scholarly journals Albedo Impacts of Changing Agricultural Practices in the United States through Space-Borne Analysis

2020 ◽  
Vol 12 (18) ◽  
pp. 2887
Author(s):  
Jon Starr ◽  
Jianglong Zhang ◽  
Jeffrey S. Reid ◽  
David C. Roberts
Keyword(s):  
United States ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Practices ◽  
The United States ◽  
Growing Period ◽  
Field Crops ◽  
Sensing Applications ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Individual

Using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Bidirectional Reflectance Distribution Function (BRDF) with the U.S. Department of Agriculture’s National Agricultural Statistics Service’s Cropland Data Layer (CDL), the daily albedo of homogenous agricultural fields was derived for 51 common United States field crops by wavelength, sky-type, day of year, crop, and hardiness zone from 2015–2018. This study suggests that crop growth can result in changes in reflectivity up to a factor of 2 at most wavelengths and is unique per crop type in timing and range. Additionally, broadband impacts were studied and found to be less conspicuous than the individual wavelengths, but still significant. The results were used to evaluate a common method of cropland albedo estimation, normalized difference vegetation index (NDVI) as a proxy for albedo, and this method was found to have some significant limitations dependent on wavelength and date. Finally, a database of surface albedo variations as a function of growing period is constructed for common field crops to the United States (as well as additional land-cover types). This database can be used to aid both satellite remote-sensing applications and long-term weather modeling efforts by providing a method for parameter adjustments based on crop driven albedo changes, including changes in cropland composition related to commodity markets and other external factors.

scholarly journals Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models

2018 ◽  
Vol 10 (10) ◽  
pp. 1601 ◽  
Author(s):  
Carl Talsma ◽  
Stephen Good ◽  
Diego Miralles ◽  
Joshua Fisher ◽  
Brecht Martens ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Scales ◽  
Laboratory Model ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Input Variables ◽  
Added Uncertainty ◽  
Model Components ◽  
The Individual

Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.

scholarly journals Nature inequity and higher COVID-19 case rates in less-green neighbourhoods in the United States

2021 ◽  
Author(s):  
Erica N. Spotswood ◽  
Matthew Benjamin ◽  
Lauren Stoneburner ◽  
Megan M. Wheeler ◽  
Erin E. Beller ◽  
...  
Keyword(s):  
United States ◽  
Low Income ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The United States ◽  
Incidence Rates ◽  
Lower Income ◽  
Urban Nature ◽  
Urbanized Areas ◽  
Block Groups

AbstractUrban nature—such as greenness and parks—can alleviate distress and provide space for safe recreation during the COVID-19 pandemic. However, nature is often less available in low-income populations and communities of colour—the same communities hardest hit by COVID-19. In analyses of two datasets, we quantified inequity in greenness and park proximity across all urbanized areas in the United States and linked greenness and park access to COVID-19 case rates for ZIP codes in 17 states. Areas with majority persons of colour had both higher case rates and less greenness. Furthermore, when controlling for sociodemographic variables, an increase of 0.1 in the Normalized Difference Vegetation Index was associated with a 4.1% decrease in COVID-19 incidence rates (95% confidence interval: 0.9–6.8%). Across the United States, block groups with lower income and majority persons of colour are less green and have fewer parks. Our results demonstrate that the communities most impacted by COVID-19 also have the least nature nearby. Given that urban nature is associated with both human health and biodiversity, these results have far-reaching implications both during and beyond the pandemic.

scholarly journals Evaluating the Salinity Tolerance of Clonal-type Bermudagrass Cultivars and an Experimental Selection

HortScience ◽  
2017 ◽  
Vol 52 (1) ◽  
pp. 185-191 ◽  
Author(s):  
Mingying Xiang ◽  
Justin Q. Moss ◽  
Dennis L. Martin ◽  
Kemin Su ◽  
Bruce L. Dunn ◽  
...  
Keyword(s):  
United States ◽  
Salinity Tolerance ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Perennial Grass ◽  
Warm Season ◽  
Dry Weight ◽  
The United States ◽  
Clonal Type ◽  
Salinity Levels

