Zheng Li

Forage production is integral to the ecological and economic functions of rangeland ecosystems worldwide and is characteristically highly variable. Understanding temporal variability and its trends in forage production are essential for sustainable resource management. This study evaluates recent long-term (2001 to 2018) trends in year-to-year fluctuations in forage production and identifies key factors driving spatial variability of these trends across California annual grasslands. Using annual forage production estimations at 30 meters from fused remote sensing observations, we calculated coefficients of variation (CV) for 8-year moving windows to quantify interannual variability in forage production, vegetation phenology, climate, as well as other environmental factors. The trends in CVs were then assessed with Mann-Kendall tests and Sen’s slope, and three random forest models were built. Results showed that more than 36% of grasslands experienced significantly increasing trends in interannual forage production fluctuations. The random forest model based solely on climate variables revealed that spatial patterns of trends in temporal fluctuations of forage production were mostly driven by long-term climatic means; specifically, drier areas with a long-term mean growing season precipitation below ~500 mm, or warmer areas with long-term mean minimum temperatures above ~6 °C, were more likely to exhibit significant increasing trends in forage production fluctuations. Spatially, trends in the temporal variability of seasonal precipitation and late season maximum temperatures, as well as other environmental factors such as soil organic matter content and elevation, also significantly contributed to trends in forage production fluctuations, although to a lesser degree. Further analysis using a random forest model with remote sensing-based phenological metrics indicated statewide trends in forage production variability were linked to trends in peak growth variability, as well as trends in the end and length of the growing season. These results highlight the value of understanding year-to-year trends to inform the local, adaptive decisions that land managers must make in order to sustainably balance forage supply with livestock grazing demand.

High severity rangeland wildfires have enormous negative impacts on human safety and infrastructure and may have long‐term consequences on ecosystem structure, function, and services, across the vast rangelands in the western United States. We explored prefire vegetation structure of all high severity wildfires, as well as associated areas with low and moderate burn severity, in nonherbaceous‐dominated rangelands in western United States from 1985 to 2018, based on fractional cover of annual forbs and grasses, perennial forbs and grasses, shrubs and trees from the Rangeland Analysis Platform. These wildfires were grouped into nine clusters using Expectation Maximization algorithms and patterns of prefire vegetation structure and the major vegetation types were assessed. High woody cover is a common characteristic of the prefire vegetation structure. Four of the clusters, which accounted for more than 70% of the areas of high severity wildfires, were characterized by high (∼50%) woody cover in prefire vegetation structure. The areas with high severity burns also had significantly higher woody cover than the areas with low or moderate severity burns in eight of the nine clusters. Some of these ecosystems can have abundant fine fuel, structural connectivity such as fuel laddering, and/or highly flammable materials, which can facilitate the ignition of the woody crowns, leading to high severity wildfires. Management strategies (such as prescribed burning) that can decrease woody cover and reduce standing dead vegetation and fuel laddering could be effective in reducing the risk and extent of high severity wildfires on rangelands in the western United States.

Rangeland fine fuel biomass is a key factor in determining fire spread and intensity, while the accuracy of biomass estimation is limited due to inherent heterogeneity in rangeland ecosystems. In this study, high spatial resolution (0.23 m) images were used to classify fuel types and predict rangeland fine fuel biomass in west Texas based on the random forest algorithm. Two biomass models, including one with the fuel type, original spectral bands, and vegetation indices as explanatory variables, and the other that contained a combination of the fuel type, original spectral bands, vegetation, and texture indices as explanatory variables, were assessed. Furthermore, the biomass models were also examined by upscaling the remote sensing images from high to medium (30 m) spatial resolution with the spectral curves derived from Landsat images. The fuel type map had an accuracy of more than 95%, and herbaceous fuel types were kept for estimating fine fuel biomass. The results showed that around 76% and 80% of biomass variances were explained by models without texture indices and with texture indices, respectively. The fuel type and the normalized difference vegetation index (NDVI) were two significant input variables influencing fine fuel biomass for both models and adding texture indices contributed to the improvement of model accuracy. An upscaling analysis for biomass estimation using medium spatial resolution imagery showed that approximately 60% of the variance in biomass was explained by the model. The addition of fractional vegetation cover improved the model performance by explaining an additional 5% of the variance in biomass estimation. These findings indicate that high spatial resolution images have the potential to effectively estimate rangeland fuel types and fine fuel biomass, which can be helpful for mapping the spatial distribution of fine fuels to aid in monitoring and fire management on rangelands.

Wildfires in forest ecosystems have been well studied, while wildfires in rangelands ecosystems have received less attention. This study evaluated temporal trends of large wildfires on rangelands in western United States from 1984 to 2017, using the Monitoring Trends in Burn Severity product (captured wildfires ≥405 ha in size in western United States) and Autoregressive Integrated Moving Average analysis. The results showed a significant increase (more than fivefold) in area of rangeland burned by large wildfires, but generally no significant trends in proportions of areas burned at different levels of severity. The trends for all rangelands, in total area burned and in areas burned at different levels of severity, were most similar to the trends for herbaceous‐dominated rangelands but largely different from the trends for shrub‐dominated rangelands. These findings can aid in the development of management strategies for anticipating and reducing wildfire risk and enhancing rangeland conservation and sustainability.

Epitaxial SrRuO₃ thin films were deposited on SrTiO₃ (100) substrates by RF sputtering for use as bottom electrodes and epitaxial buffer layers. On these conductive substrates, epitaxial Pb(Zr_0.35Ti_0.65)O₃ (PZT) thin films were deposited by metalorganic chemical vapor deposition (MOCVD). X-ray diffraction (XRD), transmission electron microscopy (TEM) and optical waveguiding were used to characterize the phase, refractive index, and film thickness of the deposited films. The epitaxial PZT films were c-axis oriented and contained -19.7% volume fraction of 90° domains. Ferroelectric hysteresis and dielectric measurements of epitaxial PZT ferroelectric capacitor structures formed using sputtered ITO top electrodes showed: a remanent polarization of 51.8 Ω/cm₂, a coercive field of 54.9 kV/cm, a dielectric constant of 410, a bipolar resistivity of ∼5.8x10⁹ Ω-cm at a field of 275 kV/cm, and a breakdown strength of >400 kV/cm. The cyclic fatigue behavior of the films showed a strong dependence on the choice of electrode materials and the fatiguing wave form. These data support the model that the fatigue mechanism in these films arises from the trapping of injected charge carriers and is predominately an electronic phenomenon.