Thu Ya Kyaw

Under recurring droughts, the southwestern U.S. loses a significant proportion of precipitation as evapotranspiration (ET), suggesting an opportunity to reduce ET via forest thinning. To better understand the potential impacts of thinning on the forest hydrologic cycle, we used sap flow sensors and Bowen ratio stations to measure ET in thinned and non-thinned ponderosa pine (Pinus ponderosa Douglas ex C. Lawson) stands in northern Arizona during the wet year of 2023, where thinning removed 42% of overstory basal area. Although our study site had experienced prolonged drought in previous years, heavy winter snowfall made 2023 a wet year. We correlated sap flow with environmental variables and used principal component analysis to identify the primary drivers of ponderosa pine water use in thinned and non-thinned stands. Results showed that after accounting for tree size, thinned stands had ~20% (~5 L day⁻¹) higher individual-tree water use at daily and weekly temporal scales than non-thinned stands. At the stand level, thinning decreased overstory ET (OET) but increased understory ET (UET), indicating a reallocation of outgoing water fluxes in the water balance. As a result, total ET (sum of OET and UET) decreased from 584 to 516 mm year⁻¹. In the semi-arid forest, this decrease in total ET of 68 mm year⁻¹ (~12% reduction) indicates an ecohydrologically meaningful outcome of forest thinning. In both stands, tree water use was strongly regulated by environmental variables, primarily atmospheric variables such as air temperature and vapor pressure deficit. Overall, our results suggest that thinning can still promote an improved stand-level forest water balance during a wet year and thus may enhance forest resilience under projected increases in heat and aridity in the southwestern U.S.

Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE https://doi.org/10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.

Southwestern US forests are experiencing increasing wildfire activity, and land managers are implementing large‐scale forest thinning treatments. We investigated semi‐arid ponderosa pine forest thinning treatment and regional drought impacts on ECOSTRESS land surface temperature (LST) and evapotranspiration (ET). Our study period at a northern Arizona study site included an average precipitation year, 2019, a regional drought period of 2020–2022, and a record winter snowfall year 2023. We examined ECOSTRESS LST and ET during spring seasons when the region experiences an annual dry period, and plant water stress is heightened. Our results indicate that ECOSTRESS LST data are sensitive to forest thinning, regional drought and their interaction. Consistent with high‐resolution UAV images, ECOSTRESS LST data indicate the thinned forest had significantly greater temperature across years, regardless of precipitation patterns. During drought, ECOSTRESS LST increased in both thinned and non‐thinned forests (by up to 10°C) and then declined in 2023. ECOSTRESS ET was similarly sensitive to forest thinning and regional drought. Consistent with in situ ET measurements, ECOSTRESS ET was significantly greater in the non‐thinned forest compared to the thinned forest. ECOSTRESS ET significantly decreased during drought in both forests. Our analysis of EMIT data indicates that EMIT trends are not consistent with ground‐based hyperspectral data that documented thinned forest moisture content is greater than that of the non‐thinned forest. While quality filtering reduces ECOSTRESS data temporal resolution, both ECOSTRESS LST and ET data can be used across large spatial extents to examine impacts of regional drought and management treatments in semi‐arid ponderosa pine forests.

Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti) and published to ESS-dive https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.

Plant traits are often measured in the field or laboratory to characterize stress responses. However, direct measurements are not always cost effective for broader sampling efforts, whereas indirect approaches such as reflectance spectroscopy could offer efficient and scalable alternatives. Here, we used field spectroscopy to assess whether (1) existing vegetation indices could predict leaf trait responses to heat stress, or if (2) partial least squares regression (PLSR) spectral models could quantify these trait responses. On several warm, sunny days, we measured leaf trait responses indicative of photosynthetic mechanisms, plant water status, and morphology, including electron transport rate (ETR), photochemical quenching (qP), leaf water potential (Ψ_leaf), and specific leaf area (SLA) in 51 urban trees from nine species. Concurrent measures of hyperspectral leaf reflectance from the same individuals were used to calculate vegetation indices for correlation with trait responses. We found that vegetation indices predicted only SLA robustly (R² = 0.55), while PLSR predicted all leaf trait responses of interest with modest success (R² = 0.36 to 0.58). Using spectral band subsets corresponding to commercially available drone-mounted hyperspectral cameras, as well as those selected for use in common multispectral satellite missions, we were able to estimate ETR, qP, and SLA with reasonable accuracy, highlighting the potential for large-scale prediction of these parameters. Overall, reflectance spectroscopy and PLSR can identify wavelengths and wavelength ranges that are important for remote sensing-based modeling of important functional trait responses of trees to heat stress over broad ranges.

