Distinguishing Land Change from Natural Variability and Uncertainty in Central Mexico with MODIS EVI, TRMM Precipitation, and MODIS LST Data

Precipitation and temperature enact variable influences on vegetation, impacting the type and condition of land cover, as well as the assessment of change over broad landscapes. Separating the influence of vegetative variability independent and discrete land cover change remains a major challenge to landscape change assessments. The heterogeneous Lerma-Chapala-Santiago watershed of central Mexico exemplifies both natural and anthropogenic forces enacting variability and change on the landscape. This study employed a time series of Enhanced Vegetation Index (EVI) composites from the Moderate Resolution Imaging Spectoradiometer (MODIS) for 2001–2007 and per-pixel multiple linear regressions in order to model changes in EVI as a function of precipitation, temperature, and elevation. Over the seven-year period, 59.1% of the variability in EVI was explained by variability in the independent variables, with highest model performance among changing and heterogeneous land cover types, while intact forest cover demonstrated the greatest resistance to changes in temperature and precipitation. Model results were compared to an independent change uncertainty assessment, and selected regional samples of change confusion and natural variability give insight to common problems afflicting land change analyses

AC Electric Field Activated Shape Memory Polymer Composite

Shape memory materials have drawn interest for applications like intelligent medical devices, deployable space structures and morphing structures. Compared to other shape memory materials like shape memory alloys (SMAs) or shape memory ceramics (SMCs), shape memory polymers (SMPs) have high elastic deformation that is amenable to tailored of mechanical properties, have lower density, and are easily processed. However, SMPs have low recovery stress and long response times. A new shape memory thermosetting polymer nanocomposite (LaRC-SMPC) was synthesized with conductive fillers to enhance its thermo-mechanical characteristics. A new composition of shape memory thermosetting polymer nanocomposite (LaRC-SMPC) was synthesized with conductive functionalized graphene sheets (FGS) to enhance its thermo-mechanical characteristics. The elastic modulus of LaRC-SMPC is approximately 2.7 GPa at room temperature and 4.3 MPa above its glass transition temperature. Conductive FGSs-doped LaRC-SMPC exhibited higher conductivity compared to pristine LaRC SMP. Applying an electric field at between 0.1 Hz and 1 kHz induced faster heating to activate the LaRC-SMPC s shape memory effect relative to applying DC electric field or AC electric field at frequencies exceeding1 kHz

Simultaneous Broadband Vector Magnetometry Using Solid-State Spins

We demonstrate a vector magnetometer that simultaneously measures all Cartesian components of a dynamic magnetic field using an ensemble of nitrogen-vacancy (NV) centers in a single-crystal diamond. Optical NV-diamond measurements provide high-sensitivity, broadband magnetometry under ambient or extreme physical conditions; and the fixed crystallographic axes inherent to this solid-state system enable vector sensing free from heading errors. In the present device, multi-channel lock-in detection extracts the magnetic-field-dependent spin resonance shifts of NVs oriented along all four tetrahedral diamond axes from the optical signal measured on a single detector. The sensor operates from near DC up to a $12.5$ kHz measurement bandwidth; and simultaneously achieves $\sim\!50$ pT/$\sqrt{\text{Hz}}$ magnetic field sensitivity for each Cartesian component, which is to date the highest demonstrated sensitivity of a full vector magnetometer employing solid-state spins. Compared to optimized devices interrogating the four NV orientations sequentially, the simultaneous vector magnetometer enables a $4\times$ measurement speedup. This technique can be extended to pulsed-type sensing protocols and parallel wide-field magnetic imaging.Comment: 13 pages, 5 figures, 1 table, Supplemental Material included as ancillary fil

Electric Field Activated Shape Memory Polymer Composite

Provided is an electrically activated shape memory polymer composite capable of thermal shape reformation using electric power to heat the composite through its matrix glass transition temperature. The composite includes an adaptable polymer matrix component using a diglycidyl ether resin, at least one substantially well-dispersed conductive or magnetic nano-filler component, and at least one elastic, laminated layer. Also provided are methods of preparing the composite and methods of activating the composite. A shape reformation of the composite is triggered by applying an electric field at DC and/or at a frequency above about 1.mu.Hz for a sufficient time

Broad-band X-Ray Spectra of the Black Hole Candidate GRO J1655-40

We present broad-band (2 keV to 2 MeV) X-ray spectra of GRO J1655-40, a luminous X-ray transient and occasional source of relativistic radio jets, obtained with RXTE and OSSE. In one observation, the luminosity is found to be 18% of the Eddington limit, which is one of the highest luminosities ever observed from GRO J1655-40. For this observation, we find that an adequate fit is obtained when a broad iron line and a reflection component are added to a model consisting of a power-law plus a soft excess component. The 95% confidence lower limit on the rms line width is 0.86 keV. The power-law component has a photon index of 2.72 and extends to at least 800 keV without a cutoff. After this observation, a significant drop in the (5-12 keV)/(1.5-5 keV) hardness ratio occurred on a timescale less than 2 hours. From an RXTE observation of GRO J1655-40 made after the hardness transition, we find that the power-law index is harder (2.415 +/- 0.011), the flux of the power-law component is lower, and the total luminosity is 10% of the Eddington limit. The change in the power-law component is consistent with the correlation between the spectral index and power-law flux previously reported for GRO J1655-40.Comment: 20 pages, 5 figures, Accepted for publication in the Astrophysical Journa

Extreme events driving year-to-year differences in gross primary productivity across the US

Solar-Induced chlorophyll Fluorescence (SIF) has previously been shown to strongly correlate with gross primary productivity (GPP), however this relationship has not yet been quantified for the recently launched TROPOspheric Monitoring Instrument (TROPOMI). Here we use a Gaussian mixture model to develop a parsimonious relationship between SIF from TROPOMI and GPP from flux towers across the conterminous United States (CONUS). The mixture model indicates the SIF-GPP relationship can be characterized by a linear model with two terms. We then estimate GPP across CONUS at 500-m spatial resolution over a 16-day moving window. We find that CONUS GPP varies by less than 4% between 2018 and 2019. However, we observe four extreme precipitation events that induce regional GPP anomalies: drought in west Texas, flooding in the midwestern US, drought in South Dakota, and drought in California. Taken together, these events account for 28% of the year-to-year GPP differences across CONUS

A double peak in the seasonality of California's photosynthesis as observed from space

Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here, we present a downscaling method to obtain 500 m spatial resolution SIF over California. We report daily values based on a 14 d window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation indices – the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation index (NIR_v) – at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas previous work has shown SIF to have a low sensitivity to clouds and to detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatiotemporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum, while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatiotemporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake