Michigan Technology Research Institute

Ann Arbor, MI, United States

Michigan Technology Research Institute

Ann Arbor, MI, United States
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Brown M.E.,University of Maryland University College | McCarty J.L.,Michigan Technology Research Institute
Applied Geography | Year: 2017

As the world becomes increasingly urbanized, the need for fresh fruits and vegetables in urban areas grows while the difficulty of bringing these perishable products to these areas also increases. Small-scale agriculture located in urban areas is a highly effective and profitable way to provide these products to communities that are far from extensive commercial agricultural areas. Here we describe how remote sensing can be used with data mining approaches to monitor urban and peri-urban farms within cities in both developed and developing countries. Using very high resolution satellite imagery together with moderate and coarse resolution imagery and information from social media and the web, we analyze the usefulness of different methods to identify farms within urban boundaries in four countries. The analysis shows how a mixed-method approach is necessary in order to identify where urban farming is occurring and to monitor its change through time. Although remote sensing-based vegetation and water indices were useful, without ancillary data they are not effective at remotely mapping the locations of urban farms. However, remote sensing is a good way to monitor vegetation condition in locations where actively managed urban farms are known to exist. © 2017 Elsevier Ltd

Burns J.W.,Michigan Technology Research Institute | Chervin R.D.,University of Michigan
Neurology | Year: 2014

Objective: To test the hypothesis that neonatal sleep physiology reflects cerebral dysfunction, we compared neurologic examination scores to the proportions of recorded sleep/wake states, sleep depth, and sleep fragmentation in critically ill neonates. Methods: Newborn infants (≥35 weeks gestation) who required intensive care and were at risk for seizures were monitored with 8- to 12-hour polysomnograms (PSGs). For each infant, the distribution of sleep-wake states, entropy of the sequence of state transitions, and delta power from the EEG portion of the PSG were quantified. Standardized neurologic examination (Thompson) scores were calculated. Results: Twenty-eight infants participated (mean gestational age 39.0 ± 1.6 weeks). An increased fraction of quiet sleep correlated with worse neurologic examination scores (Spearman rho5 0.54, p= 0.003), but the proportion of active sleep did not (p> 0.1). Higher state entropy corresponded to better examination scores (rho= -0.43, p = 0.023). Decreased delta power during quiet sleep, but not the power at other frequencies, was also associated with worse examination scores (rho = -0.48, p = 0.009). These findings retained significance after adjustment for gestational age or postmenstrual age at the time of the PSG. Sleep stage transition probabilities were also related to examination scores. Conclusions: Among critically ill neonates at risk for CNS dysfunction, several features of recorded sleep-including analyses of sleep stages, depth, and fragmentation-showed associations with neurologic examination scores. Quantitative PSG analyses may add useful objective information to the traditional neurologic assessment of critically ill neonates.

Shellhaas R.A.,University of Michigan | Thelen B.J.,Michigan Technology Research Institute | Bapuraj J.R.,University of Michigan | Burns J.W.,Michigan Technology Research Institute | And 4 more authors.
Neurology | Year: 2013

Objective: We evaluated the utility of amplitude-integrated EEG (aEEG) and regional oxygen saturation (rSO2) measured using near-infrared spectroscopy (NIRS) for short-term outcome prediction in neonates with hypoxic ischemic encephalopathy (HIE) treated with therapeutic hypothermia. Methods: Neonates with HIE were monitored with dual-channel aEEG, bilateral cerebral NIRS, and systemic NIRS throughout cooling and rewarming. The short-term outcome measure was a composite of neurologic examination and brain MRI scores at 7 to 10 days. Multiple regression models were developed to assess NIRS and aEEG recorded during the 6 hours before rewarming and the 6-hour rewarming period as predictors of short-term outcome. Results: Twenty-one infants, mean gestational age 38.8 6 1.6 weeks, median 10-minute Apgar score 4 (range 0-8), and mean initial pH 6.92 6 0.19, were enrolled. Before rewarming, the most parsimonious model included 4 parameters (adjusted R2 5 0.59; p 5 0.006): lower values of systemic rSO2 variability (p 5 0.004), aEEG bandwidth variability (p 5 0.019), and mean aEEG upper margin (p 5 0.006), combined with higher mean aEEG bandwidth (worse discontinuity; p 5 0.013), predicted worse short-term outcome. During rewarming, lower systemic rSO2 variability (p 5 0.007) and depressed aEEG lower margin (p 5 0.034) were associated with worse outcome (model-adjusted R2 5 0.49; p 5 0.005). Cerebral NIRS data did not contribute to either model. Conclusions: During day 3 of cooling and during rewarming, loss of physiologic variability (by systemic NIRS) and invariant, discontinuous aEEG patterns predict poor short-term outcome in neonates with HIE. These parameters, but not cerebral NIRS, may be useful to identify infants suitable for studies of adjuvant neuroprotective therapies or modification of the duration of cooling and/or rewarming. © 2013 American Academy of Neurology.