Bermudagrass (Cynodon sp.) is a highly productive, warm-season, perennial grass that has been grown in the United States for turfgrass, forage, pasture, rangeland, and roadside use. At the same time, many bermudagrass production and reclamation sites across the United States are affected by soil salinity issues. Therefore, identifying bermudagrass with improved salinity tolerance is important for successfully producing bermudagrass and for reclaiming salt-affected sites with saline irrigated water. In this project, the relative salinity tolerance of seven clonal-type bermudagrass was determined, including industry standards and an Oklahoma State University (OSU) experimental line. The experiment was conducted under a controlled environment with six replications of each treatment. Seven bermudagrass entries were exposed to four salinity levels (1.5, 15, 30, and 45 dS·m−1) consecutively via subirrigation systems. The relative salinity tolerance among entries was determined by normalized difference vegetation index (NDVI), digital image analysis (DIA), leaf firing (LF), turf quality (TQ), shoot dry weight (SW), visual rating (VR), and dark green color index (DGCI). Results indicated that there were variable responses to salinity stress among the entries studied. As salinity levels of the irrigation water increased, all evaluation criterion decreased, except LF. All entries had acceptable TQ when exposed to 15 dS·m−1. When exposed to 30 dS·m−1, experimental entry OKC1302 had less LF than all other entries except ‘Tifway’, while ‘Midlawn’ showed more LF than all the entries. Leaf firing ranged from 1.0 to 2.7 at 45 dS·m−1, where ‘Tifway’ outperformed all other entries. At 45 dS·m−1, the live green cover as measured using DIA ranged from 3.07% to 24.72%. The parameters LF, TQ, NDVI, DGCI, SW, and DIA were all highly correlated with one another, indicating their usefulness as relative salinity tolerance measurements.

scholarly journals A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks

2018 ◽  
Vol 15 (19) ◽  
pp. 5779-5800 ◽  
Author(s):  
Yao Zhang ◽  
Joanna Joiner ◽  
Seyed Hamed Alemohammad ◽  
Sha Zhou ◽  
Pierre Gentine
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Gross Primary Production ◽  
Terrestrial Ecosystems ◽  
Temporal Variations ◽  
Clear Sky ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Regression Slopes ◽  
The Individual ◽  
Sky Conditions

Abstract. Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05∘ 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

Estimating the Urban Bias of Surface Shelter Temperatures Using Upper-Air and Satellite Data. Part II: Estimation of the Urban Bias

1995 ◽  
Vol 34 (2) ◽  
pp. 358-370 ◽  
Author(s):  
David L. Epperson ◽  
Jerry M. Davis ◽  
Peter Bloomfield ◽  
Thomas R. Karl ◽  
Alan L. McNab ◽  
...  
Keyword(s):  
United States ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The United States ◽  
Rural Landscape ◽  
Temperature Record ◽  
Urban Bias ◽  
The Mean ◽  
The Given

Abstract A methodology is presented for estimating the urban bias of surface shelter temperatures due to the effect of the urban heat island. Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986–89 using upper-air data from the European Centre for Medium-Range Weather Forecasts to represent the background climate, site-specific data to represent the local landscape, and satellite-derived data—the normalized difference vegetation index (NDVI) and the Defense Meteorological Satellite Program (DMSP) nighttime brightness data—to represent the urban and rural landscape. Local NDVI and DMSP values were calculated for each station using the mean NDVI and DMSP values from a 3 km × 3 km area centered over the given station. Regional NDVI and DMSP values were calculated to represent a typical rural value for each station using the mean NDVI and DMSP values from a 1° × 1° latitude–longitude area in which the given station was located. Models for the United States were then developed for monthly maximum, mean, and minimum temperatures using data from over 1000 stations in the U.S. Cooperative Network and for monthly mean temperatures with data from over 1150 stations in the Global Historical Climate Network. Local biases, or the differences between the model predictions using the observed NDVI and DMSP values, and the predictions using the background regional values were calculated and compared with the results of other research. The local or urban bias of U.S. temperatures, as derived from all U.S. stations (urban and rural) used in the models, averaged near 0.40°C for monthly minimum temperatures, near 0.25°C for monthly mean temperatures, and near 0.10°C for monthly maximum temperatures. The biases of monthly minimum temperatures for individual stations ranged from near −1.1°C for rural stations to 2.4°C for stations from the largest urban areas. There are some regions of the United States where a regional NDVI value based on a 1° × 1° latitude–longitude area will not represent a typical “rural” NDVI value for the given region, Thus, for some regions of the United States, the urban bias of this study may underestimate the actual current urban bias. The results of this study indicate minimal problems for global application once global NDVI and DMSP data become available. It is anticipated that results from global application will provide insights into the urban bias of the global temperature record.

