Storm intensity and old-growth forest disturbances in the Amazon region

We analyzed the pattern of large forest disturbances or blow-downs apparently caused by severe storms in a mostly unmanaged portion of the Brazilian Amazon using 27 Landsat images and daily precipitation estimates from NOAA satellite data. For each Landsat a spectral mixture analysis (SMA) was applied. Based on SMA, we detected and mapped 279 patches (from 5 ha to 2,223 ha) characteristic of blow-downs. A total of 21,931 ha of forest were disturbed. We found a strong correlation between occurrence of blow-downs and frequency of heavy rainfall (Spearman\u27s rank, r2 = 0.84, p \u3c 0.0003). The recurrence intervals of large disturbances were estimated to be 90,000 yr for the eastern Amazon and 27,000 yr for the western Amazon. This suggests that weather patterns affect the frequency of large forest disturbances that may produce different rates of forest turnover in the eastern and western Amazon basin

Evaluating multiple causes of persistent low microwave backscatter from Amazon forests after the 2005 drought

Amazonia has experienced large-scale regional droughts that affect forest productivity and biomass stocks. Space-borne remote sensing provides basin-wide data on impacts of meteorological anomalies, an important complement to relatively limited ground observations across the Amazon’s vast and remote humid tropical forests. Morning overpass QuikScat Ku-band microwave backscatter from the forest canopy was anomalously low during the 2005 drought, relative to the full instrument record of 1999–2009, and low morning backscatter persisted for 2006–2009, after which the instrument failed. The persistent low backscatter has been suggested to be indicative of increased forest vulnerability to future drought. To better ascribe the cause of the low post-drought backscatter, we analyzed multiyear, gridded remote sensing data sets of precipitation, land surface temperature, forest cover and forest cover loss, and microwave backscatter over the 2005 drought region in the southwestern Amazon Basin (4°-12°S, 66°-76°W) and in adjacent 8°x10° regions to the north and east. We found moderate to weak correlations with the spatial distribution of persistent low backscatter for variables related to three groups of forest impacts: the 2005 drought itself, loss of forest cover, and warmer and drier dry seasons in the post-drought vs. the pre-drought years. However, these variables explained only about one quarter of the variability in depressed backscatter across the southwestern drought region. Our findings indicate that drought impact is a complex phenomenon and that better understanding can only come from more extensive ground data and/or analysis of frequent, spatially-comprehensive, high-resolution data or imagery before and after droughts

A New Generation of Corporate Codes of Ethics

Michael K. Braswell is an associate professor in the Department of Finance, Insurance, Real Estate & Law, College of Business, University of North Texas, Denton, Texas 76203. Charles M. Foster is an associate professor in the Department of Finance, Insurance, Real Estate & Law, College of Business, University of North Texas, Denton, Texas 76203. Stephen L. Poe is a professor in the Department of Finance, Insurance, Real Estate & Law, College of Business, University of North Texas, Denton, Texas 76203

GOES-R Algorithms: A Common Science and Engineering Design and Development Approach for Delivering Next Generation Environmental Data Products

GOES-R, the next generation of the National Oceanic and Atmospheric Administration’s (NOAA) Geostationary Operational Environmental Satellite (GOES) System, represents a new technological era in operational geostationary environmental satellite systems. GOES-R will provide advanced products that describe the state of the atmosphere, land, oceans, and solar/ space environments over the western hemisphere. The Harris GOES-R Ground Segment team will provide the software, based on government-supplied algorithms, and engineering infrastructures designed to produce and distribute these next-generation data products. The Harris GOES-R Team has adopted an integrated applied science and engineering approach that combines rigorous system engineering methods, with modern software design elements to facilitate the transition of algorithms for Level 1 and 2+ products to operational software. The Harris Team GOES-R GS algorithm framework, which includes a common data model interface, provides general design principles and standardized methods for developing general algorithm services, interfacing to external data, generating intermediate and L1b and L2 products and implementing common algorithm features such as metadata generation and error handling. This work presents the suite of GOES-R products, their properties and the process by which the related requirements are maintained during the complete design/development life-cycle. It also describes the algorithm architecture/engineering approach that will be used to deploy these algorithms, and provides a preliminary implementation road map for the development of the GOES-R GS software infrastructure, and a view into the integration of the framework and data model into the final design

