Monitoring Landscape Dynamics in Central U.S. Grasslands with Harmonized Landsat-8 and Sentinel-2 Time Series Data

Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals have been used for monitoring. Recently, the harmonized Landsat-8 and Sentinel-2 (HLS) data increased the temporal frequency of the data. Here we investigated if the increased data frequency provided adequate observations to characterize highly dynamic grassland processes. We evaluated HLS data available for 2016 to (1) determine if data from Sentinel-2 contributed to an improvement in characterizing landscape processes over Landsat-8 data alone, and (2) quantify how observation frequency impacted results. Specifically, we investigated into estimating annual vegetation phenology, detecting burn scars from fire, and modeling within-season wetland hydroperiod and growth of aquatic vegetation. We observed increased sensitivity to the start of the growing season (SOST) with the HLS data. Our estimates of the grassland SOST compared well with ground estimates collected at a phenological camera site. We used the Continuous Change Detection and Classification (CCDC) algorithm to assess if the HLS data improved our detection of burn scars following grassland fires and found that detection was considerably influenced by the seasonal timing of the fires. The grassland burned in early spring recovered too quickly to be detected as change events by CCDC; instead, the spectral characteristics following these fires were incorporated as part of the ongoing time-series models. In contrast, the spectral effects from late-season fires were detected both by Landsat-8 data and HLS data. For wetland-rich areas, we used a modified version of the CCDC algorithm to track within-season dynamics of water and aquatic vegetation. The addition of Sentinel-2 data provided the potential to build full time series models to better distinguish different wetland types, suggesting that the temporal density of data was sufficient for within-season characterization of wetland dynamics. Although the different data frequency, in both the spatial and temporal dimensions, could cause inconsistent model estimation or sensitivity sometimes; overall, the temporal frequency of the HLS data improved our ability to track within-season grassland dynamics and improved results for areas prone to cloud contamination. The results suggest a greater frequency of observations, such as from harmonizing data across all comparable Landsat and Sentinel sensors, is still needed. For our study areas, at least a 3-day revisit interval during the early growing season (weeks 14–17) is required to provide a \u3e50% probability of obtaining weekly clear observations

Snow cover duration trends observed at sites and predicted by multiple models

The 30-year simulations of seasonal snow cover in 22 physically based models driven with bias-corrected meteorological reanalyses are examined at four sites with long records of snow observations. Annual snow cover durations differ widely between models, but interannual variations are strongly correlated because of the common driving data. No significant trends are observed in starting dates for seasonal snow cover, but there are significant trends towards snow cover ending earlier at two of the sites in observations and most of the models. A simplified model with just two parameters controlling solar radiation and sensible heat contributions to snowmelt spans the ranges of snow cover durations and trends. This model predicts that sites where snow persists beyond annual peaks in solar radiation and air temperature will experience rapid decreases in snow cover duration with warming as snow begins to melt earlier and at times of year with more energy available for melting

Perancangan Peraga LED Terprogram Berbasis Mikrokontroler AT89C52

Dengan tersedianya mikrokontroler yang memiliki berbagai fasilitas serta murahnya harga PC yang ditawarkan, maka peluang untuk merancang peralatan pengendalian LED untuk berbagai keperluan menjadi sangat terbuka. Tujuan dari penelitian ini adalah merancang dan menguji peraga LED terprogram berbasis mikrokontroler AT89C52. Alat penampil LED matrik dirancang data-nya bisa dimasukan secara otomatis, sehingga LED dapat menampilkan data dengan segera. Perancangan sistem penampil LED matrik yang dibuat meliputi dua bagian utama yaitu bagian perangkat keras dan bagian perangkat lunak. Perangkat keras meliputi mikrokontroler AT89C52 sebagai pusat pengontrol, penggerak kolom dan penggerak baris serta LED matrik untuk menampilkan data. Perangkat lunak meliputi bahasa mesin mikrokontroler dan untuk berhubungan dengan PC menggunakan Borland Delphi. Pengujian dilakukan dengan membandingkan tampilan LED matrik dengan data masukan dari PC. Alat ini dapat mengendalikan dan menampilkan LED sesuai data yang diberikan. LED matrik dapat menampilkan empat baris teks yang masing-masing baris maksimal enam karakter, gambar serta animasi running text (teks berjalan)