‘A Routh O’ Auld Nick-Nackets’ – the antiquarian collection of John Rae

2015 ◽  
Vol 36-37 (1) ◽  
pp. 163-183
Author(s):  
Paul Taylor
Keyword(s):  
United States ◽  
Nineteenth Century ◽  
Social Context ◽  
Moral Education ◽  
The United States ◽  
Representative Case ◽  
National Museums ◽  
The Individual ◽  
The University

John Rae, a Scottish antiquarian collector and spirit merchant, played a highly prominent role in the local natural history societies and exhibitions of nineteenth-century Aberdeen. While he modestly described his collection of archaeological lithics and other artefacts, principally drawn from Aberdeenshire but including some items from as far afield as the United States, as a mere ‘routh o’ auld nick-nackets' (abundance of old knick-knacks), a contemporary singled it out as ‘the best known in private hands' (Daily Free Press 4/5/91). After Rae's death, Glasgow Museums, National Museums Scotland, the University of Aberdeen Museum and the Pitt Rivers Museum in Oxford, as well as numerous individual private collectors, purchased items from the collection. Making use of historical and archive materials to explore the individual biography of Rae and his collection, this article examines how Rae's collecting and other antiquarian activities represent and mirror wider developments in both the ‘amateur’ antiquarianism carried out by Rae and his fellow collectors for reasons of self-improvement and moral education, and the ‘professional’ antiquarianism of the museums which purchased his artefacts. Considered in its wider nineteenth-century context, this is a representative case study of the early development of archaeology in the wider intellectual, scientific and social context of the era.

scholarly journals A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

Technologies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 40
Author(s):  
Guang Yang ◽  
Yuntao Ma ◽  
Jiaqi Hu
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Extraction Methods ◽  
Average Error ◽  
Geographic Coordinate System ◽  
Optimal Thresholds ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Spatial Locations

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

Medicaid Not Required to Fund Medically Necessary Abortions

1980 ◽  
Vol 1 (8) ◽  
pp. 3-6
Author(s):  
George J. Annas
Keyword(s):  
United States ◽  
Law Enforcement ◽  
United States Supreme Court ◽  
The United States ◽  
United States Constitution ◽  
Medical Procedures ◽  
Poor Women ◽  
Law Enforcement Agency ◽  
The Individual ◽  
Joint Resolution

In an extraordinary and highly controversial 5-4 decision, the United States Supreme Court decided on June 30, 1980, that the United States Constitution does not require either the federal government or the individual states to fund medically necessary abortions for poor women who qualify for Medicaid.At issue in this case is the constitutionality of the Hyde Amendment. The applicable 1980 version provides:|N]one of the funds provided by this joint resolution shall be used to perform abortions except where the life of the mother would be endangered if the fetus were carried to term; or except for such medical procedures necessary for the victims of rape or incest when such rape or incest has been reported promptly to a law enforcement agency or public health service, (emphasis supplied)

scholarly journals US Rural Surgeons Leading During COVID-19: Where Are They Now?

2020 ◽  
pp. 000313482096005
Author(s):  
Michael Sarap ◽  
Julie Conyers ◽  
Crystal Cunningham ◽  
Adam Deutchman ◽  
Glenn Levine ◽  
...  
Keyword(s):  
United States ◽  
The United States ◽  
Social Lives ◽  
The Individual

Rural surgeons from disparate areas of the United States report on the effects of the COVID-19 pandemic in their communities as the virus has spread across the country. The pandemic has brought significant changes to the professional, economic, and social lives of the individual surgeons and their communities.

The great Chinese surprise: the rupture with the United States is real and is happening

2020 ◽  
Vol 96 (2) ◽  
pp. 419-437
Author(s):  
Xiangfeng Yang
Keyword(s):  
United States ◽  
The United States ◽  
Donald Trump ◽  
Ample Evidence ◽  
Pendulum Swing ◽  
The Us ◽  
Methodological Problems ◽  
Trade War ◽  
The Stability ◽  
The Individual

Abstract Ample evidence exists that China was caught off guard by the Trump administration's onslaught of punishing acts—the trade war being a prime, but far from the only, example. This article, in addition to contextualizing their earlier optimism about the relations with the United States under President Trump, examines why Chinese leaders and analysts were surprised by the turn of events. It argues that three main factors contributed to the lapse of judgment. First, Chinese officials and analysts grossly misunderstood Donald Trump the individual. By overemphasizing his pragmatism while downplaying his unpredictability, they ended up underprepared for the policies he unleashed. Second, some ingrained Chinese beliefs, manifested in the analogies of the pendulum swing and the ‘bickering couple’, as well as the narrative of the ‘ballast’, lulled officials and scholars into undue optimism about the stability of the broader relationship. Third, analytical and methodological problems as well as political considerations prevented them from fully grasping the strategic shift against China in the US.

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