Using NASA Techniques to Atmospherically Correct AWiFS Data for Carbon Sequestration Studies

Carbon dioxide is a greenhouse gas emitted in a number of ways, including the burning of fossil fuels and the conversion of forest to agriculture. Research has begun to quantify the ability of vegetative land cover and oceans to absorb and store carbon dioxide. The USDA (U.S. Department of Agriculture) Forest Service is currently evaluating a DSS (decision support system) developed by researchers at the NASA Ames Research Center called CASA-CQUEST (Carnegie-Ames-Stanford Approach-Carbon Query and Evaluation Support Tools). CASA-CQUEST is capable of estimating levels of carbon sequestration based on different land cover types and of predicting the effects of land use change on atmospheric carbon amounts to assist land use management decisions. The CASA-CQUEST DSS currently uses land cover data acquired from MODIS (the Moderate Resolution Imaging Spectroradiometer), and the CASA-CQUEST project team is involved in several projects that use moderate-resolution land cover data derived from Landsat surface reflectance. Landsat offers higher spatial resolution than MODIS, allowing for increased ability to detect land use changes and forest disturbance. However, because of the rate at which changes occur and the fact that disturbances can be hidden by regrowth, updated land cover classifications may be required before the launch of the Landsat Data Continuity Mission, and consistent classifications will be needed after that time. This candidate solution investigates the potential of using NASA atmospheric correction techniques to produce science-quality surface reflectance data from the Indian Remote Sensing Advanced Wide-Field Sensor on the RESOURCESAT-1 mission to produce land cover classification maps for the CASA-CQUEST DSS

Long-Term Time Series of Remote Sensing Observations for Development of Regulatory Water Quality Standards

Water quality standards in the U.S. consist of: designated uses (the services that a water body provides; e.g., drinking water, aquatic life, harvestable species, recreation) . criteria that define the environmental conditions that must be maintained to support the uses For estuaries and coastal waters in the Gulf of Mexico, there are no numeric (quantitative) criteria to protect designated uses from effects of nutrients. This is largely due to the absence of adequate data that would quantitatively link biological conditions to nutrient concentrations. The Gulf of Mexico Alliance, an organization fostering collaboration between the Gulf States and U.S. Federal agencies, has identified the development of the numeric nutrient criteria as a major step leading to reduction in MODIS Products Figure 6. Map of the Mobile Bay with a yellow patch indicating the Bon Secour Bay area selected in this study for averaging water clarity parameters retrieved from MODIS datasets. nutrient inputs to coastal ecosystems. Nutrient enrichment in estuaries and coastal waters can be quantified based on response variables that measure phytoplankton biomass and water clarity. Long-term, spatially and temporally resolved measurements of chlorophyll a concentration, total concentration of suspended solids, and water clarity are needed to establish reference conditions and to quantify stressor-response relationships

Use of Landsat Data to Track Historical Water Quality Changes in Florida Keys Marine Environments

Satellite remote sensing has shown the advantage of water quality assessment at synoptic scales in coastal regions, yet modern sensors such as SeaWiFS or MODIS did not start until the late 1990s. For non-interrupted observations, only the Landsat series have the potential to detect major water quality events since the 1980s. However, such ability is hindered by the unknown data quality or consistency through time. Here, using the Florida Keys as a case study, we demonstrate an approach to identify historical water quality events through improved atmospheric correction of Landsat data and cross-validation with concurrent MODIS data. After aggregation of the Landsat-5 Thematic Mapper (TM) 30-m pixels to 240-m pixels (to increase the signal-to-noise ratio), a MODIS-like atmospheric correction approach using the Landsat shortwave-infrared (SWIR) bands was developed and applied to the entire Landsat-5 TM data series between 1985 and 2010. Remote sensing reflectance (RRS) anomalies from Landsat (2 standard deviations from a pixel-specific monthly climatology) were found to detect MODIS RRS anomalies with over 90% accuracy for all three bands for the same period of 2002–2010. Extending this analysis for the entire Landsat-5 time-series revealed RRS anomaly events in the 1980s and 1990s, some of which are corroborated by known ecosystem changes due in part to changes in local freshwater flow. Indeed, TM RRS anomalies were shown to be useful in detecting shifts in seagrass density, turbidity increases, black water events, and phytoplankton blooms. These findings have large implications for ongoing and future water quality assessment in the Florida Keys as well as in many other coastal regions

Intraretinal hyper-reflective foci are almost universally present and co-localize with intraretinal fluid in diabetic macular edema

Purpose: In diabetic macular edema (DME), hyper-reflective foci (HRF) has been linked to disease severity and progression. Using an automated approach, we aimed to investigate the baseline distribution of HRF in DME and their co-localization with cystoid intraretinal fluid (IRF).Methods: Baseline spectral-domain optical coherence tomography (SD-OCT) volume scans (N = 1527) from phase III clinical trials YOSEMITE (NCT03622580) and RHINE (NCT03622593) were segmented using a deep-learning–based algorithm (developed using B-scans from BOULEVARD NCT02699450) to detect HRF. The HRF count and volume were assessed. HRF distributions were analyzed in relation to best-corrected visual acuity (BCVA), central subfield thickness (CST), and IRF volume in quartiles, and Diabetic Retinopathy Severity Scores (DRSS) in groups. Co-localization of HRF with IRF was calculated in the central 3-mm diameter using the en face projection.Results: HRF were present in most patients (up to 99.7%). Median (interquartile range [IQR]) HRF volume within the 3-mm diameter Early Treatment Diabetic Retinopathy Study ring was 1964.3 (3325.2) pL, and median count was 64.0 (IQR = 96.0). Median HRF volumes were greater with decreasing BCVA (nominal P = 0.0109), and increasing CST (nominal P < 0.0001), IRF (nominal P < 0.0001), and DRSS up to very severe nonproliferative diabetic retinopathy (nominal P < 0.0001). HRF co-localized with IRF in the en face projection.Conclusions: Using automated HRF segmentation of full SD-OCT volumes, we observed that HRF are a ubiquitous feature in DME and exhibit relationships with BCVA, CST, IRF, and DRSS, supporting a potential link to disease severity. The spatial distribution of HRF closely followed that of IRF

How does cognition evolve? Phylogenetic comparative psychology

Now more than ever animal studies have the potential to test hypotheses regarding how cognition evolves. Comparative psychologists have developed new techniques to probe the cognitive mechanisms underlying animal behavior, and they have become increasingly skillful at adapting methodologies to test multiple species. Meanwhile, evolutionary biologists have generated quantitative approaches to investigate the phylogenetic distribution and function of phenotypic traits, including cognition. In particular, phylogenetic methods can quantitatively (1) test whether specific cognitive abilities are correlated with life history (e.g., lifespan), morphology (e.g., brain size), or socio-ecological variables (e.g., social system), (2) measure how strongly phylogenetic relatedness predicts the distribution of cognitive skills across species, and (3) estimate the ancestral state of a given cognitive trait using measures of cognitive performance from extant species. Phylogenetic methods can also be used to guide the selection of species comparisons that offer the strongest tests of a priori predictions of cognitive evolutionary hypotheses (i.e., phylogenetic targeting). Here, we explain how an integration of comparative psychology and evolutionary biology will answer a host of questions regarding the phylogenetic distribution and history of cognitive traits, as well as the evolutionary processes that drove their evolution