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.

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.

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.