SN 2019ehk: A Double-peaked Ca-rich Transient with Luminous X-Ray Emission and Shock-ionized Spectral Features

We present panchromatic observations and modeling of the Calcium-rich supernova (SN) 2019ehk in the star-forming galaxy M100 (d ≈ 16.2 Mpc) starting 10 hr after explosion and continuing for ~300 days. SN 2019ehk shows a double-peaked optical light curve peaking at t = 3 and 15 days. The first peak is coincident with luminous, rapidly decaying Swift-XRT–discovered X-ray emission (L_x ≈ 10⁴¹ erg s⁻¹ at 3 days; L_x ∝ t⁻³), and a Shane/Kast spectral detection of narrow Hα and He II emission lines (v ≈ 500 km s⁻¹) originating from pre-existent circumstellar material (CSM). We attribute this phenomenology to radiation from shock interaction with extended, dense material surrounding the progenitor star at r (0.1–1) × 10¹⁷ cm. The photometric and spectroscopic properties during the second light-curve peak are consistent with those of Ca-rich transients (rise-time of t_r = 13.4 ± 0.210 days and a peak B-band magnitude of M_B = −15.1 ± 0.200 mag). We find that SN 2019ehk synthesized (3.1 ± 0.11) × 10⁻² M_⊙ of ⁵⁶Ni and ejected M_(ej) = (0.72 ± 0.040) M⊙ total with a kinetic energy E_k = (1.8 ± 0.10) × 10⁵⁰ erg. Finally, deep HST pre-explosion imaging at the SN site constrains the parameter space of viable stellar progenitors to massive stars in the lowest mass bin (~10 M_⊙) in binaries that lost most of their He envelope or white dwarfs (WDs). The explosion and environment properties of SN 2019ehk further restrict the potential WD progenitor systems to low-mass hybrid HeCO WD+CO WD binaries

Diversity analysis of cotton (Gossypium hirsutum L.) germplasm using the CottonSNP63K Array

Cotton germplasm resources contain beneficial alleles that can be exploited to develop germplasm adapted to emerging environmental and climate conditions. Accessions and lines have traditionally been characterized based on phenotypes, but phenotypic profiles are limited by the cost, time, and space required to make visual observations and measurements. With advances in molecular genetic methods, genotypic profiles are increasingly able to identify differences among accessions due to the larger number of genetic markers that can be measured. A combination of both methods would greatly enhance our ability to characterize germplasm resources. Recent efforts have culminated in the identification of sufficient SNP markers to establish high-throughput genotyping systems, such as the CottonSNP63K array, which enables a researcher to efficiently analyze large numbers of SNP markers and obtain highly repeatable results. In the current investigation, we have utilized the SNP array for analyzing genetic diversity primarily among cotton cultivars, making comparisons to SSR-based phylogenetic analyses, and identifying loci associated with seed nutritional traits. (Résumé d'auteur

Cross-cutting principles for planetary health education

Since the 2015 launch of the Rockefeller Foundation Lancet Commission on planetary health,1 an enormous groundswell of interest in planetary health education has emerged across many disciplines, institutions, and geographical regions. Advancing these global efforts in planetary health education will equip the next generation of scholars to address crucial questions in this emerging field and support the development of a community of practice. To provide a foundation for the growing interest and efforts in this field, the Planetary Health Alliance has facilitated the first attempt to create a set of principles for planetary health education that intersect education at all levels, across all scales, and in all regions of the world—ie, a set of cross-cutting principles