeng.users.earthengine.app/view/jagfengetal
Supplementary data to this article can be found online at https://doi. org/10.1016/j.jag.2021.102528.

NGEE-Tropics modeling lead and staff scientist at Lawrence Berkeley National Laboratory (LBNL), Charlie Koven, helped play a key role in the Intergovernmental Panel on Climate Change (IPCC)’s recent 2021 Climate Change Report. This report is the United Nations (UN) IPCC’s sixth installment in a series of climate change reports designed as summaries for policymakers, with the previous iteration last published in 2014. You can find the full report here. Charlie Koven was one of four LBNL scientists to contribute to the report’s efforts.

Figure 1. Charlie Koven alongside the cover image of the IPCC Climate Change 2021 Report from the UN IPCC website, which can be found at https://www.un.org/en/climatechange/reports.

As the modeling lead for the NGEE-Tropics project, Charlie directs the development of the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). FATES, as an ecosystem model, will ultimately enhance the U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM), allowing for a more complete and complex representation of Earth’s ecosystems. A notable highlight of the 2021 IPCC report are simulations that show that the proportion of CO2 emissions taken up by land and ocean carbon sinks is smaller in scenarios with higher cumulative CO2 emissions (Figure 2). Figure 2 is based on  Earth system models like E3SM that NGEE-Tropics is building FATES to work within, with the goal of reducing uncertainty on this critical process needed for climate projections.

Figure 2. Figure 7 from the Summary for Policymakers in the 2021 IPCC report, which shows that the proportion of CO2 emissions taken up by land and ocean carbon sinks is smaller in scenarios with higher cumulative CO2 emissions.

A major theme surrounding the publication of the 2021 iteration of the IPCC Climate Change report is the pace at which we must tackle the climate crisis. There is a renewed sense of urgency sparked by the report. With climate science as sophisticated as it’s ever been, the 2021 report shows the future of climate change and global warming with a high degree of clarity. Various carbon emission and cycling scenarios are simulated in the report, with NGEE-Tropics’ own Charlie Koven playing an important role. In the report, a primary focus is the 2015 Paris Agreement’s goal of limiting warming to well below 2 degrees Celsius (C), with a target of 1.5 degrees C. There is one thing these scenarios have in common: swift and powerful action is required to avoid the escalating negative effects of climate change. To learn more about the 2021 IPCC report, and hear the perspective of Charlie and other LBNL scientists, check out the Berkeley Lab press release on its publication.

Lara Kueppers, another NGEE-Tropics science lead and LBNL scientist, has been helping spread the word on the 2021 IPCC Climate Change report. To provide a local perspective on the global climate change report, Lara Kueppers interviewed with the California Bay Area’s KQED to communicate some of the key messages contained within the 2021 IPCC report. The full interview can be found here, on the KQED website.

Figure 3. Lara Kueppers alongside the California Bay Area’s KQED logo from www.kqed.org.

Contact: Daniel Dores, ddores@lbl.gov, Berkeley Lab Program Operations Analyst. Charlie Koven, cdkoven@lbl.gov, Berkeley Lab Staff Scientist.

Predicting seasonal fluxes of water and energy across the Amazon

The Science
Forests regulate the water cycle, and store and absorb carbon dioxide from the atmosphere. However, their success to drive evaporative cooling and to do photosynthesis depends on how efficiently forests access and use light and water during the year. We used measurements from flux towers installed in Amazonia, and compared observations with predictions from four computer-based forest simulators (models). Models are useful for predicting how forests will cope with climate change, but need to be tested first. We found that models think the Amazon dry-up too often and too quickly, and that they reflect more light when compared to observations.

The Impact
Our knowledge about forest ecology, climate, structure, etc., is used by models to describe different ecosystems.  Moreover, models are representations of how forests work — how much trees transpire and grow, leaves reflect light, soil stores water, etc.  Models are tested by comparing their output to current observations as they will be used to forecast the future of tropical forests (e.g. we can infer tree growth if rainfall is reduced). This work describes measurements (e.g. temperature, tropical forest evaporation and transpiration) at different sites across Amazonia and points to processes that need to be improved on the tested models.

Summary
We used data collected in four eddy flux towers across the Amazon to quantify the seasonal cycle of sensible heat flux, evapotranspiration, emission of thermal infrared radiation, and the optical properties of the forest canopies.  We simulated the seasonal cycle of the same quantities using four terrestrial biosphere models that are often used to predict the future of the Amazon (namely IBIS, ED2, JULES, and CLM3.5). We compared the model predictions with the tower measurements and identified that most models predict a strong seasonality of the Bowen ratio (ratio between sensible and latent heat flux), and overall low water use efficiency. Consequently, models predicted that Amazon forests experience more frequent water stress than what has been observed. Likewise, the models predicted that the forest canopies would reflect more light than observed.

We identified possible explanations for such differences.  First, models do not represent when leaves shed or replace leaves, which may bias the canopy reflectance. Likewise, models seem to exaggerate the canopy interception of rainfall, which reduces the soil available water. Finally, the incorrect estimates of water stress also led to discrepancies between predicted and observed outgoing longwave radiation. Our findings can be used as references for future model development.

Figure. A 65-m eddy covariance flux tower in the Amazon Forest (Tapajos, Brazil) where we measure meteorology and exchanges of water, energy, and carbon between the forest and the atmosphere, and can help us to understand how tropical forests (Jaru and Tapajos shown here) will respond to climate change.  Image courtesy of Natalia Restrepo-Coupe.

Contact (BER): Daniel Stover, SC-23.1, Daniel.Stover@science.doe.gov (301-903-0289)
Science Contact: Natalia Restrepo-Coupe, Saleska Lab, Ecology and Evolutionary Biology, University of Arizona, nataliacoupe@gmail.com

Funding
This research was supported as part of the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. This research was also funded by the Gordon and Betty Moore Foundation “Simulations from the Interactions Between Climate, Forests, and Land Use in the Amazon Basin: Modeling and Mitigating Large-Scale Savannization” project, and the National Aeronautics and Space Administration (NASA) through the NASA LBA-DMIP project.

Publications
Restrepo-Coupe, et al., “Understanding water and energy fluxes in the Amazonia: Lessons from an observation-model intercomparison” Global Change Biology 27, 1802–1819 (2021) [DOI: 10.1111/gcb.15555]

Scientists revealed the temporal variation in canopy disturbances using a unique dataset of five-year monthly drone-acquired imagery

Figure. Canopy disturbance visualized on canopy surface models and orthomosaics calculated from photogrammetric analyses of drone imagery. (a,b) Elevation models for a portion of the study area on two successive dates, 28 August 2019 (a) and 23 September 2019 (b). (c) Difference in elevation between the two dates, with black area indicating large decrease in canopy elevation. (d,e) RGB orthomosaics of the same dates.

The Science
A mechanistic understanding of how tropical tree mortality responds to climate variation is urgently needed to predict how tropical forest carbon pools will respond to anthropogenic global change. Researchers used five years of approximately monthly drone-acquired imagery for 50 ha of tropical forest on Barro Colorado Island, Panama, to quantify temporal variation and climate correlates of treefalls, branchfalls, or collapse of standing dead trees. They found that canopy disturbance rates are highly temporally variable, and are well-predicted by extreme rainfall events. Treefalls accounted for 77 % of the total area and 60 % of the total number of canopy disturbances in treefalls and branchfalls combined.

The Impact
Moist tropical forests account for 40% of the global biomass carbon stocks, and uncertainty regarding the future of these stocks is a major contributor to uncertainty in the future global carbon cycle. An improved understanding of the processes of forest disturbance is critical to constrain estimates of current and future carbon cycling in tropical forests under climate change. Results demonstrate the utility of repeat drone-acquired data for quantifying forest canopy disturbance rates at fine temporal and spatial resolutions, thereby enabling robust tests of how temporal variation in disturbance relates to climate drivers.

Summary
The size distribution of canopy disturbances was best fit by a Weibull function, and was close to a power function for sizes above 25 m2. Canopy disturbance rates varied strongly over time and were higher in the wet season, even though windspeeds were lower in the wet season. The strongest correlate of temporal variation in canopy disturbance rates was the frequency of 1-hour rainfall events above the 99.4th percentile (here 35.7 mm hour-1, r = 0.67). Scientists hypothesize that extreme high rainfall is associated with both saturated soils, increasing risk of uprooting, and with gusts having high horizontal and vertical windspeeds that increase stresses on tree crowns.

Contacts (BER PM): Daniel Stover, SC-23.1, Daniel.Stover@science.doe.gov (301-903-0289)
Science Contact: Raquel Fernandes de Araujo, STRI, araujo.raquelf@gmail.com

Funding
Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research; Smithsonian Institution Competitive Grants Program for Science; Smithsonian Tropical Research Institute fellowship program.

Publications
Araujo, R. F., Grubinger, S., Celes, C. H. S., Negrón-Juárez, R. I., Garcia, M., Dandois, J. P., and Muller-Landau, H. C. (2021), Strong temporal variation in treefall and branchfall rates in a tropical forest is explained by rainfall: results from five years of monthly drone data for a 50-ha plot, Biogeosciences Discuss. [DOI: 10.5194/bg-2021-102]