Potapov P.V.,University of Maryland University College | Turubanova S.A.,University of Maryland University College | Tyukavina A.,University of Maryland University College | Krylov A.M.,University of Maryland University College | And 3 more authors.
Remote Sensing of Environment | Year: 2015

In the former "Eastern Bloc" countries, there have been dramatic changes in forest disturbance and forest recovery rates since the collapse of the Soviet Union, due to the transition to open-market economies, and the recent economic crisis. Unfortunately though, Eastern European countries collected their forest statistics inconsistently, and their boundaries have changed, making it difficult to analyze forest dynamics over time. Our goal here was to consistently quantify forest cover change across Eastern Europe since the 1980s based on the Landsat image archive. We developed an algorithm to simultaneously process data from different Landsat platforms and sensors (TM and ETM. +) to map annual forest cover loss and decadal forest cover gain. We processed 59,539 Landsat images for 527 footprints across Eastern Europe and European Russia. Our results were highly accurate, with gross forest loss producer's and user's accuracy of >. 88% and >. 89%, respectively, and gross forest gain producer's and user's accuracy of >. 75% and >. 91%, based on a sample of probability-based validation points. We found substantial changes in the forest cover of Eastern Europe. Net forest cover increased from 1985 to 2012 by 4.7% across the region, but decreased in Estonia and Latvia. Average annual gross forest cover loss was 0.41% of total forest cover area, with a statistically significant increase from 1985 to 2012. Timber harvesting was the main cause of forest loss, accompanied by some insect defoliation and forest conversion, while only 7.4% of the total forest cover loss was due to large-scale wildfires and windstorms. Overall, the countries of Eastern Europe experienced constant levels or declines in forest loss after the collapse of socialism in the late 1980s, but a pronounced increase in loss in the early 2000s. By the late 2000s, however, the global economic crisis coincided with reduced timber harvesting in most countries, except Poland, Czech Republic, Slovakia, and the Baltic states. Most forest disturbance did not result in a permanent forest loss during our study period. Indeed, forest generally recovered fast and only 12% of the areas of forest loss prior to 1995 had not yet recovered by 2012. Our results allow national and sub-national level analysis and are available on-line (. http://glad.geog.umd.edu/europe/) to serve as a baseline for further analyses of forest dynamics and its drivers. © 2014 Elsevier Inc.

McCarty J.L.,Michigan Technology Research Institute | Ellicott E.A.,University of Maryland University College | Romanenkov V.,Institute for Agrochemistry | Rukhovitch D.,Soil Dokuchaev Institute | Koroleva P.,Soil Dokuchaev Institute
Atmospheric Environment | Year: 2012

Cropland fires are an important source of black carbon (BC) emissions. Previous research has suggested that springtime cropland burning in Eastern Europe, more specifically Russia, is a main contributor of BC in the Arctic atmosphere, acting as a short-lived climate forcer strongly influencing snow-ice albedo and radiation transmission. BC emissions from cropland burning were estimated for the Russian Federation for years 2003 through 2009 using three satellite fire products, the 1 km MODIS Active Fire Product, 0.5° MODIS Fire Radiative Power monthly climate modeling grid product, and the 500 m MODIS Burned Area Product, and a agricultural statistics approach based on a modified method developed and published by the All-Russian Institute of Organic Peat and Fertilizers to estimate farm- and regional-level residue loading from straw surplus left after grain harvesting, while accounting for agricultural management and agrometeorological inputs. The satellite-based emission calculations utilized several different land cover classification schemas for defining croplands in Russia for both the 1 km MODIS Land Cover Product and the 300 m MERIS GlobCover v2.2 data sets. In general, the peaks of BC emissions from cropland burning occurred during the spring (April-May), summer (July-August), and the fall (October). 2008 had the highest annual BC emissions. The range of average annual BC emissions from cropland burning calculated from the different satellite fire products was 2.49 Gg-22.2 Gg, with the agricultural statistics approach annual average equal to 8.90 Gg. The Global Fire Emissions Database (GFED) version 3 reported an annual average of 11.9 Gg of BC from agricultural burning. The results from this analysis showed that the majority of BC emissions originated in European Russia, followed by smaller contributions from West Siberia, Far East Russia, and East Siberia macro-regions. An uncertainty assessment on data used to calculate the BC emissions found moderate uncertainty in some of the input data used in this first attempt to produce spatially and temporally explicit BC emission estimates from cropland burning in the Russian Federation. © 2012 Elsevier Ltd.