Eastern cottonwood (Populus deltoidesW. Bartram ex Marshall) and hybrid poplars are well-known bioenergy crops. With advances in tree breeding, it is increasingly necessary to find economical ways to identify high-performingPopulusgenotypes that can be planted under different environmental conditions. Photosynthesis and leaf nitrogen content are critical parameters for plant growth, however, measuring them is an expensive and time-consuming process. Instead, these parameters can be quickly estimated from hyperspectral leaf reflectance if robust statistical models can be developed. To this end, we measured photosynthetic capacity parameters (Rubisco-limited carboxylation rate (V_cmax), electron transport-limited carboxylation rate (J_max), and triose phosphate utilization-limited carboxylation rate (TPU)), nitrogen per unit leaf area (N_area), and leaf reflectance of seven taxa and 62 genotypes ofPopulusfrom two study plantations in Mississippi. For statistical modeling, we used least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Our results showed that the predictive ability of LASSO and PCA models was comparable, except forN_areain which LASSO was superior. In terms of model interpretability, LASSO outperformed PCA because the LASSO models needed 2 to 4 spectral reflectance wavelengths to estimate parameters. The LASSO models used reflectance values at 758 and 935 nm for estimatingV_cmax(R²= 0.51 and RMSPE = 31%) andJ_max(R²= 0.54 and RMSPE = 32%); 687, 746, and 757 nm for estimating TPU (R²= 0.56 and RMSPE = 31%); and 304, 712, 921, and 1021 nm for estimatingN_area(R²= 0.29 and RMSPE = 21%). The PCA model also identified 935 nm as a significant wavelength for estimatingV_cmaxandJ_max. Therefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening ofPopulusgenotypes because of its capacity to estimate these physicochemical parameters.

By Thu Ya Kyaw, Courtney M. Siegert, Padmanava Dash, et al., Published on 01/01/22

Generating renewable bioenergy crops requires varietals that are suited to grow under varying environmental conditions necessitating the development and testing of a wide range of poplar (Populus) genotypes. Meanwhile, there is an increasing demand for refining the selection process of high-performing poplars. However, a cost-effective method is still needed to predict the productivity of various poplar genotypes. Photosynthetic capacity and leaf nitrogen are important growth-related physicochemical traits, but measuring them in the field and laboratory is expensive and time-consuming. Alternatively, remote sensing of hyperspectral leaf spectra may serve as a proxy to rapidly estimate these traits, which are associated with absorption, reflection, and transmission of solar radiation. To quantify photosynthetic traits, CO ₂ response curves were used to estimate Rubisco-limited carboxylation rate (V _cmax ), maximum electron transport rate (J _max ), and triose phosphate utilization (TPU). From the same leaves measured for photosynthesis, leaf reflectance was measured with a handheld spectroradiometer. We measured a total of 105 leaf samples, including 6 taxa with 61 different poplar genotypes. For data analyses, Least Absolute Shrinkage and Selection Operator and Principal Component Analysis were used to determine the wavelengths that were the most useful for capturing the variability in the physicochemical data. Results showed that leaf reflectance at 758 nm and 936 nm were crucial wavelengths for predicting V _cmax (RMSPE = 31%) and J _max (RMSPE = 32%), while 687 nm and 757 nm were important predictors for TPU (RMSPE = 31%), and 709 nm and 927 nm were important predictors for leaf nitrogen (RMSPE = 22%). The wavelengths near 687 nm and 760 nm are the oxygen absorption bands, and also overlap with the chlorophyll fluorescence emission of plants. Therefore, it is possible to apply hyperspectral reflectance models for rapid clonal screening and high-throughput field phenotyping of photosynthetic capacity parameters and leaf nitrogen of various poplar genotypes.

The Bago Mountain Range in Myanmar is known as the “home of teak” (Tectona grandis L. f.) because of its bountiful, naturally growing teak-bearing forests. Accelerating forest loss and degradation are threatening the sustainable production of teak in the region. Changes in land cover between 2000 and 2017 in four reserved forests of the Bago Mountain Range were mapped using supervised classification of Landsat imagery and training data collected in the field. A stratified random sample was used to collect reference data to assess accuracy of the maps and estimate area. Based on the reference sample, it was estimated that the forest area declined from 71 240 ha (standard error (SE) = 1524 ha) in 2000 to 40 891 ha (SE = 4404 ha) in 2017, whereas the area of degraded forests increased from 88 797 ha (SE = 1694 ha) to 97 013 ha (SE = 5395 ha). The annualized rates of gross forest loss and gross forest degradation were 1.03% and 0.97%, respectively, indicating that forest degradation paralleled forest loss. In many degraded areas, there is an opportunity to ameliorate the situation through silviculture. The 2017 map identifies bamboo-dominated degraded forests where enrichment planting or reforestation is recommended.