Ancient Amazonian populations left lasting impacts on forest structure

Amazonia contains a vast expanse of contiguous tropical forest and is influential in global carbon and hydrological cycles. Whether ancient Amazonia was highly disturbed or modestly impacted, and how ancient disturbances have shaped current forest ecosystem processes, is still under debate. Amazonian Dark Earths (ADEs), which are anthropic soil types with enriched nutrient levels, are one of the primary lines of evidence for ancient human presence and landscape modifications in settings that mostly lack stone structures and which are today covered by vegetation. We assessed the potential of using moderate spatial resolution optical satellite imagery to predict ADEs across the Amazon Basin. Maximum entropy modeling was used to develop a predictive model using locations of ADEs across the basin and satellite‐derived remotely sensed indices. Amazonian Dark Earth sites were predicted to be primarily along the main rivers and in eastern Amazonia. Amazonian Dark Earth sites, when compared with randomly selected forested sites located within 50 km of ADE sites, were less green canopies (lower normalized difference vegetation index) and had lower canopy water content. This difference was accentuated in two drought years, 2005 and 2010. This is contrary to our expectation that ADE sites would have nutrient‐rich soils that support trees with greener canopies and forests on ADE soils being more resilient to drought. Biomass and tree height were lower on ADE sites in comparison with randomly selected adjacent sites. Our results suggested that ADE‐related ancient human impact on the forest is measurable across the entirety of the 6 million km2 of Amazon Basin using remotely sensed data

Testing a Spanish-language colorectal cancer screening decision aid in Latinos with limited English proficiency: Results from a pre-post trial and four month follow-up survey

Abstract Background Compared with non-Latinos, Latinos in the US have low rates of colorectal cancer (CRC) screening and low rates of knowledge regarding CRC screening tests and guidelines. Spanish speaking Latinos have particularly low CRC screening rates and screening knowledge. Our purpose was twofold: (1) to evaluate the effect of a computer-based, Spanish-language CRC screening decision aid on screening knowledge, intent to obtain screening, and screening self-efficacy in a community sample of Latinos with limited English proficiency (LEP); and (2) to survey these decision aid viewers at four months to determine their rates of CRC discussions with a health care provider as well as their rates of screening test completion. Methods We recruited 50-75 year old Latinos with LEP who were not current with CRC. Participants screening viewed a 14 minute multimedia decision aid that addresses CRC screening rationale, recommendations, and options. We conducted an uncontrolled (pre-post) study in which we assessed screening knowledge, self-efficacy, and intent at baseline and immediately after decision aid viewing. We also conducted a follow-up telephone survey of participants at four months to examine rates of patient-provider screening discussions and test completion. Results Among n = 80 participants, knowledge scores increased from 20% (before) to 72% (after) decision aid viewing (absolute difference [95%CI]: 52% [46, 59]). The proportion with high screening self-efficacy increased from 67% to 92% (25% [13, 37]); the proportion with high screening intent increased from 63% to 95% (32% [21, 44]). We reached 68 (85%) of 80 participants eligible for the follow-up survey. Of these 36 (53%) reported discussing screening with a provider and 13 (19%) completed a test. Conclusion Viewing a Spanish-language decision aid increased CRC screening knowledge, self-efficacy, and intent among Latinos with LEP. Decision aid viewing appeared to promote both CRC screening discussions with health care providers and test completion. The decision aid may be an effective tool for promoting CRC screening and reducing screening disparities in this population

Ecological research in the Large Scale Biosphere Atmosphere Experiment in Amazonia: A discussion of early results

The Large-scale Biosphere–Atmosphere Experiment in Amazonia (LBA) is a multinational, interdisciplinary research program led by Brazil. Ecological studies in LBA focus on how tropical forest conversion, regrowth, and selective logging influence carbon storage, nutrient dynamics, trace gas fluxes, and the prospect for sustainable land use in the Amazon region. Early results from ecological studies within LBA emphasize the variability within the vast Amazon region and the profound effects that land-use and land-cover changes are having on that landscape. The predominant land cover of the Amazon region is evergreen forest; nonetheless, LBA studies have observed strong seasonal patterns in gross primary production, ecosystem respiration, and net ecosystem exchange, as well as phenology and tree growth. The seasonal patterns vary spatially and interannually and evidence suggests that these patterns are driven not only by variations in weather but also by innate biological rhythms of the forest species. Rapid rates of deforestation have marked the forests of the Amazon region over the past three decades. Evidence from ground-based surveys and remote sensing show that substantial areas of forest are being degraded by logging activities and through the collapse of forest edges. Because forest edges and logged forests are susceptible to fire, positive feedback cycles of forest degradation may be initiated by land-use-change events. LBA studies indicate that cleared lands in the Amazon, once released from cultivation or pasture usage, regenerate biomass rapidly. However, the pace of biomass accumulation is dependent upon past land use and the depletion of nutrients by unsustainable land-management practices. The challenge for ongoing research within LBA is to integrate the recognition of diverse patterns and processes into general models for prediction of regional ecosystem function