Jenkins L.K.,Michigan Technology Research Institute
Special Paper of the Geological Society of America | Year: 2010

From an evolutionary perspective, glacial lakes at the Bering Glacier System are highly immature and are classified as extremely oligotrophic, resulting from their relatively recent formation and the surrounding harsh, northern climate. Unlike temperate or tropical lakes, northern glacial lakes do not contain significant amounts of biological material. Instead, these lakes are dominated by rock flour, suspended sediment originating from glacial rock weathering. This lack of biological influence makes satellite turbidity mapping and prediction more straightforward and potentially more accurate than similar efforts in temperate or tropical environments, where biology typically drives these systems and strongly affects the remotely sensed, electro-optical signal. In-situ turbidity data, collected using an autonomous robot buoy, were used to develop a model-based turbidity algorithm. Multiple linear regression analyses were conducted using different Landsat 7 ETM+ bands to determine the best predictor(s) of turbidity in glacial lakes. The final algorithm utilized Landsat 7 ETM+ band 3 (red portion of the electromagnetic spectrum) and band 4 (near-infrared portion of the electromagnetic spectrum) data to predict turbidity concentrations. Turbidity maps created using the algorithm can be used to help determine inter-and intra-annual sediment dynamics of Vitus Lake. This information could be used to help researchers predict significant glacial events such as outburst floods or surge events. The turbidity maps could also provide insight into the hydrologic routing of the Bering Glacier System by showing where the Glacier is discharging sediment-laden fresh water into Vitus Lake through subsurface conduits. The turbidity algorithm also has broader applicability to other glacial lakes in south-central Alaska and potentially to glacial lakes worldwide. © 2010 The Geological Society of America.

Olson Jr. C.E.,Michigan Technology Research Institute
American Society for Photogrammetry and Remote Sensing Annual Conference, ASPRS 2013 | Year: 2013

Historical data on light reflectance and transmittance from vegetation is abundant and provides insight into some of the errors encountered in Lidar data sets. Broadleaved vegetation is not opaque at the wavelengths of many common Lidar systems. Transmittance through broadleaved foliage, and multiple reflections within tree crowns can result in spurious returns in the Lidar point cloud. These same properties may permit additional uses of Lidar data sets. Copyright © (2013) by the American Society for Photogrammetry & Remote Sensing.

Olson Jr. C.E.,Michigan Technology Research Institute
American Society for Photogrammetry and Remote Sensing Annual Conference, ASPRS 2013 | Year: 2013

Successful development of photogrammetry and aerial mapping depended on accuracy, but the growth of social media and the demand for images has often made speed more important than accuracy. But, what is accuracy? Is it positional accuracy or thematic accuracy? Even the phrase, "Close enough for government work," requires interpretation. Which level of government, and how you plan to use the data, profoundly influence the level of accuracy required. Accuracy is necessarily relative - relative to the use intended for the data. When these data are used for some other purpose than what was intended when the data were acquired, problems may develop. Care in defining what we mean by "accuracy" is important. Until we do that "accuracy" may be a meaningless term. Copyright © (2013) by the American Society for Photogrammetry & Remote Sensing.

Masarik M.P.,Michigan Technology Research Institute | Subotic N.S.,Michigan Technology Research Institute
2016 IEEE Radar Conference, RadarConf 2016 | Year: 2016

This document derives approximate expressions for the Cramèr-Rao lower bounds on the variance of unbiased estimates of the parameters of a narrow-band radar model in the presence of additive white Gaussian noise as well as interference with known structure. We show that the Cramèr-Rao lower bounds with interference are comprised of the bound when the interference is not present and a term that is proportional to the squared normalized-correlation between the radar signal and the interfering signal. Numerical simulations demonstrating these bounds are shown and the threshold effect is observed. The bounds are then used to define an objective function to be used for waveform co-design, and a simple example of this is shown. © 2016 IEEE.

Agency: Department of Defense | Branch: Missile Defense Agency | Program: STTR | Phase: Phase I | Award Amount: 100.00K | Year: 2013

Aegis BMD 5.0 CU will expand and update the Baseline 9 MRBM and IRBM threat set, while Aegis BMD 5.1 will have capabilities against more sophisticated short to intermediate range ballistic missiles. An ability to discriminate this wider set of increasingly sophisticated threats is essential. Technology Service Corporation (TSC) and the Michigan Tech Research Institute (MTRI) propose to identify features that can be correlated and fused between RF and EO/IR sensors to enhance the discrimination function of Aegis BMD against sophisticated decoys, and in the presence of debris. TSC and MTRI will conduct feature selection trade studies, and a novel scheme that adaptively selects feature vectors, while simultaneously correlating and discriminating objects, will be developed. The techniques will be analyzed against notional threats of interest that are representative of Aegis BMD 5.0 CU threats.

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